diff --git "a/3263.jsonl" "b/3263.jsonl"
new file mode 100644--- /dev/null
+++ "b/3263.jsonl"
@@ -0,0 +1,703 @@
+{"seq_id":"38335552544","text":"#!/usr/local/bin/env python\n# -*- coding: utf-8 -*-\n\nimport datetime\n\nl = [\n\n    # USL\n\n    {\n        'name': 'Baton Rouge Capitals',\n        'city': 'Baton Rouge, LA',\n        'founded': 2007,\n        },\n\n    {\n        'name': 'Lafayette Swamp Cats',\n        'city': 'Lafayette, LA',\n        'founded': 2000,\n        'dissolved': 2004,\n        },\n\n    {\n        'name': 'Shreveport/Bossier Lions',\n        'city': 'Shreveport, LA',\n        },\n\n    {\n        'name': 'Baton Rouge Bombers',\n        'city': 'Baton Rouge, LA',\n        'founded': 1997,\n        'dissolved': 1998,\n        },\n]\n","repo_name":"SoccerStatsUS/metadata","sub_path":"data/teams/usa/states/la.py","file_name":"la.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"33224084368","text":"import socket\nimport threading\nfrom tkinter import *\n\n# pour la création de la socket\n# pour la connextion au serveur\nuser = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nuser.connect(('127.0.0.1', 5010))\n\n\n# pour les fonctions\n\ndef recevoir():\n    while True:\n        try:\n            # pour recevoir les messages envoyés par le client\n            message = user.recv(1024).decode('ascii')\n\n            # si le message == \"ps\" ==> le serveur demande le pseudo du client\n            if message == 'ps':\n                # le client envoie son pseudo au serveur\n                user.send(userup.cget(\"text\").encode('ascii'))\n            # sinon\n            else:\n                # le client enregistre les messages envoyés par le serveur\n                listmessage.insert(END, message)\n        except:\n            # pour fermer la socket\n            user.close()\n            break\n\n\ndef ecrire():\n    # pour archiver le pseudo et le message\n    message = '{}: {}'.format(userup.cget(\"text\"), entry.get())\n    print(message)\n    # pour encoder le message et l'envoyer à la socket \"user\"\n    user.send(message.encode('ascii'))\n    # pour supprimer la valeur de entry\n    entry.delete(0, END)\n\n\ndef get_user():\n    username = entry.get()\n    if(username != ''):\n        # pour changer les champs\n        userup.config(text=username)\n        btn.config(text='Envoyer', command=ecrire)\n        label.config(text='Saisir votre Message :')\n        entry.config(width=50)\n        # pour supprimer la valeur de entry\n        entry.delete(0, END)\n        # pour lancer la fonction recevoir()\n        receive_thread = threading.Thread(target=recevoir)\n        receive_thread.start()\n\n\nfenetre = Tk()\n\n# des info sur la fenetre\nfenetre.title(\"CHAT\")\nfenetre.geometry('1000x700')\nfenetre.resizable(False, False)\ntitle = Label(fenetre, text='My CHAT', fg='black', bg='#f5f0e1')\ntitle.config(font=('times', 20, 'bold'))\ntitle.pack(fill=X)\n\n# label \"userup\" pour écrire le nom\nuserup = Label(fenetre, text=' ', fg='black')\nuserup.config(font=('times', 20, 'bold'))\nuserup.place(x=10, y=0)\n\n# frame \"userframe\" pour le saisie et les bouttons\nuserframe = Frame(fenetre)\nuserframe.pack(pady=10)\n\n# label \"lablframe\" dans la frame \"userframe\" pour la saisie et les bouttons\nlablframe = Label(userframe, text='', fg='white',\n                  bg='#e1f0f5', width=1000, height=8)\nlablframe.pack()\n\n# label \"label\" dans \"userframe\" pour saisir le pseudo\nlabel = Label(userframe, text='Saisir votre pseudo :',\n              fg='black', bg=\"#e1f0f5\")\nlabel.config(font=('times', 14, 'bold'))\nlabel.place(x=20, y=40)\n\n# entry \"entry\" dans \"userframe\" pour entrer ...\nentry = Entry(userframe, width=30, fg='#008B8B', bg='white')\nentry.config(font=('times', 14, 'bold'))\nentry.place(x=250, y=40)\n\n# button \"btn\"\nbtn = Button(userframe, text='confirmer',\n             command=get_user, bg='#1e3d59', fg='white')\nbtn.config(font=('times', 14, 'bold'))\nbtn.place(x=800, y=35)\n\n# une autre frame \"messageframe\"\nmessageframe = Frame(fenetre)\nmessageframe.pack()\n\n# scrollbar\nscroll = Scrollbar(messageframe, orient=VERTICAL)\n\n# listbox \"listmessage\" pour afficher les messages\nlistmessage = Listbox(messageframe, width=1000, height=30,\n                      bg='#F0FFFF', yscrollcommand=scroll.set)\nlistmessage.pack()\n\n# config scroll\nscroll.config(command=listmessage.yview)\nscroll.pack(side=RIGHT, fill=Y)\n\n\nfenetre.mainloop()\n","repo_name":"sekhriabdelali/Client-Serveur-Socket","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3404,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24525533659","text":"import datetime\nimport os\n\n\ndef days(datefrom, dateto):\n    datenow = datefrom\n    while datenow < dateto:\n        yield datenow\n        datenow += datetime.timedelta(days=1)\n\n\ndef mkdir_p(path):\n    \"\"\"\n    create directory {path} if necessary\n    \"\"\"\n    if os.path.exists(path) and os.path.isdir(path):\n        return\n\n    os.makedirs(path)\n\n\nONE_DAY = datetime.timedelta(days=1)\n\n\ndef td_to_hours(td: datetime.timedelta):\n    \"\"\"\n    :type td: timedelta\n    \"\"\"\n    day_hours = td.days * 24\n    full_hours = int(float(td.seconds) / 3600)\n    remaining_hours = float(td.seconds % 3600) / 3600\n    hours = (day_hours + full_hours + remaining_hours)\n    return round(hours, 2)\n","repo_name":"eugene-katsevman/keeper","sub_path":"keeper/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"22466370943","text":"import math\nimport random\n\n\nROW_COUNT = 6\nCOLUMN_COUNT = 7\n\nEMPTY = 0\nPLAYER_PIECE = 1\nAI_PIECE = -1\n\n\nWINDOW_LENGTH = 4\n\ndef drop_piece(board, row, col, piece):\n\tboard[row][col] = piece\n\ndef is_valid_location(board, col):\n\tfor i in range(0, 6):\n\t\tif board[i][col] == 0:\n\t\t\treturn True\n\treturn False\n\ndef get_next_open_row(board, col):\n\tfor r in range(ROW_COUNT):\n\t\tif board[r][col] == 0:\n\t\t\treturn r\n\n\n\ndef winning_move(board, piece):\n\t# Check horizontal locations for win\n\tfor c in range(COLUMN_COUNT-3):\n\t\tfor r in range(ROW_COUNT):\n\t\t\tif board[r][c] == piece and board[r][c+1] == piece and board[r][c+2] == piece and board[r][c+3] == piece:\n\t\t\t\treturn True\n\n\t# Check vertical locations for win\n\tfor c in range(COLUMN_COUNT):\n\t\tfor r in range(ROW_COUNT-3):\n\t\t\tif board[r][c] == piece and board[r+1][c] == piece and board[r+2][c] == piece and board[r+3][c] == piece:\n\t\t\t\treturn True\n\n\t# Check positively sloped diaganols\n\tfor c in range(COLUMN_COUNT-3):\n\t\tfor r in range(ROW_COUNT-3):\n\t\t\tif board[r][c] == piece and board[r+1][c+1] == piece and board[r+2][c+2] == piece and board[r+3][c+3] == piece:\n\t\t\t\treturn True\n\n\t# Check negatively sloped diaganols\n\tfor c in range(COLUMN_COUNT-3):\n\t\tfor r in range(3, ROW_COUNT):\n\t\t\tif board[r][c] == piece and board[r-1][c+1] == piece and board[r-2][c+2] == piece and board[r-3][c+3] == piece:\n\t\t\t\treturn True\n\treturn False\n\ndef evaluate_window(window, piece):\n\tscore = 0\n\topp_piece = PLAYER_PIECE\n\tif piece == PLAYER_PIECE:\n\t\topp_piece = AI_PIECE\n\n\tif window.count(piece) == 4:\n\t\tscore += 100\n\telif window.count(piece) == 3 and window.count(EMPTY) == 1:\n\t\tscore += 5\n\telif window.count(piece) == 2 and window.count(EMPTY) == 2:\n\t\tscore += 2\n\n\tif window.count(opp_piece) == 3 and window.count(EMPTY) == 1:\n\t\tscore -= 4\n\n\treturn score\n\ndef score_position(board, piece):\n\tscore = 0\n\n\t## Score center column\n\tcenter_array = [int(i) for i in list(board[:, COLUMN_COUNT//2])]\n\tcenter_count = center_array.count(piece)\n\tscore += center_count * 3\n\n\t## Score Horizontal\n\tfor r in range(ROW_COUNT):\n\t\trow_array = [int(i) for i in list(board[r,:])]\n\t\tfor c in range(COLUMN_COUNT-3):\n\t\t\twindow = row_array[c:c+WINDOW_LENGTH]\n\t\t\tscore += evaluate_window(window, piece)\n\n\t## Score Vertical\n\tfor c in range(COLUMN_COUNT):\n\t\tcol_array = [int(i) for i in list(board[:,c])]\n\t\tfor r in range(ROW_COUNT-3):\n\t\t\twindow = col_array[r:r+WINDOW_LENGTH]\n\t\t\tscore += evaluate_window(window, piece)\n\n\t## Score posiive sloped diagonal\n\tfor r in range(ROW_COUNT-3):\n\t\tfor c in range(COLUMN_COUNT-3):\n\t\t\twindow = [board[r+i][c+i] for i in range(WINDOW_LENGTH)]\n\t\t\tscore += evaluate_window(window, piece)\n\n\tfor r in range(ROW_COUNT-3):\n\t\tfor c in range(COLUMN_COUNT-3):\n\t\t\twindow = [board[r+3-i][c+i] for i in range(WINDOW_LENGTH)]\n\t\t\tscore += evaluate_window(window, piece)\n\n\treturn score\n\ndef is_terminal_node(board):\n\treturn winning_move(board, PLAYER_PIECE) or winning_move(board, AI_PIECE) or len(get_valid_locations(board)) == 0\n\ndef MinMax(board, depth, alpha, beta, maximizingPlayer):\n\tvalid_locations = get_valid_locations(board)\n\tis_terminal = is_terminal_node(board)\n\tif depth == 0 or is_terminal:\n\t\tif is_terminal:\n\t\t\tif winning_move(board, AI_PIECE):\n\t\t\t\treturn (None, 100000000000000)\n\t\t\telif winning_move(board, PLAYER_PIECE):\n\t\t\t\treturn (None, -10000000000000)\n\t\t\telse: # Game is over, no more valid moves\n\t\t\t\treturn (None, 0)\n\t\telse: # Depth is zero\n\t\t\treturn (None, score_position(board, AI_PIECE))\n\tif maximizingPlayer:\n\t\tvalue = -math.inf\n\t\tcolumn = random.choice(valid_locations)\n\t\tfor col in valid_locations:\n\t\t\trow = get_next_open_row(board, col)\n\t\t\tb_copy = board.copy()\n\t\t\tdrop_piece(b_copy, row, col, AI_PIECE)\n\t\t\tnew_score = MinMax(b_copy, depth-1, alpha, beta, False)[1]\n\t\t\tif new_score > value:\n\t\t\t\tvalue = new_score\n\t\t\t\tcolumn = col\n\t\t\talpha = max(alpha, value)\n\t\t\tif alpha >= beta:\n\t\t\t\tbreak\n\t\treturn column, value\n\n\telse: # Minimizing player\n\t\tvalue = math.inf\n\t\tcolumn = random.choice(valid_locations)\n\t\tfor col in valid_locations:\n\t\t\trow = get_next_open_row(board, col)\n\t\t\tb_copy = board.copy()\n\t\t\tdrop_piece(b_copy, row, col, PLAYER_PIECE)\n\t\t\tnew_score = MinMax(b_copy, depth-1, alpha, beta, True)[1]\n\t\t\tif new_score < value:\n\t\t\t\tvalue = new_score\n\t\t\t\tcolumn = col\n\t\t\tbeta = min(beta, value)\n\t\t\tif alpha >= beta:\n\t\t\t\tbreak\n\t\treturn column, value\n\ndef get_valid_locations(board):\n\tvalid_locations = []\n\tfor col in range(COLUMN_COUNT):\n\t\tif is_valid_location(board, col):\n\t\t\tvalid_locations.append(col)\n\treturn valid_locations\n\ndef pick_best_move(board, piece):\n\n\tvalid_locations = get_valid_locations(board)\n\tbest_score = -10000\n\tbest_col = random.choice(valid_locations)\n\tfor col in valid_locations:\n\t\trow = get_next_open_row(board, col)\n\t\ttemp_board = board.copy()\n\t\tdrop_piece(temp_board, row, col, piece)\n\t\tscore = score_position(temp_board, piece)\n\t\tif score > best_score:\n\t\t\tbest_score = score\n\t\t\tbest_col = col\n\n\treturn best_col\n","repo_name":"ElefthManolis/Deep-Q-Learning-Connect4","sub_path":"Connect4_MinMax.py","file_name":"Connect4_MinMax.py","file_ext":"py","file_size_in_byte":4881,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"7258437155","text":"####################################################################\n#           ______________________________________\n#  ________|                0xFlags               |_______\n#  \\       |                                      |      /\n#   \\      |              @0x68616469             |     /\n#   /      |______________________________________|     \\\n#  /__________)                                (_________\\\n#\n# A Handy Package to Parse Command-Line Arguments\n#\n# Github repo : https://github.com/0x68616469/oxflags\n#\n####################################################################\n\nimport sys\nfrom oxtables import Table\nfrom oxansi import Short as a\n\nclass Flag:\n\n    def __init__(self, **kwargs): \n        self.description = kwargs.get('description', '')\n        self.flags = kwargs.get('flags', [])\n    \n    class flag:\n        def __init__(self, **kwargs):\n            self.name = kwargs.get('name', '').replace('-','')\n            self.short = kwargs.get('short', '')\n            self.full = kwargs.get('full', '')\n            self.default = kwargs.get('default', '')\n            self.type = kwargs.get('type', 'string')\n            self.required = kwargs.get('required', False)\n            self.help = kwargs.get('help', '')\n            \n    def new(self, **kwargs):\n        short = kwargs.get('short', '')\n        full = kwargs.get('full', '')\n        default = kwargs.get('default', '')\n        type = kwargs.get('type', 'string')\n        required = kwargs.get('required', False)\n        help = kwargs.get('help', '')\n\n        if 'full' not in kwargs:\n            print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}Long option required: (.., full='..') \")\n            sys.exit(1)\n        \n        if (default == \"\" and type != \"bool\") and (default == \"\" and required != True):\n            print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}Default value required for {full}.\")\n            sys.exit(1)\n    \n        self.flags.append(self.flag(short=short, full=full, default=default, type=type, required=required, help=help))\n                \n    def parse(self):\n        for flag in self.flags:\n            setattr(self, flag.full.replace('-',''), flag.default)\n        \n        argc = len(sys.argv)\n        i = 1\n        while i < argc:\n            for flag in self.flags:\n                if sys.argv[i] == flag.short or sys.argv[i] == flag.full:\n                    if flag.type == \"bool\":\n                        if flag.default == True:\n                            setattr(self, flag.full.replace('-',''), False)\n                        else:\n                            setattr(self, flag.full.replace('-',''), True)\n                    \n                    elif flag.type == \"int\":\n                        i += 1\n                        if i >= argc:\n                            print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}Missing \\\"int\\\" argument for \\\"{flag.full}\\\".\")\n                            sys.exit(1)\n                        setattr(self, flag.full.replace('-',''), int(sys.argv[i]))\n\n                    else: # Consider as string\n                        i += 1\n                        if i >= argc:\n                            print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}Missing \\\"string\\\" argument for \\\"{flag.full}\\\".\")\n                            sys.exit(1)\n                        setattr(self, flag.full.replace('-',''), sys.argv[i])\n                    \n                    flag.required = False\n                    break\n                \n                elif sys.argv[i] == \"-h\" or sys.argv[i] == \"--help\":\n                    print(f\"{a.bl}[{a.g}?{a.bl}] {a.bl}Help: {a.rst}{self.description}.\")\n                    th = Table(spacer=True, style=\"=\", border_color=\"black\")\n                    th.add([\"\", \"Option\", \"Description\", \"Default\"])\n                    for flag in self.flags:\n                        th.add([flag.short, flag.full, flag.help, flag.default])\n                    th.draw()\n                    sys.exit(0)\n                    \n            else:\n                print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}\\\"{sys.argv[i]}\\\" unknown.\")\n                sys.exit(1)\n                \n            i += 1\n            \n        for flag in self.flags:\n            if flag.required == True:\n                print(f\"{a.bl}[{a.r}!{a.bl}] {a.r}Error: {a.rst}\\\"{flag.full}\\\" is required.\")\n                sys.exit(1)\n","repo_name":"0x68616469/oxflags","sub_path":"oxflags/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13751914188","text":"import requests\nimport json\nimport sys\n\nclass airbnbScraper:\n\n    def __init__(self, init_rooms):\n        self.rooms = []\n        self.results = {}\n        # required data with key and value for prettyprint\n        self.requiredData = {\n            'name':'Property Name',\n            'localized_room_type':'Property Type',\n            'bedrooms':'# of Bedrooms',\n            'beds':'# of Beds',\n            'bathroom_label':'# of Bathrooms'\n            }\n\n        # New line variable for handling prettyprint\n        self.newLine = '\\n\\r'\n\n        if init_rooms is not None:\n            for room in init_rooms:\n                self.rooms.append(room)\n        else:\n            # given room list, hard coded for now\n            self.rooms = [\n            'https://www.airbnb.co.uk/rooms/14531512?s=51',\n            'https://www.airbnb.co.uk/rooms/19278160?s=51',\n            'https://www.airbnb.co.uk/rooms/19292873?s=51'\n            ]\n\n\n    def getPrettyAttributeName(self, attribute):\n        return self.requiredData[attribute]\n\n    def getRoomJson(self, request):\n        startTag = \"<!--\"\n        endTag = \"-->\"\n\n        # grab text from http request\n        rText = request.text\n\n        # trim down the html page\n        trimmed = rText[rText.find('<script type=\"application/json\" data-hypernova-key=\"p3show_marketplacebundlejs\"'):]\n\n        # extract the data we need by the HTML comment tags (remembering to include the length of the start tag itself)\n        data = trimmed[ trimmed.find(startTag) + len(startTag) : trimmed.find(endTag) ]\n\n        # load it as json and return it\n        return json.loads(data)\n\n    def getListingJson(self, roomJson):\n        # return the data we're interested in from the overall JSON\n        return roomJson['bootstrapData']['reduxData']['marketplacePdp']['listingInfo']['listing']\n\n    def getAmenityJson(self, listingJson):\n        # return the amenity data\n        return listingJson['listing_amenities']\n\n    def getListingId(self,listingJson):\n        return listingJson['id']\n\n    def getListingData(self, listingJson):\n        # results\n        listingData = {}\n        # loop through each attribute in the listing\n        for attribute in listingJson:\n\n            # if it's something we're interested in, add it to the results\n            if attribute in self.requiredData:\n                # grabs the human friendly name for the attribute E.G. \"Property Name\" for \"localized_room_type\"\n                listingData[self.getPrettyAttributeName(attribute)] = listingJson[attribute]\n        return listingData\n\n    def printListingData(self, filename, listingJson):\n        filename.write( self.getListingData(listingJson) )\n\n    def getAmenityData(self, amenityJson):\n        # results\n        amenityData = []\n        # Iterator for unnamed elements in json\n        i = 0\n\n        # loop through each amenity\n        for amenity in amenityJson:\n\n            # all amenities are listed, but only those which 'is_present' are the ones we're interested in\n            if amenity['is_present']:\n\n                # amenity names are pretty enough, no need to tidy\n                amenityData.append(amenityJson[i]['name'])\n            i += 1\n        return amenityData\n\n    def printAmenityData(self, filename, amenityJson):\n        filename.write( self.getAmenityData(amenityJson) )\n\n    def getListingTitle(self, listingId):\n        return self.newLine + '==== ROOM: ' + str ( listingId ) + ' ====' + self.newLine\n\n    def printListingTitle(self, filename):\n        filename.write(self.getListingTitle())\n\n    def getAmenityTitle(self):\n        return self.newLine + '==== AMENITIES ====' + self.newLine\n\n    def printAmenityTitle(self, filename):\n        filename.write(self.getAmenityTitle())\n\n    def scrapeUrl(self, url):\n        roomData = {}\n        # make the request, get the overall JSON\n        urlData = self.getRoomJson(requests.get(url))\n\n        # get the listing JSON\n        listingData = self.getListingJson(urlData)\n\n        # get the amenity JSON\n        amenityData = self.getAmenityJson(listingData)\n\n        # add listing data to the room object\n        roomData['Listing'] = self.getListingData(listingData)\n\n        # add amenity data to room object\n        roomData['Amenities'] = self.getAmenityData(amenityData)\n\n        # add room object to the overall results\n        self.results[self.getListingId(listingData)] = roomData\n\n        return self.results\n\n    def printResults(self):\n        # for each room\n        for entry in self.results:\n            \n            #print the data\n            print( self.getListingTitle( entry ) )\n            for key, value in self.results[entry]['Listing'].items():\n                print (key + ': ' + str( value ) + self.newLine)\n\n            print( self.getAmenityTitle() )\n\n            for amenity in self.results[entry]['Amenities']:\n                print ( str(amenity) )\n\nif __name__ == \"__main__\":\n    # pass parameters (ignoring function call)\n    scraper = airbnbScraper(sys.argv[1:])\n\n    # loop through each room we're interested in\n    for room in scraper.rooms:\n        scraper.scrapeUrl(room)\n\n    scraper.printResults()\n","repo_name":"Kashk/airbnbscraper","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":5153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38399324373","text":"# .replace 활용\n\nnum_list = [\"ZRO\", \"ONE\", \"TWO\", \"THR\", \"FOR\", \"FIV\", \"SIX\", \"SVN\", \"EGT\", \"NIN\"]\n\nT = int(input())\n\nfor tc in range(1, T + 1):\n    N = int(input().split()[1])\n    data = input()\n\n    for num, num_str in enumerate(num_list):\n        data = data.replace(num_str, str(num))\n\n    data_list = data.split()\n    data_list.sort()\n    ans = \" \".join(data_list)\n\n    for num, num_str in enumerate(num_list):\n        ans = ans.replace(str(num), num_str)\n\n    print(\"#{}\".format(tc))\n    print(ans)\n","repo_name":"LeeSungRyul/TIL","sub_path":"Algorithm/SWEA/D3/1221_d3_GNS_2.py","file_name":"1221_d3_GNS_2.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33679428743","text":"from collections import deque\r\nimport sys\r\n\r\ninput = sys.stdin.readline\r\n\r\nqueue = deque()\r\nN = int(input())\r\nfor _ in range(N):\r\n    cmd = input()\r\n    if 'push' in cmd:\r\n        command, num = cmd.split()\r\n        queue.append(int(num))\r\n    elif 'pop' in cmd:\r\n        if len(queue) != 0:\r\n            print(queue.popleft())\r\n        else:\r\n            print(-1)\r\n    elif 'size' in cmd:\r\n        print(len(queue))\r\n    elif 'empty' in cmd:\r\n        if len(queue) == 0:\r\n            print(1)\r\n        else:\r\n            print(0)\r\n    elif 'front' in cmd:\r\n        if len(queue) != 0:\r\n            print(queue[0])\r\n        else:\r\n            print(-1)\r\n    elif 'back' in cmd:\r\n        if len(queue) != 0:\r\n            print(queue[-1])\r\n        else:\r\n            print(-1)\r\n    # print(*queue)\r\n    ","repo_name":"ash-hun/my_Boj","sub_path":"백준/Silver/18258. 큐 2/큐 2.py","file_name":"큐 2.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39979535173","text":"import pygame\n\n\nclass Globals:\n    _instance = None\n\n    def __new__(cls, *args, **kwargs):\n        if not cls._instance:\n            cls._instance = super(Globals, cls).__new__(cls, *args, **kwargs)\n        return cls._instance\n\n    def __init__(self):\n        self.main = None\n        self.amount_of_vertices = 4\n\n        self.red = (255, 0, 0,1)\n        self.blue = (0, 0, 255,1)\n        self.green = (0, 255, 0,1)\n        self.white = (255,255,255,1)\n        self.black = (0,0,0,1)\n        self.widget_color = (24,116,165,0.7)\n        self.shadow_color = (135,62,35,0)\n        self.font_color = (226,135,67,1)\n        self.font = pygame.font.Font('GUI/Targa.ttf', 64)\n        self.text_font = pygame.font.Font('GUI/Targa.ttf', 21)\n        self.shadow_font = pygame.font.Font('GUI/Targa.ttf', 68)\n        self.clock = pygame.time.Clock()\n\n        self.main = None\n        self.screen_x, self.screen_y = 1440, 960\n        self.screen_size = (self.screen_x, self.screen_y)\n        self.screen = pygame.display.set_mode(self.screen_size)\n        self.menu_is_enabled = True\n\n    def set_main(self, main):\n        self.main = main\n","repo_name":"LeoEngelberger/TSPVisualizer","sub_path":"globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4681713013","text":"\"\"\"\nWrite a program that lets the user insert a number of\nhours, minutes and seconds and prints the total number of\nseconds.\n\"\"\"\nimport sys\n\nprint(\"Usage: ./print_secs.py <hour> ?<minute> ?<second>\")\ntry:\n  if (len(sys.argv) > 4) or (len(sys.argv) < 2):\n    raise ValueError(\"Invalid number of arguments\")\n  else:\n    if len(sys.argv) >= 2:\n      ret = int(sys.argv[1]) * 60 * 60\n    if len(sys.argv) >= 3:\n      ret += int(sys.argv[2]) * 60\n    if len(sys.argv) == 4:\n      ret += int(sys.argv[3])\n    print(\"Total number of seconds:\", ret)\nexcept ValueError as e:\n  print(e)\n  print(\"Usage: ./print_secs.py <hour> ?<minute> ?<second>\")\n  sys.exit(1)","repo_name":"keiji11/42DATA-LAB","sub_path":"week1/ex01/print_secs.py","file_name":"print_secs.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20786764572","text":"import prediction.prediction\nimport vegetation.vegetation\nimport inquirer\n\nwhile True:\n    questions = [\n        inquirer.List('choice',\n                      message=\"Choose Country?\",\n                      choices=['bolivia', 'china', 'exit'],\n                      ),\n    ]\n    answers = inquirer.prompt(questions)\n    country = answers['choice']\n    if country == 'exit':\n        break\n    questions = [\n        inquirer.List('choice',\n                      message=\"Choose Action?\",\n                      choices=['find vegetation', 'show vegetation', 'predict', 'create model'],\n                      ),\n    ]\n    answers = inquirer.prompt(questions)\n\n    action = answers['choice']\n    if action == \"find vegetation\":\n        vegetation.vegetation.getvegetation(country)\n    if action == \"show vegetation\":\n        vegetation.vegetation.createOverlay(country)\n    if action ==\"predict\":\n        imgs = vegetation.vegetation.convertImgsToArrays(country)\n        img = imgs[len(imgs)-1]\n        prediction.prediction.predictFutureVegetation(img, country)\n    if action == \"create model\":\n        prediction.prediction.teachModel(vegetation.vegetation.convertImgsToArrays(country),country)\n\n","repo_name":"Morwors/eco-project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16309071162","text":"############################################################\n#   Dev: Josias Moukpe\n#   Class: Machine Learning\n#   Date: 2/22/2022\n#   file: testTennis.py\n#   Description: corrupt the class labels of\n#   training examples from 0% to 20% (2% in-\n#   crement) by changing from the correct class\n#   to another class; output the accuracy on the\n#   uncorrupted test set with and without rule\n#   post-pruning.\n#############################################################\n\n\n# imports\nfrom learner import Learner\nfrom utils import corrupt_data, consolidate_rules\n\n\ndef main():\n    '''main of the program'''\n\n    training_path = 'data/iris/iris-train.txt'\n    testing_path = 'data/iris/iris-test.txt'\n    attributes_path = 'data/iris/iris-attr.txt'\n    debugging = False\n    validation = True # higher accuracy when test data is used for vaildation\n\n    # create learner\n    dtl = Learner(\n        attributes_path, \n        training_path, \n        testing_path, \n        debugging,\n        validation\n    )\n\n    # start of the experiment \n    for p in range(0, 22, 2):\n\n\n        print('////////////////////////////////////////////////////////////')\n        # corrupt the data'\n        print('\\nCorrupting the data by changing from the correct class to another class...')\n        print('\\nPercentage of corruption: {}%'.format(p))\n        dtl.training = corrupt_data(dtl.training, dtl.get_classes(), p / 100.)\n        # print('\\nCorrupted data:')\n        # print(dtl.training)\n\n        print('\\nLearning the decision tree...\\n')\n        # run the program\n        tree = dtl.learn()\n\n        print('\\nTesting the tree on uncorrupted testing data\\n')\n        # testing tree on test data\n        testing_acc = dtl.test(tree, dtl.testing)\n        print('\\nTesting accuracy: ', testing_acc)\n\n        print('\\nPrinting the decision tree rules\\n')\n        # print the rules\n        dtl.tree_to_rules(tree)    \n        # tree.print_rules()\n\n        print('\\nPruning the tree...\\n')\n        # prune the tree\n        tree = dtl.rule_post_pruning(tree, dtl.validation)\n        tree.rules = consolidate_rules(tree.rules)\n        # tree.print_rules()\n\n        print('\\nTesting the rules on uncorrupted testing data\\n')\n        # testing tree on test data\n        testing_acc = dtl.test(tree, dtl.testing)\n        print('\\nTesting accuracy: ', testing_acc)\n\n    \nif __name__ == '__main__':\n    main()\n","repo_name":"ERUD1T3/decision-tree","sub_path":"source/testIrisNoisy.py","file_name":"testIrisNoisy.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29454921049","text":"import random\n\ndef revisar_palabra(self):\n    if self == juego.palabra_secreta:\n        print(\"GANASTE!\")\n        return True\n    else:\n        return False\n\n\ndef ocultar_letras(palabra, cantidad):\n    lista = []\n    for j in palabra:\n        lista.append(j)\n        #print(lista)\n    for i in range(cantidad):\n        espacio = random.randint(0, len(palabra)-1)\n        lista[espacio] = \"_\"\n    palabra = \"\".join(lista)\n    #print(palabra)\n    return palabra\n\n\ndef revisar_letra(palabra_secreta, palabra, letra):\n    añadir_letra = True\n    guiones = [i for i, c in enumerate(palabra) if c == \"_\"]\n    #print(guiones)\n    palabra = list(palabra)\n    for i in guiones:\n        i = int(i)\n        if palabra_secreta[i] == letra:\n            palabra[i] = letra\n            añadir_letra = False\n    if añadir_letra:\n        juego.letras_erroneas.append(letra.upper())\n    palabra = \"\".join(palabra)\n    return palabra\n\nclass juego:\n    @staticmethod\n    def jugar():\n        while juego.vidas > 0:\n            letra = str(input(\"Ingrese una palabra o letra:\"))\n            if len(letra) > 1:\n                if revisar_palabra(letra):\n                    break\n                else:\n                    juego.vidas -= 1\n                    print(\"Intente de nuevo\")\n            else:\n                juego.palabra = revisar_letra(juego.palabra_secreta, juego.palabra, letra)\n                print(juego.palabra)\n                print(\"Letras Erroneas: \", juego.letras_erroneas)\n            if revisar_palabra(juego.palabra):\n                break\n\n    palabras_posibles= [\"cocos\", \"huevos\", \"bolas\", \"cola\", \"lepidoptero\", \"murcielago\", \"mañana\"]\n    vidas = 7\n    palabra_secreta = random.choice(palabras_posibles)\n    #print(palabra_secreta)\n    cantidad = int(len(palabra_secreta)//1.5)\n    #print(cantidad)\n    palabra = ocultar_letras(palabra_secreta, cantidad)\n    letras_erroneas = []\n\nif __name__ == \"__main__\":\n    juego.jugar()\n\n         ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema4_ej1/tema4_ej1_826437a331d3bd03239b06b27d1c1234.py","file_name":"tema4_ej1_826437a331d3bd03239b06b27d1c1234.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18662078721","text":"# 6. Write a program to calculate surface volume and area of a cylinder.\r\n\r\n\r\nPIE = 3.14 \r\nradius = float(input(\"input radius of cyclinder : \"))\r\nheight = float(input(\"input height of cyclinder : \"))\r\n\r\narea = 2*PIE*radius*(radius+height)\r\nvolume =  2*PIE*radius**2\r\n\r\nprint(\"Surface volume of cylinder : \",area)\r\nprint(\"Surface area of cylinder : \",volume)\r\n","repo_name":"Rohit-Nandagawali/python-practicals","sub_path":"practical 3/ex6.py","file_name":"ex6.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33376343865","text":"import torch\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport config\nfrom data_loader import *\nfrom model import *\nfrom utils import *\nfrom loss import *\n\n# Multislice Viewer by: https://www.datacamp.com/community/tutorials/matplotlib-3d-volumetric-data\n\ndef multi_slice_viewer(img, gt, pred):\n    fig, ax = plt.subplots(1, 2)\n    gt_c = img_tumor_map(img, gt)\n    pred_c = img_tumor_map(img, pred)\n    for i in range(2):\n        if i == 1:\n            ax[i].volume = pred_c\n        else:\n            ax[i].volume = gt_c\n        ax[i].index = img.shape[0] // 2\n        if i == 1:\n            ax[i].imshow(pred_c[ax[i].index])\n        else:\n            ax[i].imshow(gt_c[ax[i].index])\n    fig.canvas.mpl_connect('key_press_event', process_key)\n\ndef img_tumor_map(img, map):\n    color_0 = [0/255, 0/255, 0/255, 0]\n    color_1 = [150/255, 10/255, 50/255, 0]\n    color_2 = [10/255, 150/255, 50/255, 0]\n    color_3 = [10/255, 100/255, 100/255, 0]\n    map_0 = map == 0\n    map_1 = map == 1\n    map_2 = map == 2\n    map_3 = map == 3\n    map_ = np.zeros([128, 128, 128, 4])\n    map_[map_0, :] = np.array(color_0)\n    map_[map_1, :] = np.array(color_1)\n    map_[map_2, :] = np.array(color_2)\n    map_[map_3, :] = np.array(color_3)\n    print(map_)\n    img = img - np.min(img)\n    img = img / np.max(img)\n    map_[..., 3] = img\n    return map_\n\ndef process_key(event):\n    fig = event.canvas.figure\n    ax_0 = fig.axes[0]\n    ax_1 = fig.axes[1]\n    if event.key == 'j':\n        previous_slice(ax_0)\n        previous_slice(ax_1)\n    elif event.key == 'k':\n        next_slice(ax_0)\n        next_slice(ax_1)\n    fig.canvas.draw()\n\ndef previous_slice(ax):\n    volume = ax.volume\n    ax.index = (ax.index - 1) % volume.shape[0]  # wrap around using %\n    ax.images[0].set_array(volume[ax.index])\n\ndef next_slice(ax):\n    volume = ax.volume\n    ax.index = (ax.index + 1) % volume.shape[0]\n    ax.images[0].set_array(volume[ax.index])\n\ndef cod2label(map):\n    map_com = torch.zeros([128, 128, 128])\n    wt_mask = map[0] == 1\n    map_com[wt_mask] = 1\n    et_mask = map[1] == 1\n    map_com[et_mask] = 2\n    ed_mask = map[2] == 1\n    map_com[ed_mask] = 3\n    return map_com\n\n\nif __name__ == '__main__':\n    import matplotlib.pyplot as plt\n    import matplotlib.cm as cm\n    import matplotlib.animation as animation\n\n    csv_path = '/media/tensorist/Extreme SSD/brats2020/testset.csv'\n    loader = BrainLoader(csv_path, train=False)\n    model = UNet3D(4, [16, 32, 64, 128, 256], num_classes=config.NUM_CLASSES).to(config.DEVICE)\n    model = model.float()\n    optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE, weight_decay=config.WEIGHT_DECAY)\n    train, test = dataset_loader()\n    load_checkpoint(config.CHECKPOINT + '_72.pth.tar', model, optimizer, optimizer.param_groups[0]['lr'])\n    loss_metric = LossAndMetric(num_classes=4)\n\n    animation = False\n\n    frames = []\n\n    for i in range(1):\n        img, gt = loader[1]\n        img = torch.tensor(img[np.newaxis, ...])\n        gt = torch.tensor(gt[np.newaxis, ...])\n        img = img.to(config.DEVICE)\n        gt = gt.to(config.DEVICE)\n        pred_logits = model(img.float())\n        pred_probs = torch.sigmoid(pred_logits)\n        pred_probs_one_mask = pred_probs > 0.5\n        pred_ = torch.zeros_like(pred_probs)\n        pred_[pred_probs_one_mask] = 1\n        pred_ = pred_.squeeze()\n        img = img.cpu().numpy().squeeze()\n        gt = gt.cpu().numpy().squeeze()\n        gt_com = cod2label(gt)\n        pred_com = cod2label(pred_)\n        if animation:\n            fig, ax = plt.subplots(1, 2)\n            gt_c = img_tumor_map(img[0], gt_com)\n            pred_c = img_tumor_map(img[0], pred_com)\n            for i in range(img.shape[1]):\n                frames.append([ax[0].imshow(gt_c[i, :, :], animated=True),\n                               ax[1].imshow(pred_c[i, :, :], animated=True)])\n        else:\n            multi_slice_viewer(img[0], gt_com, pred_com)\n            plt.show()\n\n    if animation:\n        ani = animation.ArtistAnimation(fig, frames, interval=200, blit=True,\n                                        repeat_delay=1000)\n        ani.save('/home/tensorist/project_files/tumor_segmentation/application_movie_bsp8.gif', writer='imagemagick')\n        plt.show()\n\n","repo_name":"marsolo93/3D-Segmentation-of-Brain-Tumors-with-PyTorch","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":4249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"75152072567","text":"import csv\n\nclass Node:\n    def __init__(self, key, value=None, left=None, right=None):\n        self.key = key\n        self.value = value\n        self.left = left\n        self.right = right\n\ndef add(root, key, value=None):\n    if root == None:\n        root = Node(key, value=value)\n        return root\n    else:\n        if key < root.key:\n            if root.left == None:\n                root.left = Node(key, value=value)\n            else:\n                add(root.left, key, value=value)\n        else:\n            if root.right == None:\n                root.right = Node(key, value=value)\n            else:\n                add(root.right, key, value=value)\n        return root\n\ndef search(root, key):\n    if key == root.key:\n        return root.value\n    elif key < root.key:\n        if root.left is None:\n            return 'No value attributed to key'\n        return search(root.left, key)\n    elif key > root.key:\n        if root.right is None:\n            return str(key)+ 'No value attributed to key'\n        return search(root.right, key)\n    else:\n        print(str(root.key) + ' is found')\n\nif __name__ == '__main__':\n\n    file = 'tree_test_csv.csv'\n\n    first_line = True\n    for line in open(file, 'r'):\n        file_data = line.rstrip().split(',')\n        if first_line is True:\n            data_tree = Node(file_data[0], file_data[1])\n            first_line = False\n        else:\n            add(data_tree, file_data[0], file_data[1])\n\n    find_key = '10'\n    tree_traverse = search(data_tree, find_key)\n    print('key = ' + find_key + '\\n' + 'value = ' + str(tree_traverse))\n\n\n\n\n\n\n#    root = None\n\n#    root = add(root, 9, 8)\n#    root = add(root, 6)\n#    root = add(root, 8)\n#    root = add(root, 15)\n#    root = add(root, 13)\n#    root = add(root, 21)\n#\n#    root = add(root, 6)\n#    root = add(root, 14)\n#    root = add(root, 3)\n\n\n#    print(root.key, root.value)\n#    print(root.left.key, root.left.value)\n#    print(root.right.left.key, root.right.left.value)\n#    print(root.left.right.key)\n","repo_name":"cu-swe4s-fall-2019/trees-adziulko","sub_path":"binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6461745193","text":"import pandas as pd\nimport shutil, os\nimport os.path as osp\nimport torch\nimport numpy as np\nfrom torch_geometric.data import InMemoryDataset\nfrom ogb.utils.url import decide_download, download_url, extract_zip\nfrom ogb.io.read_graph_pyg import read_graph_pyg\nfrom itertools import repeat, product\nfrom copy import deepcopy\n\nclass PygGraphPropPredDataset(InMemoryDataset):\n    def __init__(self, name, root = 'dataset', transform=None, pre_transform = None, meta_dict = None,aug = None):\n\n        self.name = name ## original name, e.g., ogbg-molhiv\n        \n        if meta_dict is None:\n            self.dir_name = '_'.join(name.split('-')) \n            \n            # check if previously-downloaded folder exists.\n            # If so, use that one.\n            if osp.exists(osp.join(root, self.dir_name + '_pyg')):\n                self.dir_name = self.dir_name + '_pyg'\n\n            self.original_root = root\n            self.root = osp.join(root, self.dir_name)\n            \n            master = pd.read_csv(os.path.join(os.path.dirname(__file__), 'master.csv'), index_col = 0)\n            if not self.name in master:\n                error_mssg = 'Invalid dataset name {}.\\n'.format(self.name)\n                error_mssg += 'Available datasets are as follows:\\n'\n                error_mssg += '\\n'.join(master.keys())\n                raise ValueError(error_mssg)\n            self.meta_info = master[self.name]\n            \n        else:\n            self.dir_name = meta_dict['dir_path']\n            self.original_root = ''\n            self.root = meta_dict['dir_path']\n            self.meta_info = meta_dict\n    \n        if osp.isdir(self.root) and (not osp.exists(osp.join(self.root, 'RELEASE_v' + str(self.meta_info['version']) + '.txt'))):\n            print(self.name + ' has been updated.')\n            if input('Will you update the dataset now? (y/N)\\n').lower() == 'y':\n                shutil.rmtree(self.root)\n\n        self.download_name = self.meta_info['download_name'] ## name of downloaded file, e.g., tox21\n\n        self.num_tasks = int(self.meta_info['num tasks'])\n        self.eval_metric = self.meta_info['eval metric']\n        self.task_type = self.meta_info['task type']\n        self.__num_classes__ = int(self.meta_info['num classes'])\n        self.binary = self.meta_info['binary'] == 'True'\n\n        super(PygGraphPropPredDataset, self).__init__(self.root, transform, pre_transform)\n\n        self.data, self.slices = torch.load(self.processed_paths[0])\n        self.aug = aug\n\n    def get_idx_split(self, split_type = None):\n        print('------------------------get---------------------------------------')\n        if split_type is None:\n            split_type = self.meta_info['split']\n            \n        path = osp.join(self.root, 'split', split_type)\n\n        # short-cut if split_dict.pt exists\n        if os.path.isfile(os.path.join(path, 'split_dict.pt')):\n            return torch.load(os.path.join(path, 'split_dict.pt'))\n\n        train_idx = pd.read_csv(osp.join(path, 'train.csv.gz'), compression='gzip', header = None).values.T[0]\n        valid_idx = pd.read_csv(osp.join(path, 'valid.csv.gz'), compression='gzip', header = None).values.T[0]\n        test_idx = pd.read_csv(osp.join(path, 'test.csv.gz'), compression='gzip', header = None).values.T[0]\n\n        return {'train': torch.tensor(train_idx, dtype = torch.long), 'valid': torch.tensor(valid_idx, dtype = torch.long), 'test': torch.tensor(test_idx, dtype = torch.long)}\n\n    @property\n    def num_classes(self):\n        return self.__num_classes__\n\n    @property\n    def raw_file_names(self):\n        if self.binary:\n            return ['data.npz']\n        else:\n            file_names = ['edge']\n            if self.meta_info['has_node_attr'] == 'True':\n                file_names.append('node-feat')\n            if self.meta_info['has_edge_attr'] == 'True':\n                file_names.append('edge-feat')\n            return [file_name + '.csv.gz' for file_name in file_names]\n\n    @property\n    def processed_file_names(self):\n        return 'geometric_data_processed.pt'\n\n    def download(self):\n        url = self.meta_info['url']\n        if decide_download(url):\n            path = download_url(url, self.original_root)\n            extract_zip(path, self.original_root)\n            os.unlink(path)\n            shutil.rmtree(self.root)\n            shutil.move(osp.join(self.original_root, self.download_name), self.root)\n\n        else:\n            print('Stop downloading.')\n            shutil.rmtree(self.root)\n            exit(-1)\n\n    def process(self):\n        print('------------------process--------------------------')\n        ### read pyg graph list\n        add_inverse_edge = self.meta_info['add_inverse_edge'] == 'True'\n\n        if self.meta_info['additional node files'] == 'None':\n            additional_node_files = []\n        else:\n            additional_node_files = self.meta_info['additional node files'].split(',')\n\n        if self.meta_info['additional edge files'] == 'None':\n            additional_edge_files = []\n        else:\n            additional_edge_files = self.meta_info['additional edge files'].split(',')\n\n        data_list = read_graph_pyg(self.raw_dir, add_inverse_edge = add_inverse_edge, additional_node_files = additional_node_files, additional_edge_files = additional_edge_files, binary=self.binary)\n\n        if self.task_type == 'subtoken prediction':\n            graph_label_notparsed = pd.read_csv(osp.join(self.raw_dir, 'graph-label.csv.gz'), compression='gzip', header = None).values\n            graph_label = [str(graph_label_notparsed[i][0]).split(' ') for i in range(len(graph_label_notparsed))]\n\n            for i, g in enumerate(data_list):\n                g.y = graph_label[i]\n\n        else:\n            if self.binary:\n                graph_label = np.load(osp.join(self.raw_dir, 'graph-label.npz'))['graph_label']\n            else:\n                graph_label = pd.read_csv(osp.join(self.raw_dir, 'graph-label.csv.gz'), compression='gzip', header = None).values\n\n            has_nan = np.isnan(graph_label).any()\n\n            for i, g in enumerate(data_list):\n                if 'classification' in self.task_type:\n                    if has_nan:\n                        g.y = torch.from_numpy(graph_label[i]).view(1,-1).to(torch.float32)\n                    else:\n                        g.y = torch.from_numpy(graph_label[i]).view(1,-1).to(torch.long)\n                else:\n                    g.y = torch.from_numpy(graph_label[i]).view(1,-1).to(torch.float32)\n\n        if self.pre_transform is not None:\n            data_list = [self.pre_transform(data) for data in data_list]\n\n        data, slices = self.collate(data_list)\n\n        print('Saving...')\n        torch.save((data, slices), self.processed_paths[0])\n    def get(self, idx):\n        data = self.data.__class__()\n\n        if hasattr(self.data, '__num_nodes__'):\n            data.num_nodes = self.data.__num_nodes__[idx]\n\n        for key in self.slices.keys():\n            item, slices = self.data[key], self.slices[key]\n            if torch.is_tensor(item):\n                s = list(repeat(slice(None), item.dim()))\n                s[self.data.__cat_dim__(key,\n                                        item)] = slice(slices[idx],\n                                                       slices[idx + 1])\n            else:\n                s = slice(slices[idx], slices[idx + 1])\n            data[key] = item[s]\n\n\n        node_num = torch.tensor(data.x.size()[0] - 1)\n        \n        sl = torch.tensor([[n,n] for n in range(node_num)]).t()\n        data.edge_index = torch.cat((data.edge_index, sl), dim=1)\n\n        if self.aug == 'dnodes':\n            data_aug = drop_nodes(deepcopy(data))\n        elif self.aug == 'pedges':\n            data_aug = permute_edges(deepcopy(data))\n        elif self.aug == 'subgraph':\n            data_aug = subgraph(deepcopy(data))\n        elif self.aug == 'mask_nodes':\n            data_aug = mask_nodes(deepcopy(data))\n        elif self.aug == 'none':\n            \"\"\"\n            if data.edge_index.max() > data.x.size()[0]:\n                print(data.edge_index)\n                print(data.x.size())\n                assert False\n            \"\"\"\n            data_aug = deepcopy(data)\n            data_aug.x = torch.ones((data.edge_index.max()+1, 1))\n\n        elif self.aug == 'random2':\n            n = np.random.randint(2)\n            if n == 0:\n               data_aug = drop_nodes(deepcopy(data))\n            elif n == 1:\n               data_aug = subgraph(deepcopy(data))\n            else:\n                print('sample error')\n                assert False\n\n\n        elif self.aug == 'random3':\n            n = np.random.randint(3)\n            if n == 0:\n               data_aug = drop_nodes(deepcopy(data))\n            elif n == 1:\n               data_aug = permute_edges(deepcopy(data))\n            elif n == 2:\n               data_aug = subgraph(deepcopy(data))\n            else:\n                print('sample error')\n                assert False\n\n\n        elif self.aug == 'random4':\n            n = np.random.randint(4)\n            if n == 0:\n               data_aug = drop_nodes(deepcopy(data))\n            elif n == 1:\n               data_aug = permute_edges(deepcopy(data))\n            elif n == 2:\n               data_aug = subgraph(deepcopy(data))\n            elif n == 3:\n               data_aug = mask_nodes(deepcopy(data))\n            else:\n                print('sample error')\n                assert False\n\n        elif self.aug == 'minmax':\n            n = np.random.choice(5, 1)[0]\n            if n == 0:\n               data_aug = drop_nodes(deepcopy(data))\n            elif n == 1:\n               data_aug = permute_edges(deepcopy(data))\n            elif n == 2:\n               data_aug = subgraph(deepcopy(data))\n            elif n == 3:\n               data_aug = mask_nodes(deepcopy(data))\n            elif n == 4:\n                data_aug = deepcopy(data)\n\n        else:\n            print('augmentation error')\n            assert False\n        return data, data_aug\n\n\ndef drop_nodes(data):\n\n    node_num, _ = data.x.size()\n    _, edge_num = data.edge_index.size()\n    drop_num = int(node_num / 10)\n\n    idx_drop = np.random.choice(node_num, drop_num, replace=False)\n    idx_nondrop = [n for n in range(node_num) if not n in idx_drop]\n    idx_dict = {idx_nondrop[n]:n for n in list(range(node_num - drop_num))}\n\n    # data.x = data.x[idx_nondrop]\n    edge_index = data.edge_index.numpy()\n\n    adj = torch.zeros((node_num, node_num))\n    adj[edge_index[0], edge_index[1]] = 1\n    adj[idx_drop, :] = 0\n    adj[:, idx_drop] = 0\n    edge_index = adj.nonzero().t()\n\n    data.edge_index = edge_index\n\n\n    return data\n\n\ndef permute_edges(data):\n\n    node_num, _ = data.x.size()\n    _, edge_num = data.edge_index.size()\n    permute_num = int(edge_num / 10)\n\n    edge_index = data.edge_index.transpose(0, 1).numpy()\n\n    idx_add = np.random.choice(node_num, (permute_num, 2))\n  \n    edge_index = edge_index[np.random.choice(edge_num, edge_num-permute_num, replace=False)]\n\n    data.edge_index = torch.tensor(edge_index).transpose_(0, 1)\n\n    return data\n\ndef subgraph(data):\n\n    node_num, _ = data.x.size()\n    _, edge_num = data.edge_index.size()\n    sub_num = int(node_num * 0.2)\n\n    edge_index = data.edge_index.numpy()\n\n    idx_sub = [np.random.randint(node_num, size=1)[0]]\n    idx_neigh = set([n for n in edge_index[1][edge_index[0]==idx_sub[0]]])\n\n    count = 0\n    while len(idx_sub) <= sub_num:\n        count = count + 1\n        if count > node_num:\n            break\n        if len(idx_neigh) == 0:\n            break\n        sample_node = np.random.choice(list(idx_neigh))\n        if sample_node in idx_sub:\n            continue\n        idx_sub.append(sample_node)\n        idx_neigh.union(set([n for n in edge_index[1][edge_index[0]==idx_sub[-1]]]))\n\n    idx_drop = [n for n in range(node_num) if not n in idx_sub]\n    idx_nondrop = idx_sub\n    idx_dict = {idx_nondrop[n]:n for n in list(range(len(idx_nondrop)))}\n\n    # data.x = data.x[idx_nondrop]\n    edge_index = data.edge_index.numpy()\n\n    adj = torch.zeros((node_num, node_num))\n    adj[edge_index[0], edge_index[1]] = 1\n    adj[idx_drop, :] = 0\n    adj[:, idx_drop] = 0\n    edge_index = adj.nonzero().t()\n\n    data.edge_index = edge_index\n\n    return data\n\n\ndef mask_nodes(data):\n\n    node_num, feat_dim = data.x.size()\n    mask_num = int(node_num / 10)\n\n    idx_mask = np.random.choice(node_num, mask_num, replace=False)\n    data.x[idx_mask] = torch.tensor(np.random.normal(loc=0.5, scale=0.5, size=(mask_num, feat_dim)), dtype=torch.float32)\n\n    return data\n\nif __name__ == '__main__':\n\n    pyg_dataset = PygGraphPropPredDataset(name = 'ogbg-code2')\n    print(pyg_dataset.num_classes)\n    split_index = pyg_dataset.get_idx_split()\n    ","repo_name":"BUPT-GAMMA/AAGOD","sub_path":"dataset_pyg_aug.py","file_name":"dataset_pyg_aug.py","file_ext":"py","file_size_in_byte":12769,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"36971146836","text":"from glob import glob\nimport pandas as pd\n\nfolders = glob('../data/dataset/train/*')\n\nX = []\ny = []\n\nfor folder in folders:\n\timages = glob(folder+'/*.jpg')\n\tX += images\n\n\tname = folder.split('/')[-1]\n\tprint(name)\n\ty += [name for x in images]\n\n\nprint (len(X))\nprint (len(y))\n\ndf = pd.DataFrame()\ndf['path'] = pd.Series(X)\ndf['label'] = pd.Series(y)\ndf.to_csv('../data/csv/all.csv',index=False)\n","repo_name":"kennethrithvik/IET-Shopee_hackathon","sub_path":"balu/1_get_all_paths.py","file_name":"1_get_all_paths.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"19936563133","text":"import random\n\nREWARD = -0.01\nDISCOUNT = 0.99\nMAX_ERROR = 10**(-3)\nACTIONS = [(-1,0),(0,1),(1,0),(0,-1)]\nCOMPASS = {0: \"UP\", 1: \"RIGHT\", 2: \"DOWN\", 3: \"LEFT\"}\nDIRECTIONS = 4\nNUM_ROWS = 3\nNUM_COLS = 4\nV = [[0, 0, 0, 1], [0, 0, 0, -1], [0, 0, 0, 0]]\n\npolicy = [[random.randint(0,3) for _ in range(NUM_COLS)] for __ in range(NUM_ROWS)]\n\ndef GetV(V, r, c, action):\n    dr, dc = ACTIONS[action]\n    r += dr\n    c += dc\n    if r<0 or c<0 or r>=NUM_ROWS or c>=NUM_COLS or (r==c==1):\n        return V[r-dr][c-dc]\n    return V[r][c]\n\ndef CalculateV(V, r, c, action):\n    v = REWARD\n    v += 0.1 * DISCOUNT * GetV(V, r, c, (action-1)%4)\n    v += 0.8 * DISCOUNT * GetV(V, r, c, action)\n    v += 0.1 * DISCOUNT * GetV(V, r, c, (action+1)%4)\n    return v\n\ndef PolicyEvaluation(policy, V):\n    while True:\n        VDash = [[0, 0, 0, 1], [0, 0, 0, -1], [0, 0, 0, 0]]\n        err = 0\n        for r in range(NUM_ROWS):\n            for c in range(NUM_COLS):\n                if r==c==1 or (r<=1 and c==3):\n                    continue\n                VDash[r][c] = round(CalculateV(V, r, c, policy[r][c]), 3)\n                err = max(err, VDash[r][c] - V[r][c])\n        V = VDash\n        if err < MAX_ERROR * (1-DISCOUNT) / DISCOUNT:\n            break\n    return V\n\ndef PolicyIteration(policy, V):\n    print(\"\\nDuring Updating : \")\n    latestV = V\n    while True:\n        V = PolicyEvaluation(policy, V)\n        unchanged = True\n        for r in range(NUM_ROWS):\n            for c in range(NUM_COLS):\n                if r==c==1 or (r<=1 and c==3):\n                    continue\n                maxAction, maxV = None, -float(\"inf\")\n                for action in range(DIRECTIONS):\n                    v = CalculateV(V, r, c, action)\n                    if v > maxV:\n                        maxAction, maxV = action, v\n                if maxV > CalculateV(V, r, c, policy[r][c]):\n                    policy[r][c] = maxAction\n                    unchanged = False\n        if unchanged:\n            break\n        print()\n        PrintEnvironment(V)\n        latestV = V\n    return latestV\n\n\ndef PrintEnvironment(V, policy=False):\n    for r in range(NUM_ROWS):\n        for c in range(NUM_COLS):\n            if r == 1 and c == 1:\n                print(\"WALL\".ljust(6), end=\" | \")\n            elif c == 3:\n                if r <= 1:\n                    if r == 0:\n                        print(\"+1\")\n                    else:\n                        print(\"-1\")\n                else:\n                    if not policy:\n                        print(str(V[r][c]).ljust(6))\n                    else:\n                        nextDirection = \"RIGHT\"\n                        m = -float(\"inf\")\n                        for i in range(DIRECTIONS):\n                            dr, dc = ACTIONS[i]\n                            newR = r + dr\n                            newC = c + dc\n                            if newR<0 or newC<0 or newR>=NUM_ROWS or newC>=NUM_COLS:\n                                continue\n                            if V[newR][newC] >= m:\n                                m = V[newR][newC]\n                                nextDirection = COMPASS[i]\n                        print(nextDirection.ljust(6))\n            else:\n                if not policy:\n                    print(str(V[r][c]).ljust(6), end=\" | \")\n                else:\n                    nextDirection = \"RIGHT\"\n                    m = -float(\"inf\")\n                    for i in range(DIRECTIONS):\n                        dr, dc = ACTIONS[i]\n                        newR = r + dr\n                        newC = c + dc\n                        if newR<0 or newC<0 or newR>=NUM_ROWS or newC>=NUM_COLS:\n                            continue\n                        if V[newR][newC] >= m:\n                            m = V[newR][newC]\n                            nextDirection = COMPASS[i]\n                    print(nextDirection.ljust(6), end=\" | \")\n\n\nprint(\"The initial random policy is:\\n\")\nPrintEnvironment(policy)\nV = PolicyIteration(policy, V)\nprint(\"\\nThe optimal policy is:\\n\")\nPrintEnvironment(V, True)\n","repo_name":"Kirandeep2806/AI-Algorithms","sub_path":"PolicyIteration.py","file_name":"PolicyIteration.py","file_ext":"py","file_size_in_byte":4055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"863193451","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 17 09:22:36 2017\n\n@author: Anirudh\n\"\"\"\nfrom datetime import datetime\nimport argparse\nimport base64\nimport cv2\nimport shutil\nimport os\nimport numpy as np\nimport socketio\nimport eventlet\nimport eventlet.wsgi\nfrom PIL import Image\nfrom flask import Flask\nfrom io import BytesIO\nfrom keras.models import load_model\n\nsio = socketio.Server()\napp = Flask(__name__)\nmodel = None\nprev_image_array = None\n\ndef preprocess_image(img):\n\n    new_img = img[50:140,:,:]\n\n    new_img = cv2.GaussianBlur(new_img, (3,3), 0)\n\n    new_img = cv2.resize(new_img,(200, 66), interpolation = cv2.INTER_AREA)\n\n    new_img = cv2.cvtColor(new_img, cv2.COLOR_RGB2YUV)\n    return new_img\n\n@sio.on('telemetry')\ndef telemetry(sid, data):\n    # The current steering angle of the car\n    steering_angle = data[\"steering_angle\"]\n    # The current throttle of the car\n    throttle = data[\"throttle\"]\n    # The current speed of the car\n    speed = data[\"speed\"]\n    # The current image from the center camera of the car\n    imgString = data[\"image\"]\n    image = Image.open(BytesIO(base64.b64decode(imgString)))\n    image_array = np.asarray(image)\n    img = preprocess_image(image_array)\n    transformed_image_array = img[None, :, :, :]\n    # This model currently assumes that the features of the model are just the images. Feel free to change this.\n    steering_angle = float(model.predict(transformed_image_array, batch_size=1))\n    # The driving model currently just outputs a constant throttle. Feel free to edit this.\n    throttle = 0.2\n    if float(speed) < 10:\n        throttle = 1\n    send_control(steering_angle, throttle)\n    \n    if args.image_folder != '':\n            timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]\n            image_filename = os.path.join(args.image_folder, timestamp)\n            image.save('{}.jpg'.format(image_filename))\n\n@sio.on('connect')\ndef connect(sid, environ):\n    print(\"connect \", sid)\n    send_control(0, 0)\n\n\ndef send_control(steering_angle, throttle):\n    sio.emit(\"steer\", data={\n    'steering_angle': steering_angle.__str__(),\n    'throttle': throttle.__str__()\n    }, skip_sid=True)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Remote Driving')\n    parser.add_argument('model', type=str,\n    help='Path to model definition json. Model weights should be on the same path.')\n    parser.add_argument(\n        'image_folder',\n        type=str,\n        nargs='?',\n        default='',\n        help='Path to image folder. This is where the images from the run will be saved.'\n    )\n    args = parser.parse_args()\n\n\n    model = load_model(args.model)\n    model.compile(\"adam\", \"mse\")\n\n    \n    if args.image_folder != '':\n        print(\"Creating image folder at {}\".format(args.image_folder))\n        if not os.path.exists(args.image_folder):\n            os.makedirs(args.image_folder)\n        else:\n            shutil.rmtree(args.image_folder)\n            os.makedirs(args.image_folder)\n        print(\"RECORDING THIS RUN ...\")\n    else:\n        print(\"NOT RECORDING THIS RUN ...\")\n    # wrap Flask application with engineio's middleware\n    app = socketio.Middleware(sio, app)\n\n    # deploy as an eventlet WSGI server\n    eventlet.wsgi.server(eventlet.listen(('', 4567)), app)","repo_name":"Anirudhav93/sdProject3","sub_path":"drive.py","file_name":"drive.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38628633427","text":"\n# Collect historical data\n# This will first collect all players in the NBA with their years played.\n# Then, it will look at what players are eligible to be scraped (if they fall within the date range specified)\n# Then, for each of those players, we will add in each game they've played.\n\n\nimport requests\nimport os\nimport string\nimport sqlite3\nfrom BeautifulSoup import BeautifulSoup\nimport data_dump\nimport pandas as pd \n\n\nif __name__=='__main__':\n    database = data_dump.data_container()\n    \n    query='SELECT * FROM players_stats'\n    stats = database.run_query(query)\n    \n    query='SELECT * FROM players_history'\n    history = database.run_query(query)\n    history = history[['player_name','home_link','birth_place','shoots','highschool']]\n    \n    query='SELECT * FROM players_list'\n    homelink = database.run_query(query)\n    new_columns=homelink.columns.values\n    new_columns[0]='player_name'\n    homelink.columns=new_columns\n    \n    \n    dataset=pd.merge(\n                     stats,\n                     history,\n                     how='left',\n                     on=['player_name','home_link']\n                     )\n    \n    dataset=pd.merge(dataset,\n                     homelink,\n                     how='left',\n                     on=['player_name','home_link']\n                     )\n    \n    dataset.to_csv('sample.csv',index=False)\n    database.close_connection()\n    ","repo_name":"jsaperas/fanduel_project","sub_path":"data_pull_scripts/pull_sample_data.py","file_name":"pull_sample_data.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41964201147","text":"import logging, pymysql, traceback\nfrom app import app\nfrom config import mysql\nfrom flask import jsonify\nfrom flask import request\nfrom case_crawler import get_tribunals_court_cases_data\nfrom crawler import get_tribunals_detail_data\nfrom courtClassCaseList import TribunalsCaseListInsert\nfrom courtClassForDetailData import TribunalsInsert\n\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s:%(levelname)s:%(name)s:%(message)s')\nfile_handler = logging.FileHandler('tribunals.log')\nfile_handler.setFormatter(formatter)\nlogger.addHandler(file_handler)\n\n\n@app.route('/tribunals/caselist', methods=['POST'])\ndef tribunals_caselist():\n    try:\n        _json = request.json\n        _search_by = _json['search_by']\n        _location = _json['location']\n        _case_type = _json.get('case_type', None)\n        _case_status = _json.get('case_status', None)\n        _party_type = _json.get('party_type', None)\n        _party_name = _json.get('party_name', None)\n        _filing_no = _json.get('filing_no', None)\n        _case_number = _json.get('case_number', None)\n        _advocate_name = _json.get('advocate_name', None)\n        _case_year = _json['case_year']\n\n        input_data = {\n            'search_by': _search_by,\n            'location': _location,\n            'case_type': _case_type,\n            'case_status': _case_status,\n            'party_type': _party_type,\n            'party_name': _party_name,\n            'filing_no': _filing_no,\n            'case_number': _case_number,\n            'advocate_name': _advocate_name,\n            'case_year': _case_year\n        }\n        case_list_data = get_tribunals_court_cases_data(input_data)\n        if case_list_data != []:\n            logger.info('we have got case_list_data successfully')\n            \n        court_type_id = 8\n        if case_list_data != []:\n            if request.method == 'POST':\n                TribunalsCaseListInsert(court_type_id, _search_by, _location, _advocate_name, _party_name, _party_type, _case_type, _case_number, _case_year, _case_status, case_list_data).createTribunalsCaseList()\n\n                respone = jsonify({'meaasge': 'Fetched NCLAT Tribunals Case List succesfully!'})\n                respone.status_code = 200\n                return respone\n            else:\n                return showMessage()\n        else:\n            return dataNotFound()\n\n    except:\n        logger.info(f\"Error in tribunals_caselist :- {traceback.format_exc()}\")\n\n\n@app.route('/tribunals/detaildata', methods=['POST'])\ndef create_employee():\n    try:\n        _json = request.json\n        _location = _json['location']\n        _filing_no = _json['filing_no']\n        _status = 1\n        court_type_id = 8\n\n        input_data = {\n            'location': _location,\n            'filing_no': _filing_no\n        }\n\n        if request.method == 'POST':\n            conn = mysql.connect()\n            cursor = conn.cursor(pymysql.cursors.DictCursor)\n            sqlqueryselect = f\"SELECT `token`,`xsrf_token`,`laravel_session` FROM `case_list` where `location`='{_location}' and `filing_no`={_filing_no}\"\n            cursor.execute(sqlqueryselect)\n            security_tokens = cursor.fetchone()\n            _token = security_tokens.get('token')\n            _xsrf_token = security_tokens.get('xsrf_token')\n            _laravel_session = security_tokens.get('laravel_session')\n            conn.commit()\n            tribunals_data = get_tribunals_detail_data(input_data, _token, _xsrf_token, _laravel_session)\n            if tribunals_data != None:\n                getlastid = TribunalsInsert(court_type_id, _status, _location, _token, _xsrf_token, _laravel_session, tribunals_data).createCaseDetails()\n            else:\n                return dataNotFound()\n\n            respone = jsonify(\n                {'case_id': getlastid, 'meaasge': 'Fetched NCLAT Tribunals data succesfully!'})\n\n            return respone\n        else:\n            return showMessage()\n\n    except:\n        logger.info(f\"Error in create_employee :- {traceback.format_exc()}\")\n\n\n@app.errorhandler(404)\ndef showMessage(error=None):\n    message = {\n        'status': 404,\n        'message': 'Record not found: ' + request.url,\n    }\n    respone = jsonify(message)\n    respone.status_code = 404\n    return respone\n\n\n@app.errorhandler(500)\ndef dataNotFound(error=None):\n    message = {\n        'status': 'Oops, data not found!'\n    }\n    respone = jsonify(message)\n    respone.status_code = 500\n    return respone\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=9000, debug=True)\n\n","repo_name":"Shubham100718/dockerize_python_app","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19049368899","text":"from flask import Flask, session\r\n# Server-side client to support larger data transfer\r\nfrom flask_session import Session\r\nfrom handlers import database\r\nfrom flask import (request,\r\n                   redirect,\r\n                   render_template,\r\n                   url_for,\r\n                   flash)\r\nfrom werkzeug.utils import secure_filename\r\nimport os\r\nfrom os.path import join, dirname, realpath\r\nfrom handlers.data_handling import file_options, saveData, CurrentData, processNewRecord, getRequestedData, \\\r\n    renderDashboard\r\nfrom handlers.graph_templates import *\r\nfrom handlers.models import *\r\nimport secrets\r\n\r\nUPLOAD_FOLDER = join(dirname(realpath(__file__)), 'static/temp_files')\r\nbasedir = os.path.abspath(os.path.dirname(__file__))\r\nALLOWED_EXTENSIONS = ['csv', 'xlsx']\r\n\r\napp = Flask(__name__)\r\n\r\napp.secret_key = secrets.token_bytes(16)\r\n\r\napp.config['SESSION_TYPE'] = 'filesystem'\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\n\r\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'database.db')\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\n\r\nSession(app)\r\ndatabase.init_app(app)\r\n\r\n\r\n@app.route('/create_db', methods=[\"GET\"])\r\ndef create_db():\r\n    createDB()\r\n    return f\"Database created\"\r\n\r\n\r\n@app.route('/', methods=[\"GET\", \"POST\"])\r\ndef login():\r\n    if request.method == \"POST\":\r\n        email = request.form.get(\"email\")\r\n        password = request.form.get(\"password\")\r\n        try:\r\n            # Validate password and retrieve the users user id\r\n            current_user = UserLogin.query.filter_by(user_email=email).all()[0]\r\n            logged_pwd = current_user.user_pwd\r\n            user_id = current_user.user_id\r\n            if password != logged_pwd:\r\n                message = \"Incorrect password.\"\r\n                return render_template('login.html', message=message)\r\n\r\n            # Capture users credentials in session\r\n            session[\"account\"] = {\"email\": email, \"user_id\": user_id}\r\n            return redirect('home')\r\n\r\n        except Exception as e:\r\n            message = \"Account not found.\"\r\n            return render_template('login.html', message=message)\r\n\r\n    return render_template('login.html')\r\n\r\n\r\n@app.route('/register', methods=[\"GET\", \"POST\"])\r\ndef register():\r\n    if request.method == \"POST\":\r\n        email = request.form.get(\"user-email\")\r\n        pwd = request.form.get(\"user-pwd\")\r\n\r\n        # User registration checks\r\n        if \"@\" not in email:\r\n            message = \"Not a valid email.\"\r\n            return render_template('register.html', message=message)\r\n\r\n        if len(pwd) < 8:\r\n            message = \"Password must be at least 8 characters.\"\r\n            return render_template('register.html', message=message)\r\n\r\n        registerUser(email=email, pwd=pwd)\r\n        return redirect('/')\r\n\r\n    return render_template('register.html')\r\n\r\n\r\n@app.route('/home', methods=[\"GET\", \"POST\"])\r\ndef home():\r\n    user_info = session.get(\"account\")\r\n    if request.method == \"POST\":\r\n        action = request.form.get(\"project_button\")\r\n\r\n        if action.startswith(\"delete_\"):\r\n            table_name = action.strip(\"delete_\")\r\n            deleteTable(user_id=user_info[\"user_id\"], table_name=table_name)\r\n            print(f'Deleted project: {table_name}')\r\n            return redirect('/home')\r\n\r\n        if action == \"Add record\":\r\n            print(\"Adding record\")\r\n            table_name = request.form.get(\"table-selection\")\r\n            processNewRecord(request, user_id=user_info[\"user_id\"], table_name=table_name)\r\n\r\n            return redirect('/home')\r\n\r\n        # Show selected dashboard\r\n        table_name = action\r\n        json_obj, col_mapping = getRequestedData(user_id=user_info[\"user_id\"], table_name=table_name)\r\n\r\n        session[\"preview_dataframe\"] = {\"dataframe\": pd.read_json(json_obj),\r\n                                        \"dashboard_config\": {\"project_name\": table_name,\r\n                                                             \"fields\": col_mapping,\r\n                                                             }}\r\n        # data[\"dataframe\"] = pd.read_json(json_obj)\r\n        # data[\"dashboard_config\"] = {\"project_name\": table_name,\r\n        #                             \"fields\": col_mapping,\r\n        #                             }\r\n        return redirect(url_for('active_dashboard', date_range='all-time'))\r\n\r\n    user_datasets = UserTables.query.filter_by(user_id=user_info[\"user_id\"]).all()\r\n    if len(user_datasets) > 0:\r\n        user_tables = [[table.table_name, table.last_updated] for table in user_datasets]\r\n        return render_template('home.html', user=user_info, projects=user_tables)\r\n    return render_template('home.html', user=user_info, projects=None)\r\n\r\n\r\n@app.route('/data-import', methods=[\"GET\", \"POST\"])\r\ndef data_import():\r\n    user_info = session.get(\"account\")\r\n    if request.method == \"POST\":\r\n        file = request.files[\"file\"]\r\n\r\n        ext = file.filename.split('.')[-1].lower()\r\n        if ext not in ALLOWED_EXTENSIONS:\r\n            return render_template('import_data.html', message=\"File type not supported.\")\r\n\r\n        filename = secure_filename(file.filename)\r\n        save_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\r\n        file.save(save_path)\r\n\r\n        if ext == 'csv':\r\n            dataframe, fields = file_options.load_csv(save_path)\r\n            session[\"preview_dataframe\"] = {\"dataframe\": dataframe,\r\n                                            \"fields\": fields}\r\n        if ext == 'xlsx':\r\n            dataframe, fields = file_options.load_excel(save_path)\r\n            session[\"preview_dataframe\"] = {\"dataframe\": dataframe,\r\n                                            \"fields\": fields}\r\n        return redirect('/preview-data')\r\n    return render_template('import_data.html', user=user_info)\r\n\r\n\r\n@app.route('/preview-data', methods=[\"GET\", \"POST\"])\r\ndef preview_data():\r\n    username = session.get(\"account\")\r\n    messages = None\r\n    if request.method == \"POST\":\r\n        title = request.form.get(\"data-title\")\r\n        date_col = request.form.get(\"date-column\")\r\n        sales_col = request.form.get(\"sales-column\")\r\n        costs_col = request.form.get(\"costs-column\")\r\n        labor_col = request.form.get(\"labor-column\")\r\n        materials_col = request.form.get(\"materials-column\")\r\n        data = session.get(\"preview_dataframe\")\r\n        data[\"dashboard_config\"] = {\"project_name\": title,\r\n                                    \"fields\": {\"date_field\": date_col,\r\n                                               \"sales_field\": sales_col,\r\n                                               \"costs_field\": costs_col,\r\n                                               \"labor_field\": labor_col,\r\n                                               \"materials_field\": materials_col,\r\n                                               },\r\n                                    }\r\n\r\n        action = request.form[\"submit-btn\"]\r\n        if action == \"Save & Analyze\":\r\n            try:\r\n                table_name = saveData(session, overwrite=False)\r\n                json_obj, col_mapping = getRequestedData(user_id=username[\"user_id\"], table_name=table_name)\r\n                data = session[\"preview_dataframe\"]\r\n                data[\"dataframe\"] = pd.read_json(json_obj)\r\n                data[\"dashboard_config\"] = {\"project_name\": table_name,\r\n                                            \"fields\": col_mapping,\r\n                                            }\r\n                return redirect(\"/active-dashboard\")\r\n            except Exception as e:\r\n                message = \"Please follow upload instructions. \" \\\r\n                          \"If the issue persists, report the following error to dev support:\"\r\n                messages = [message, e]\r\n        else:\r\n            try:\r\n                saveData(session)\r\n                return redirect('/home')\r\n            except Exception as e:\r\n                message = \"Please follow upload instructions. \" \\\r\n                          \"If the issue persists, report the following error to dev support:\"\r\n                messages = [message, e]\r\n    data = session.get(\"preview_dataframe\")\r\n    preview_dataframe = data[\"dataframe\"]\r\n    table = create_table(preview_dataframe.loc[:100])\r\n    return render_template('preview_data.html', data=data, table=table, user=username, messages=messages)\r\n\r\n\r\n@app.route('/active-dashboard/<date_range>', methods=[\"GET\", \"POST\"])\r\ndef active_dashboard(date_range=None):\r\n    user = session.get(\"account\")\r\n\r\n    if request.method == \"POST\":\r\n        action = request.form.get(\"dashboard-action\")\r\n        if action.startswith(\"add_record_\"):\r\n            table_name = action.split(\"add_record_\")[1]\r\n            processNewRecord(request, user_id=user[\"user_id\"], table_name=table_name)\r\n\r\n            json_obj, col_mapping = getRequestedData(user_id=user[\"user_id\"], table_name=table_name)\r\n\r\n            data = session[\"preview_dataframe\"]\r\n            data[\"dataframe\"] = pd.read_json(json_obj)\r\n            data[\"dashboard_config\"] = {\"project_name\": table_name,\r\n                                        \"fields\": col_mapping,\r\n                                        }\r\n            return redirect(url_for('active_dashboard', date_range='all-time'))\r\n\r\n    data = session.get(\"preview_dataframe\")\r\n\r\n    try:\r\n        session[\"dashboard_objects\"] = renderDashboard(data, date_range=date_range)\r\n    except ValueError as e:\r\n        print(str(e))\r\n        flash('No data available for requested date range.')\r\n        session[\"dashboard_objects\"] = renderDashboard(data, date_range=\"All-time\")\r\n    visual_objects = session.get(\"dashboard_objects\")\r\n\r\n    return render_template('active_dashboard.html', data=data, user=user, objects=visual_objects)\r\n\r\n\r\n@app.route('/active-dashboard/settings-applied', methods=[\"POST\"])\r\ndef dashboard_settings():\r\n    if request.method == \"POST\":\r\n        date_range = request.form.get('date-range')\r\n        print(date_range)\r\n        if date_range == 'custom':\r\n            try:\r\n                start_date = request.form.get('selected-start-date')\r\n                end_date = request.form.get('selected-end-date')\r\n                session[\"active-dashboard\"] = {\"filters\": {\"start date\": start_date,\r\n                                                           \"end date\": end_date}}\r\n                print(f'Start: {start_date}\\nEnd: {end_date}')\r\n            except Exception as e:\r\n                print(e)\r\n\r\n    return redirect(url_for('active_dashboard', date_range=date_range))\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run()\r\n","repo_name":"JRose31/ABADashboards","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22029181377","text":"from sklearn.model_selection import cross_val_score\nimport numpy as np\nfrom util.string import print_primary, print_secondary\nnp.random.seed(123)\n\n\ndef cross_validation(model_func, x_train, y_train, cv_folds, k=None,\n                     results=None, scoring='explained_variance', v=1):\n    # scoring: neg_mean_squared_error explained_variance\n    print_secondary('\\t scoring: %s' % scoring)\n    scores = cross_val_score(\n        model_func, x_train, y_train, cv=cv_folds, scoring=scoring)\n    if results is not None:\n        results[k] = scores\n    if v:\n        print('scores per fold ', [round(score, 4) for score in scores])\n        print('  mean score    ', np.mean(scores))\n        print('  standard dev. ', np.std(scores))\n\n\ndef scores_table(results):\n    # render latex table\n    print('Model & Mean & Std. dev. \\\\\\\\ \\n\\\\hline')\n    best_k = ''\n    best_mean = -np.inf\n    for k, scores_acc in results.items():\n        if np.mean(scores_acc) > best_mean:\n            best_mean = np.mean(scores_acc)\n            best_k = k\n        print('%s & %0.4f & %0.4f\\\\\\\\' %\n              (k, np.mean(scores_acc), np.std(scores_acc)))\n    print_primary('\\nbest score: %s, with mean: %0.4f' % (best_k, best_mean))\n    return best_k\n","repo_name":"voschezang/Data-Mining","sub_path":"util/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72046088248","text":"def exchangelist(list):\r\n\tfh=[]\r\n\tsh=[]\r\n\tmid=len(list)//2\r\n\tln=len(list)\r\n\tfor i in list:\r\n\t\tif i in range(0,mid):\r\n\t\t\tfh.append(list[i])\r\n\t\tif i in range(mid,ln):\r\n\t\t\tsh.append(list[i])\r\n\tshr=sh[::-1]\r\n\tres=shr+fh\r\n\treturn res\r\nlist=[1,2,3,4,5,6,7]\r\nprint(exchangelist(list))","repo_name":"vamshi99k/python-programs","sub_path":"exchangelist - Copy.py","file_name":"exchangelist - Copy.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28041398632","text":"from src.si.util.util import euclidean, accuracy_score\nfrom .model import Model\nimport numpy as np\n\nclass KNN(Model):\n\n    def __init__(self, n_neighbors, classification=True):\n        \"\"\"\n                Abstract class defining an interface for supervised learning models.\n                \"\"\"\n        super(KNN).__init__()\n        self.n_neighbors = n_neighbors\n        self.is_fitted = False\n        self.classification = classification\n\n    def fit(self, dataset):\n        self.dataset = dataset\n        self.is_fitted = True\n\n    def get_neighbors(self, x):\n        dist = euclidean(x, self.dataset.X)\n        ord_idx = np.argsort(dist)\n        return ord_idx[:self.n_neighbors]\n\n    def predict(self, x):\n        assert self.is_fitted, \"Model must be fitted before prediction\"\n        neighbors = self.get_neighbors(x)\n        values = self.dataset.Y[neighbors].tolist()\n\n        #prediction = max(set(values), key=values.count)  # o maximo que aparece, ou seja o que aparece mais vezes\n\n        #Esta implementaçao nao e mesmo necessaria e amis para funcuionar com regressao tambem\n        if self.classification:\n            prediction = max(set(values), key=values.count) #o maximo que aparece, ou seja o que aparece mais vezes, com labels\n\n        else:\n            prediction = sum(values)/len(values) #para funcionar com regressao valores\n\n        return prediction\n\n\n    def cost(self):\n        y_pred = np.ma.apply_along_axis(self.predict, #o ma aqui indica que e com mascara\n                                        axis=0, arr=self.dataset.X.T)\n        return accuracy_score(self.dataset.Y, y_pred)\n\n\n","repo_name":"Monicasfe/SIB","sub_path":"src/si/supervised/KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30251871094","text":"import os, sys, glob, time\r\n\r\n#usage: python3 /home/hj43453/01.py_scripts/.....py\r\n#this program makes gff.bed of the whole genes (maxlen single mRNA,n=11868)\r\n#input: 'Obir_maxlen_isoform_geneID_11868.txt','chr_Obir_v5.4_genomic.gff.temp'\r\n#output:chr_Obir_v5.4_genomic11868.gff.bed \r\n\r\n'''\r\ngene   =================  #confirmed that gene and mRNA are equal.\r\nexon   ===   ===    ====\r\nCDS      =   ===    ==     UTR = exon - CDS\r\nintron    ===   ====       intron = gene - exon\r\n''' \r\n#######################################\r\n\r\ndef read_rnaID(name):\r\n\r\n    rnaIDlist = []\r\n    infile = open(name,'r')\r\n    for i in infile.readlines():\r\n        rnaIDlist.append(i.rstrip().split('\\t')[1]) #0 = rnaID, 1 = geneID\r\n    return rnaIDlist \r\n\r\n\r\ndef readgffbed(name,geneIDlist):\r\n\r\n    outfile = open('chr_Obir_v5.4_genomic11868.gff.bed','w')\r\n\r\n\r\n    n = 0\r\n    infile = open(name,'r')\r\n    for i in infile.readlines():\r\n          \r\n        spl = i.rstrip().split('\\t')\r\n        sID = spl[3]   #Chr1 Gnomon\tgene\t1070\t7288   +        \r\n\r\n        if sID in geneIDlist:\r\n            print(i.rstrip(),file=outfile)\r\n\r\n    outfile.close()\r\n\r\n\r\n######## MAIN #########################################################\r\n\r\n\r\ngeneIDlist  = read_rnaID('Obir_maxlen_isoform_geneID_11868.txt')\r\nreadgffbed('chr_Obir_v5.4_genomic.gff.temp',geneIDlist)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"schmitzlab/Methylome-of-clonal-ant_Obir-v5.4","sub_path":"extract_introns/2_mk_whole_gene_gffbed_singlemRNA_070821_v3.py","file_name":"2_mk_whole_gene_gffbed_singlemRNA_070821_v3.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1994634286","text":"import os\nimport unittest\nfrom unittest.mock import MagicMock\n\nimport torch\nimport torch.distributed as torch_dist\nimport torch.nn as nn\nfrom torch.optim import SGD\n\nfrom mmengine.dist import all_gather, broadcast\nfrom mmengine.model import (BaseDataPreprocessor, BaseModel,\n                            ExponentialMovingAverage,\n                            MMDistributedDataParallel,\n                            MMSeparateDistributedDataParallel)\nfrom mmengine.optim import AmpOptimWrapper, OptimWrapper, OptimWrapperDict\nfrom mmengine.testing import assert_allclose\nfrom mmengine.testing._internal import MultiProcessTestCase\nfrom mmengine.utils.dl_utils import TORCH_VERSION\nfrom mmengine.utils.version_utils import digit_version\n\nif digit_version(TORCH_VERSION) >= digit_version('2.0.0'):\n    from mmengine.model import MMFullyShardedDataParallel  # noqa: F401\n\n\nclass ToyDataPreprocessor(BaseDataPreprocessor):\n\n    def forward(self, data: dict, training: bool = False):\n        self.called = True\n        return super().forward(data, training)\n\n\nclass ToyModel(BaseModel):\n\n    def __init__(self):\n        super().__init__(data_preprocessor=ToyDataPreprocessor())\n        self.conv1 = nn.Conv2d(3, 1, 1)\n        self.conv2 = nn.Conv2d(1, 1, 1)\n\n    def forward(self, inputs, data_sample=None, mode='tensor'):\n        x = self.conv1(inputs)\n        x = self.conv2(x)\n        if mode == 'loss':\n            return dict(loss=x)\n        elif mode == 'predict':\n            return x\n        else:\n            return x\n\n\nclass ComplexModel(BaseModel):\n\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(3, 1, 1)\n        self.conv2 = nn.Conv2d(3, 1, 1)\n\n    def train_step(self, data, optim_wrapper):\n        inputs = self.data_preprocessor(data)['inputs']\n        loss1 = self.conv1(inputs)\n        optim_wrapper['optim_wrapper1'].update_params(loss1)\n        loss2 = self.conv2(inputs)\n        optim_wrapper['optim_wrapper2'].update_params(loss2)\n        return dict(loss1=loss1, loss2=loss2)\n\n    def val_step(self, data):\n        return 1\n\n    def test_step(self, data):\n        return 2\n\n    def forward(self):\n        pass\n\n\nclass TestDistributedDataParallel(MultiProcessTestCase):\n\n    def setUp(self):\n        super().setUp()\n        self._spawn_processes()\n\n    @unittest.skipIf(\n        not torch.cuda.is_available(), reason='cuda should be available')\n    def test_train_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        # Mixed precision training and gradient asynchronous should be valid at\n        # the same time\n        model = ToyModel().cuda()\n        ddp_model = MMDistributedDataParallel(module=model)\n        optimizer = SGD(ddp_model.parameters(), lr=0)\n        optim_wrapper = AmpOptimWrapper(\n            optimizer=optimizer, accumulative_counts=3)\n        inputs = torch.randn(1, 3, 1, 1).cuda() * self.rank * 255\n        data = dict(inputs=inputs, data_sample=None)\n        res = ddp_model.train_step(data, optim_wrapper=optim_wrapper)['loss']\n        self.assertIs(res.dtype, torch.float16)\n        grad = ddp_model.module.conv1.weight.grad\n        all_grads = all_gather(grad)\n        with self.assertRaises(AssertionError):\n            assert_allclose(all_grads[0], all_grads[1])\n\n        # Gradient accumulation\n        ddp_model.train_step(data, optim_wrapper=optim_wrapper)\n\n        # Test update params and clean grads.\n        ddp_model.train_step(data, optim_wrapper=optim_wrapper)\n        grad = ddp_model.module.conv1.weight.grad\n        if digit_version(torch.__version__) < digit_version('2.0.0'):\n            all_grads = all_gather(grad)\n            assert_allclose(all_grads[0], torch.zeros_like(all_grads[0]))\n            assert_allclose(all_grads[1], torch.zeros_like(all_grads[0]))\n        else:\n            self.assertIsNone(grad)\n\n        # Test enable detect_anomalous_params.\n        ddp_model = MMDistributedDataParallel(\n            module=model, detect_anomalous_params=True)\n        optimizer = SGD(ddp_model.parameters(), lr=0)\n        optim_wrapper = AmpOptimWrapper(\n            optimizer=optimizer, accumulative_counts=3)\n        inputs = torch.randn(1, 3, 1, 1).cuda() * self.rank * 255\n        data = dict(inputs=inputs, data_sample=None)\n        res = ddp_model.train_step(data, optim_wrapper=optim_wrapper)['loss']\n\n    def test_val_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ToyModel()\n        ddp_model = MMDistributedDataParallel(module=model)\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=None)\n        # Test get predictions.\n        predictions = ddp_model.val_step(data)\n        self.assertIsInstance(predictions, torch.Tensor)\n        self.assertTrue(model.data_preprocessor.called)\n\n    def test_test_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ToyModel()\n        ddp_model = MMDistributedDataParallel(module=model)\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=None)\n        predictions = ddp_model.test_step(data)\n        self.assertIsInstance(predictions, torch.Tensor)\n        self.assertTrue(model.data_preprocessor.called)\n\n    def _init_dist_env(self, rank, world_size):\n        \"\"\"Initialize the distributed environment.\"\"\"\n        os.environ['MASTER_ADDR'] = '127.0.0.1'\n        os.environ['MASTER_PORT'] = '29510'\n        os.environ['RANK'] = str(rank)\n        torch_dist.init_process_group(\n            backend='gloo', rank=rank, world_size=world_size)\n\n\n@unittest.skipIf(\n    not torch.cuda.is_available(), reason='cuda should be available')\nclass TestMMSeparateDistributedDataParallel(TestDistributedDataParallel):\n\n    def test_init(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ComplexModel()\n        model.ema = ExponentialMovingAverage(nn.Conv2d(1, 1, 1))\n        model.act = nn.ReLU()\n        ddp_model = MMSeparateDistributedDataParallel(model.cuda())\n        self.assertIsInstance(ddp_model.module.ema, ExponentialMovingAverage)\n        self.assertIsInstance(ddp_model.module.conv1,\n                              MMDistributedDataParallel)\n        self.assertIsInstance(ddp_model.module.act, nn.ReLU)\n\n    def test_train_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        # Test `optim_wrapper` is a dict. In this case,\n        # There will be two independently updated `DistributedDataParallel`\n        # submodules.\n        model = ComplexModel()\n        ddp_model = MMSeparateDistributedDataParallel(model.cuda())\n        optimizer1 = SGD(model.conv1.parameters(), lr=0.1)\n        optimizer2 = SGD(model.conv1.parameters(), lr=0.2)\n        optim_wrapper1 = OptimWrapper(optimizer1, 1)\n        optim_wrapper2 = OptimWrapper(optimizer2, 1)\n        optim_wrapper_dict = OptimWrapperDict(\n            optim_wrapper1=optim_wrapper1, optim_wrapper2=optim_wrapper2)\n        inputs = torch.randn(1, 3, 1, 1).cuda() * self.rank * 255\n        data = dict(inputs=inputs, data_sample=None)\n        # Automatically sync grads of `optim_wrapper1` since\n        # `cumulative_iters` = 1\n        ddp_model.train()\n        self.assertTrue(ddp_model.training)\n        ddp_model.train_step(data, optim_wrapper=optim_wrapper_dict)\n\n    def test_val_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ComplexModel()\n        ddp_model = MMSeparateDistributedDataParallel(model)\n        data = torch.randn(1, 3, 1, 1)\n        # Test get predictions.\n        ddp_model.eval()\n        self.assertFalse(ddp_model.training)\n        predictions = ddp_model.val_step(data)\n        self.assertEqual(predictions, 1)\n\n    def test_test_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ComplexModel()\n        ddp_model = MMSeparateDistributedDataParallel(model)\n        data = torch.randn(1, 3, 1, 1)\n        # Test get predictions.\n        ddp_model.eval()\n        self.assertFalse(ddp_model.training)\n        predictions = ddp_model.test_step(data)\n        self.assertEqual(predictions, 2)\n\n    def _init_dist_env(self, rank, world_size):\n        \"\"\"Initialize the distributed environment.\"\"\"\n        os.environ['MASTER_ADDR'] = '127.0.0.1'\n        os.environ['MASTER_PORT'] = '29515'\n        os.environ['RANK'] = str(rank)\n        torch_dist.init_process_group(\n            backend='gloo', rank=rank, world_size=world_size)\n\n\n@unittest.skipIf(\n    torch.cuda.device_count() < 2, reason='need 2 gpu to test fsdp')\n@unittest.skipIf(\n    digit_version(TORCH_VERSION) < digit_version('2.0.0'),\n    reason='fsdp needs Pytorch 2.0.0 or higher')\nclass TestMMFullyShardedDataParallel(MultiProcessTestCase):\n\n    def _init_dist_env(self, rank, world_size):\n        \"\"\"Initialize the distributed environment.\"\"\"\n        os.environ['MASTER_ADDR'] = '127.0.0.1'\n        os.environ['MASTER_PORT'] = '29520'\n        os.environ['RANK'] = str(rank)\n\n        num_gpus = torch.cuda.device_count()\n        torch.cuda.set_device(rank % num_gpus)\n        torch_dist.init_process_group(\n            backend='nccl', rank=rank, world_size=world_size)\n\n    def setUp(self) -> None:\n        super().setUp()\n        self._spawn_processes()\n\n    def test_train_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        # Test `optim_wrapper` is a instance of `OptimWrapper`\n        model = ToyModel()\n        fsdp_model = MMFullyShardedDataParallel(module=model.cuda())\n        optimizer = SGD(fsdp_model.parameters(), lr=0)\n        optim_wrapper = OptimWrapper(optimizer, accumulative_counts=1)\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=MagicMock())\n        fsdp_model.train()\n        self.assertTrue(fsdp_model.training)\n        fsdp_model.train_step(data, optim_wrapper=optim_wrapper)\n\n        # require_grad=False\n        model = ToyModel()\n        for _, param in model.state_dict().items():\n            broadcast(param)\n        model.conv1.requires_grad_(False)\n        ori_weight = model.conv1.weight.clone()\n\n        def wrap_policy(module, recurse=True, *args, **kwargs):\n            if recurse:\n                return True\n            return isinstance(module, nn.Conv2d)\n\n        fsdp_model = MMFullyShardedDataParallel(\n            module=model.cuda(), auto_wrap_policy=wrap_policy)\n        optimizer = SGD(fsdp_model.parameters(), lr=0.1)\n        optim_wrapper = OptimWrapper(optimizer, accumulative_counts=1)\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=MagicMock())\n        fsdp_model.train()\n        self.assertTrue(fsdp_model.training)\n        fsdp_model.train_step(data, optim_wrapper=optim_wrapper)\n\n        with fsdp_model.summon_full_params(fsdp_model):\n            updated_weight = fsdp_model.module.conv1.weight.cpu()\n            assert_allclose(ori_weight, updated_weight)\n\n    def test_val_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ToyModel()\n        fsdp_model = MMFullyShardedDataParallel(module=model.cuda())\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=MagicMock())\n        # Test get predictions.\n        predictions = fsdp_model.val_step(data)\n        self.assertIsInstance(predictions, torch.Tensor)\n\n    def test_test_step(self):\n        self._init_dist_env(self.rank, self.world_size)\n        model = ToyModel()\n        fsdp_model = MMFullyShardedDataParallel(module=model.cuda())\n        inputs = torch.randn(1, 3, 1, 1) * self.rank * 255\n        data = dict(inputs=inputs, data_sample=MagicMock())\n        predictions = fsdp_model.test_step(data)\n        self.assertIsInstance(predictions, torch.Tensor)\n","repo_name":"open-mmlab/mmengine","sub_path":"tests/test_model/test_wrappers/test_model_wrapper.py","file_name":"test_model_wrapper.py","file_ext":"py","file_size_in_byte":11803,"program_lang":"python","lang":"en","doc_type":"code","stars":853,"dataset":"github-code","pt":"77"}
+{"seq_id":"6480123199","text":"from __future__ import (absolute_import, division, print_function,\n                        unicode_literals)\n\nimport csv\nimport itertools\nimport os\nimport shutil\nimport sys\nimport json\nimport time\n\nimport scipy\nimport scipy.io.wavfile as wav\nimport numpy as np\n\nfrom raspy.progressTrackers import ProgressPrinter\n\nmaxFileSize = 2**23\n\n\ndef splitLargeFile(fileName, arr, args, callback=ProgressPrinter(), sampling_frequency=250000):\n    \"\"\" Split large files, checking for signals near the end. \"\"\"\n    bigFileData = arr\n    # fs, bigFileData = scipy.io.wavfile.read(fileName, True)\n    fileList = []\n    minFileLen = 5000\n    maxFileLen = maxFileSize // bigFileData.itemsize\n    lastl = 0\n    l = maxFileLen\n\n    with callback.context('partitioning file data', subtotal=len(bigFileData), uniform=True):\n        # subtract noise out and check thresholding for basic call avoidance\n        _, __, s = scipy.signal.spectrogram(\n            bigFileData[:l], fs=sampling_frequency, window='triang', nperseg=512,\n            noverlap=384, mode='psd'\n        )\n        noise = np.array([s.mean(1)]*20).T\n\n        del _, __, s\n\n        # keep breaking the file into chunks while there is still file left\n        i = 0\n        while lastl < len(bigFileData):\n            callback(count=lastl)\n            nsignal = 100\n\n            if l + minFileLen < len(bigFileData):\n                # inch the end of the chunk back until it's not on a call\n                while nsignal > 15:\n                    if l - lastl < minFileLen:\n                        l = lastl + maxFileLen // 2\n                        args['minSig'] *= 10.0**0.5\n                    else:\n                        l -= 1280\n                    _, __, s = scipy.signal.spectrogram(\n                        # bigFileData[l-(23*128):l], fs=250000, window='triang',\n                        bigFileData[l - (23 * 128):l], fs=sampling_frequency, window='triang',\n                        nperseg=512, noverlap=384, mode='psd'\n                    )\n                    nsignal = ((s-noise)>args['minSig']).sum()\n            else:\n                l = len(bigFileData)\n\n            # save this chunk as a file\n            path, fname = os.path.split(os.path.abspath(fileName))\n            newFileName = fileName[:-4] + '\\\\' + fname[:-4] + ' - ' + str(i) + '.wav'\n            try:\n                os.makedirs(path + '\\\\' + fname[:-4])\n            except WindowsError:\n                pass\n            wav.write(newFileName, sampling_frequency, bigFileData[lastl:l].copy())\n            fileList.append((bigFileData[lastl:l].copy(), lastl, newFileName))\n\n            # move to the next chunk\n            lastl = l\n            i += 1\n            l += maxFileSize//(2*bigFileData.itemsize) + 1280\n\n            callback(\"broke file into %d subfiles\" % (i,), count=len(bigFileData))\n\n    return fileList\n","repo_name":"Mide-RASP/Rat-Call-Classification-Tool","sub_path":"raspy/runRaspy.py","file_name":"runRaspy.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13109251673","text":"\nfrom google.appengine.api import users\nfrom google.appengine.ext import ndb\nimport webapp2\nimport jinja2\n\nenv = jinja2.Environment(loader=jinja2.FileSystemLoader(\"templates\"))\nenv2 = jinja2.Environment(loader=jinja2.FileSystemLoader(\"NewTopicTemplate\"))\n\ndef get_category_id(category_name):\n    if category_name=='politics':\n        n=100\n    elif category_name== 'sexism':\n        n=124\n    elif category_name=='racism':\n        n=300\n    elif category_name=='world hunger':\n        n=400\n    elif category_name=='police brutality':\n        n=500\n    elif category_name== 'gun laws':\n        n=600\n    elif category_name=='student loans':\n        n=700\n    elif category_name== 'sexual assault':\n        n=800\n    elif category_name=='homelessness':\n        n=900\n\n    return n\n        \n \nclass DTopic(ndb.Model):\n\t#Topic = ndb.StringProperty(required=True)\n\tCategory = ndb.StringProperty(required=True, default='Misc')\n\tComments = ndb.StringProperty(repeated=True)\n\tTheId= ndb.IntegerProperty()\n\n\nclass WebPageHandler(webapp2.RequestHandler):\n def get(self): \n    template = env.get_template('index.html')   \n    #template = jinja_environment.get_template('index.html')\n    self.response.out.write(template.render())\n\n\n\nclass MainHandler(webapp2.RequestHandler):  #Page user see's first asking them to login in \n    topics = {'politics': 'politics',\n          'sexism': 'sexism',\n          'racism': 'racism',\n          'world hunger': 'worldhunger',\n          'police brutality':'policebrutality',\n          'gun laws': 'gun',\n          'student loans': 'studentloans',\n          'sexual assault': 'sexualassault',\n          'homelessness': 'homelessness'}\n    \n    def get(self):      \n        user = users.get_current_user()\n        if user:  #if the user is logged in it will render the main webpage\n            data = {'topics': self.topics}\n            template = env.get_template('index.html') \n            self.response.out.write(template.render(data))\n        else:  #if the user is not logged in, it will ask them to login then redirect \n            template = env.get_template('mainindex.html') \n            login = users.create_login_url('/')\n            greeting = '<a href=\"%s\">Sign in or register</a>.' % login\n            data = {\"LogIn\" : greeting}\n            self.response.out.write(template.render(data))\n        # datatwo = {}\n        # self.response.write(template.render(datatwo))\n\n\n\nclass SetTopicHandler(webapp2.RequestHandler):\n    def get(self):\n        templates=env2.get_template('newTopic.html')  #render's the form for creating new topics\n        self.response.out.write(templates.render())\n\n    def post(self):\n    \t#self.response.out.write(\"submitted\")\n    \t##results_template = env2.get_template('Talk.html')\n        #self.response.out.write(results_template.render(top))\n        n=get_category_id(self.request.get(\"Category\"))\n        string1 = self.request.get(\"SideOne\")\n        string2 = self.request.get(\"SideTwo\")\n        t = DTopic(\n        \tTheId=n,\n        \tTopic=(string1 + \" vs \"+ string2),\n        \tCategory =self.request.get(\"Category\"),\n        \tComments =[self.request.get(\"FirstComment\")])\t\n\n        t.put()\n        self.redirect('/listtopic')\n\n\n\n\nclass TalkPageHandler(webapp2.RequestHandler):\n    def get(self):\n        self.redirect('/listtopic?category=' + self.request.get('category'))\n        #templates=env2.get_template('Talk.html')\n        #self.response.out.write(templates.render())\n        #top = DTopic.query().filter(DTopic.TheId == 124).get()\n        #for comment in top:\n        #print top.Comments\n        #data={\n        #'comments':top.Comments\n        #}\n\n        #results_template = env2.get_template('Talk.html')\n        #self.response.out.write(results_template.render(data))\n\n    #def post(self):\n     #   top=DTopic.query().filter(DTopic.TheId == 124).get()\n      #  top.Comments.append(self.request.get('newCom'))\n       # top.put()\n       \n        #results_template = env2.get_template('Talk.html')\n        \n#user.get_current_user##############################################################################\n\nclass ListTopicHandler(webapp2.RequestHandler):\n    def get(self):\n        templates=env.get_template('topics.html')\n        n=get_category_id(self.request.get(\"category\"))\n        top = DTopic.query().filter(DTopic.TheId == n).get()\n        if top is None:\n            my_comments = []\n            t = DTopic(\n                TheId=n,\n                Category =self.request.get(\"Category\"))\n            t.put()\n            #'Category'=(self.request.get(\"category\"))\n        else:\n            my_comments = top.Comments\n\n        data={\n            'topic': self.request.get('category'),\n            'comments':my_comments\n        }\n        results_template = env.get_template('topics.html') \n        self.response.out.write(results_template.render(data))\n        \n    def post(self): ## here's the new POST method in the MainHandler\n        n=get_category_id(self.request.get(\"category\"))\n        top=DTopic.query().filter(DTopic.TheId == n).get()\n        top.Comments.append(self.request.get('newCom'))\n        top.put()\n        self.redirect('/listtopic?category=' + self.request.get('category'))\n        #self.redirect('/talkpage')\nclass FormatHandler(webapp2.RequestHandler):\n    def get(self):\n        template = env.get_template(\"format.html\")\n        format_query = Format.query()\n        format_results = format_query.fetch()\n        format_result = format_results[0]\n        data = {}\n        data['title'] = format_result.title\n        data[''] = format_result.source\n        self.response.write(template.render(data)) \n\nclass WorkHandler(webapp2.RequestHandler):\n    def get(self):\n        templates=env2.get_template('mainindex.html')  #render's the form for creating new topics\n        self.response.out.write(templates.render())\n\n\napp = webapp2.WSGIApplication([\n    ('/', MainHandler),\n    ('/webpage', WebPageHandler),\n    ('/newtopic', SetTopicHandler),\n    ('/talkpage', TalkPageHandler),\n    ('/workpage', WorkHandler),\n    ('/listtopic', ListTopicHandler)\n], debug=True)\n","repo_name":"jacqugraves/group5","sub_path":"letstalk/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30239992118","text":"from collections import defaultdict\n\n\n# read in vent lines\nwith open('input.txt') as f:\n    lines = [ line.split(' -> ') for line in f.read().splitlines() ]\n\n\n# get all points between endpoints\ndef draw_line(x1, y1, x2, y2):\n    x1, y1, x2, y2 = [ int(i) for i in [x1, y1, x2, y2 ] ]\n\n    if x1 == x2:\n        return [ f'{x1},{i}' for i in range(min(y1,y2), max(y1,y2) + 1) ]\n    elif y1 == y2:\n        return [ f'{i},{y1}' for i in range(min(x1,x2), max(x1,x2) + 1) ]\n    else:\n        None\n\n\n# draw all lines between vent line endpoints\nvents = []\nfor line in lines:\n    vent_line = draw_line(*line[0].split(','), *line[1].split(','))\n    if vent_line: vents.extend(vent_line)\n\n\n# count duplicate points which indicate intersection\npoint_counts = defaultdict(int)\nfor vent in vents:\n    point_counts[vent] += 1\n\nnum_intersections = len([ 1 for point, count in point_counts.items() if count > 1 ])\n\nprint(f'Number of vent line intersections: {num_intersections}')\n","repo_name":"jtcrank/aoc","sub_path":"2021/5/5_1.py","file_name":"5_1.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8091336426","text":"import socket\n\nfrom time import sleep\nfrom environs import Env\n\n\nclass IbrdtnDaemon:\n    def __init__(self):\n        env = Env()\n        env.read_env()\n        self._DTN_DAEMON_ADDRESS = env.str(\"DTN_DAEMON_ADDRESS\")\n        self._DTN_DAEMON_PORT = env.int(\"DTN_DAEMON_PORT\")\n        self._DTN_APP = env.str(\"DTN_APP\")\n        self.daemon_socket = None\n        self.daemon_stream = None\n\n    def create_connection(self):\n        \"\"\"\n          Attempts to create a connection to IBRDTN daemon, if it fails, takes a\n          5 seconds interval until next try (keep trying until it succeeds).\n        \"\"\"\n        connected = False\n\n        while not connected:\n            try:\n                print(\"Trying to connect to IBRDTN daemon...\")\n                self._connect_to_daemon()\n                connected = True\n                print(\"Trying to connect to IBRDTN daemon... CONNECTED!\")\n            except ConnectionError:\n                sleep(5)\n\n    def _connect_to_daemon(self):\n        \"\"\"\n            Creates a socket and a stream (file object aka file descriptor)\n            to communicate with DTN daemon. Sets the daemon in protocol extended\n            mode and the endpoint app source.\n        \"\"\"\n        try:\n            # Create the socket to communicate with the DTN daemon\n            self.daemon_socket = socket.socket()\n            # Connect to the DTN daemon\n            self.daemon_socket.connect(\n                (self._DTN_DAEMON_ADDRESS, self._DTN_DAEMON_PORT)\n            )\n            # Get a file object associated with the daemon's socket\n            self.daemon_stream = self.daemon_socket.makefile()\n            # Read daemon's header response\n            self.daemon_stream.readline()\n            # Switch to extended protocol mode\n            self.daemon_socket.send(b\"protocol extended\\n\")\n            # Read protocol switch response\n            self.daemon_stream.readline()\n            # Set endpoint identifier\n            self.daemon_socket.send(\n                bytes(\"set endpoint %s\\n\" % self._DTN_APP, encoding=\"UTF-8\")\n            )\n            # Read protocol set EID response\n            self.daemon_stream.readline()\n\n        except ConnectionError as error:\n            raise ConnectionError(\n                \"Failed to create a socket and stream to the IBRDTN daemon.\\n\",\n                error,\n            )\n\n    def close_connection(self):\n        \"\"\"\n        Closes stream (file descriptor) and socket to IBTDTN daemon.\n        \"\"\"\n        if self.daemon_stream is not None:\n            self.daemon_stream.close()\n\n        if self.daemon_socket is not None:\n            self.daemon_socket.close()\n","repo_name":"dzvid/aqs-gateway","sub_path":"src/ibrdtn.py","file_name":"ibrdtn.py","file_ext":"py","file_size_in_byte":2648,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"23944655061","text":"#!/usr/bin/env python\n\"\"\"\n05-Oct-2017: Scratch script for blog post about Google Calendar API\nThis script is a sample and NOT indicative of Qxf2's programming habits.\n\nThis script will:\na) Connect to Google Calendar\nb) Get calendar ids for all Qxf2 employees\nc) Execute a search in a given hardcoded timeframe for all employees\nd) List any event with the word PTO in the summary\n\nTo setup, I followed this reference:\nhttps://developers.google.com/google-apps/calendar/quickstart/python\n\nReferences:\n1. https://developers.google.com/google-apps/calendar/quickstart/python\n2. https://developers.google.com/google-apps/calendar/v3/reference/events/list\n3. https://developers.google.com/google-apps/calendar/v3/reference/calendarList/list\n\"\"\"\n\n\nfrom __future__ import print_function\nimport httplib2\nimport os\n\n# from apiclient import discovery\n# Commented above import statement and replaced it below because of\n# reader Vishnukumar's comment\n# Src: https://stackoverflow.com/a/30811628\n\nimport googleapiclient.discovery as discovery\nfrom oauth2client import client\nfrom oauth2client import tools\nfrom oauth2client.file import Storage\n\nimport datetime\n\ntry:\n\timport argparse\n\tflags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()\nexcept ImportError:\n\tflags = None\n\n# If modifying these scopes, delete your previously saved credentials\n# at ~/.credentials/calendar-python-quickstart.json\nSCOPES = 'https://www.googleapis.com/auth/calendar.readonly'\nCLIENT_SECRET_FILE = 'client_secret.json'\nAPPLICATION_NAME = 'Quickstart'\nMAKERSPACE_CALENDAR_ID = \"makerspace.se_dsd75rv0l7rblcq1sd627fab38@group.calendar.google.com\"\n\n\ndef get_credentials():\n\t\"\"\"Gets valid user credentials from storage.\n\n\tIf nothing has been stored, or if the stored credentials are invalid,\n\tthe OAuth2 flow is completed to obtain the new credentials.\n\n\tReturns:\n\t\tCredentials, the obtained credential.\n\t\"\"\"\n\tcredential_dir = os.path.join(\".\", '.credentials')\n\tif not os.path.exists(credential_dir):\n\t\tos.makedirs(credential_dir)\n\tcredential_path = os.path.join(credential_dir, 'calendar-python-quickstart.json')\n\n\tstore = Storage(credential_path)\n\tcredentials = store.get()\n\tif not credentials or credentials.invalid:\n\t\tflow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES)\n\t\tflow.user_agent = APPLICATION_NAME\n\t\tif flags:\n\t\t\tcredentials = tools.run_flow(flow, store, flags)\n\t\telse:  # Needed only for compatibility with Python 2.6\n\t\t\tcredentials = tools.run(flow, store)\n\t\tprint('Storing credentials to ' + credential_path)\n\treturn credentials\n\ndef get_makerspace_calendar_entry(service):\n\tcalendar_list = service.calendarList().list().execute()\n\tfor calendar_list_entry in calendar_list['items']:\n\t\tif MAKERSPACE_CALENDAR_ID == calendar_list_entry['id']:\n\t\t\treturn calendar_list_entry\n\telse:\n\t\traise KeyError(f\"Could not find the Makerspace calendar '{MAKERSPACE_CALENDAR_ID}' among the shared calendars\")\n\ndef main():\n\t\"\"\"Shows basic usage of the Google Calendar API.\n\n\tCreates a Google Calendar API service object and outputs a list of the next\n\t10 events on the user's calendar.\n\t\"\"\"\n\tcredentials = get_credentials()\n\thttp = credentials.authorize(httplib2.Http())\n\tservice = discovery.build('calendar', 'v3', http=http)\n\n\t# This code is to fetch the calendar ids shared with me\n\t# Src: https://developers.google.com/google-apps/calendar/v3/reference/calendarList/list\n\tmakerspace_calendar_entry = get_makerspace_calendar_entry(service)\n\n\t# This code is to look for all-day events in each calendar for the month of September\n\t# Src: https://developers.google.com/google-apps/calendar/v3/reference/events/list\n\t# You need to get this from command line\n\t# Bother about it later!\n\tstart_date = datetime.datetime.now().isoformat() + 'Z'\n\tend_date = (datetime.datetime.now() + datetime.timedelta(days=30)).isoformat() + 'Z'\n\n\tcount = 0\n\tprint('\\n----%s:\\n' % MAKERSPACE_CALENDAR_ID)\n\teventsResult = service.events().list(\n\t\tcalendarId=MAKERSPACE_CALENDAR_ID,\n\t\ttimeMin=start_date,\n\t\ttimeMax=end_date,\n\t\tsingleEvents=True,\n\t\torderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tkey_events = [event for event in events if \"summary\" in event and \"Nyckel\" in event[\"summary\"]]\n\n\tprint(\"\")\n\tif len(key_events) == 0:\n\t\tprint('No upcoming events found.')\n\telse:\n\t\tprint(f\"There are {len(key_events)} key handouts in the next 30 days in calendar '{MAKERSPACE_CALENDAR_ID}':\")\n\t\tfor event in key_events:\n\t\t\tprint(f\"event: {event['start']['dateTime']} to {event['end']['dateTime']}\")\n\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"makerspace/Makerspace-calendar-events","sub_path":"list_events.py","file_name":"list_events.py","file_ext":"py","file_size_in_byte":4525,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14880772625","text":"#https://www.codewars.com/kata/525caa5c1bf619d28c000335/train/python\nimport numpy as np\ndef is_solved(board):\n    #convert to numpy array\n    b=np.array(board)\n    #we obtain posibles win positions\n    winnerList=[[b[0,0],b[1,1],b[2,2]],\n              [b[0,2],b[1,1],b[2,0]],\n                b[:,0].tolist(),\n                b[:,1].tolist(),\n                b[:,2].tolist(),\n                b[0,:].tolist(),\n                b[1,:].tolist(),\n                b[2,:].tolist()]\n    \n   #we go through the possibilities of victory and check if in some\n   #same player has marked all 3 positions\n    for t in winnerList:\n        if all(e1 == 1 for e1 in t):\n            return 1\n        elif all(e1 == 2 for e1 in t):\n            return 2\n    #If we have arrived here, no player has won, we check if there is any free space left -> value 0    \n    if np.any(b[:] == 0):\n        return -1 \n\n    #in any other case, it is a tie\n    return 0\n    \n\n\n\nprint(is_solved( [[1, 2, 1],\n                  [1, 1, 2],\n                  [2, 2, 0]]))","repo_name":"wikilift/CodeWars","sub_path":"py/TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"43694944411","text":"from ftw.upgrade import UpgradeStep\n\n\nclass MigrateRemark(UpgradeStep):\n\n    def __call__(self):\n        from ftw.book.content.remark import Remark\n        for obj in self.objects({'portal_type': 'Remark'},\n                                'Migrate Remark class'):\n\n            # The Remark class stays the same but subclasses\n            # the ftw.contentpage TextBlock class now.\n            # For having up to date objects we mainly recalculate\n            # the provided interfaces (using self.migrate_class) and\n            # reindex the corresponding index.\n            self.migrate_class(obj, Remark)\n            obj.reindexObject(idxs=['object_provides'])\n","repo_name":"4teamwork/ftw.book","sub_path":"ftw/book/upgrades/to3007.py","file_name":"to3007.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"8416678414","text":"from rest_framework import serializers\nfrom .models import Signal\nimport numpy as np\nfrom mytrading.models import Trader\n\nclass TraderSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Trader\n        fields = (\"username\",\"first_name\", \"last_name\")\n\n\nclass SignalSerializer(serializers.ModelSerializer):\n    trader = TraderSerializer()\n\n    class Meta:\n        model = Signal\n        fields = (\n            'trader',\n            'ticker',\n            'buy_emas',\n            'sell_emas',\n            'buy_smas',\n            'sell_smas',\n            'buy_ema_10_25',\n            'sell_ema_10_25',\n            'buy_ema_25_50',\n            'sell_ema_25_50',\n            'buy_ema_10_50',\n            'sell_ema_10_50',\n            'buy_sma_10_25',\n            'sell_sma_10_25',\n            'buy_sma_25_50',\n            'sell_sma_25_50',\n            'buy_sma_10_50',\n            'sell_sma_10_50'\n        )\n\n    def to_representation(self, instance):\n        rep = super().to_representation(instance)\n        for key, value in rep.items():\n            if isinstance(value, list):\n                rep[key] = [\n                    None if (isinstance(x, float) and np.isnan(x)) else x for x in value\n                ]\n        return rep\n","repo_name":"amiri-reza/FinalProject","sub_path":"stock_prediction/rerestful_api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20508048180","text":"# Global parameters (regex costs)\r\n\r\nUNION_COST = 30\r\nCONCAT_COST = 5\r\nCLOSURE_COST = 20\r\nSYMBOL_COST = 20\r\nHOLE_COST = 100\r\n\r\n\r\n\r\n# DQN parameters\r\n\r\n\r\nBATCH_SIZE = 32\r\nGAMMA = 0.999\r\nEPS_START = 1\r\nEPS_END = 0.05\r\nEPS_DECAY = 50000\r\nTARGET_UPDATE = 1\r\n\r\nLENGTH_LIMIT = 30\r\nEXAMPLE_LENGTH_LIMIT = 100\r\n\r\n# gym 행동 공간에서 행동의 숫자를 얻습니다.\r\nn_actions = 12\r\n\r\nREPLAY_INITIAL = 10000\r\nREPALY_MEMORY_SIZE = 1000000\r\n\r\n\r\n# A2C parameters\r\n\r\n\r\nNUM_PROCESSES = 1  # 동시 실행 환경 수\r\nNUM_ADVANCED_STEP = 5 # 총 보상을 계산할 때 Advantage 학습을 할 단계 수\r\nSHOW_ITER = 1\r\n\r\n# A2C 손실함수 계산에 사용되는 상수\r\nvalue_loss_coef = 0.5\r\nentropy_coef = 0.01\r\npolicy_loss_coef = 1\r\nmax_grad_norm = 0.5\r\n\r\nGAMMA = 0.9  # 시간할인율\r\n\r\nlr = 1e-3\r\neps = 1e-5\r\nalpha = 0.99","repo_name":"suhyeon0123/SynthesizingRegularExpression","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"48333171463","text":"import torch\nimport torch.utils.model_zoo as model_zoo\nfrom torch import nn\nfrom torchvision.models.resnet import ResNet, BasicBlock, model_urls\n\n\nclass CrashDetectorNet(ResNet):\n    \"\"\"\n    \"\"\"\n\n    def __init__(self, pretrained_resnet18=True, H=32, **kwargs):\n        super(CrashDetectorNet, self).__init__(BasicBlock, [2, 2, 2, 2])\n        if pretrained_resnet18 == True:\n            self.load_from_pretrained_resnet18()\n\n        num_ftrs = self.fc.in_features\n        self.fc_0 = torch.nn.Linear(num_ftrs, H)\n        self.fc_1 = torch.nn.Linear(H, 2)\n        self.fc_softmax = nn.Softmax(dim=None)\n\n    def forward(self, x, **kwargs):\n        pool_out = self._one_forward(x)\n        fc_0_out = self.fc_0(pool_out)\n        fc_0_out = self.relu(fc_0_out)\n        fc_1_out = self.fc_1(fc_0_out)\n        fc_soft = self.fc_softmax(fc_1_out)\n\n        return fc_soft\n\n    def _one_forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        return x\n\n    def load_from_pretrained_resnet18(self):\n        print(\"Loading pretrained resnet18\")\n        self.load_state_dict(model_zoo.load_url(model_urls[\"resnet18\"]))\n","repo_name":"facebookresearch/pyrobot","sub_path":"examples/crash_detection/crash_utils/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":2174,"dataset":"github-code","pt":"77"}
+{"seq_id":"31824735724","text":"# vim: set fileencoding=utf-8\nfrom typing import Any, Dict, List\nfrom typing_extensions import Final\n\nfrom bemani.backend.popn.base import PopnMusicBase\nfrom bemani.backend.popn.fantasia import PopnMusicFantasia\n\nfrom bemani.backend.base import Status\nfrom bemani.common import Profile, VersionConstants, ID\nfrom bemani.data import UserID, Link, Score\nfrom bemani.protocol import Node\n\n\nclass PopnMusicSunnyPark(PopnMusicBase):\n    name: str = \"Pop'n Music Sunny Park\"\n    version: int = VersionConstants.POPN_MUSIC_SUNNY_PARK\n\n    # Chart type, as returned from the game\n    GAME_CHART_TYPE_EASY: Final[int] = 0\n    GAME_CHART_TYPE_NORMAL: Final[int] = 1\n    GAME_CHART_TYPE_HYPER: Final[int] = 2\n    GAME_CHART_TYPE_EX: Final[int] = 3\n\n    # Chart type, as packed into a hiscore binary\n    GAME_CHART_TYPE_EASY_POSITION: Final[int] = 0\n    GAME_CHART_TYPE_NORMAL_POSITION: Final[int] = 1\n    GAME_CHART_TYPE_HYPER_POSITION: Final[int] = 2\n    GAME_CHART_TYPE_EX_POSITION: Final[int] = 3\n\n    # Medal type, as returned from the game\n    GAME_PLAY_MEDAL_CIRCLE_FAILED: Final[int] = 1\n    GAME_PLAY_MEDAL_DIAMOND_FAILED: Final[int] = 2\n    GAME_PLAY_MEDAL_STAR_FAILED: Final[int] = 3\n    GAME_PLAY_MEDAL_CIRCLE_CLEARED: Final[int] = 5\n    GAME_PLAY_MEDAL_DIAMOND_CLEARED: Final[int] = 6\n    GAME_PLAY_MEDAL_STAR_CLEARED: Final[int] = 7\n    GAME_PLAY_MEDAL_CIRCLE_FULL_COMBO: Final[int] = 9\n    GAME_PLAY_MEDAL_DIAMOND_FULL_COMBO: Final[int] = 10\n    GAME_PLAY_MEDAL_STAR_FULL_COMBO: Final[int] = 11\n    GAME_PLAY_MEDAL_PERFECT: Final[int] = 15\n\n    # Maximum music ID for this game\n    GAME_MAX_MUSIC_ID: Final[int] = 1350\n\n    def previous_version(self) -> PopnMusicBase:\n        return PopnMusicFantasia(self.data, self.config, self.model)\n\n    @classmethod\n    def get_settings(cls) -> Dict[str, Any]:\n        \"\"\"\n        Return all of our front-end modifiably settings.\n        \"\"\"\n        return {\n            \"ints\": [\n                {\n                    \"name\": \"Music Open Phase\",\n                    \"tip\": \"Default music phase for all players.\",\n                    \"category\": \"game_config\",\n                    \"setting\": \"music_phase\",\n                    \"values\": {\n                        0: \"No music unlocks\",\n                        1: \"Phase 1\",\n                        2: \"Phase 2\",\n                        3: \"Phase 3\",\n                        4: \"Phase 4\",\n                        5: \"Phase 5\",\n                        6: \"Phase 6\",\n                        7: \"Phase 7\",\n                        8: \"Phase 8\",\n                        9: \"Phase MAX\",\n                    },\n                },\n                {\n                    \"name\": \"Event Phase\",\n                    \"tip\": \"Event phase for all players.\",\n                    \"category\": \"game_config\",\n                    \"setting\": \"event_phase\",\n                    \"values\": {\n                        0: \"No event\",\n                        1: \"Pop'n Walker Phase 1\",\n                        2: \"Pop'n Walker Phase 2\",\n                        3: \"Pop'n Walker Phase 3\",\n                        # Phase 4 turns off Pop'n Walker but does not enable\n                        # Wai Wai Pop'n Zoo so I've left it out.\n                        # Phase 5 appears to be identical to phase 6 below.\n                        6: \"Wai Wai Pop'n Zoo Phase 1: Elephants\",\n                        # Phase 7 appears to be identical to phase 8 below.\n                        8: \"Wai Wai Pop'n Zoo Phase 2: Dog\",\n                        9: \"Wai Wai Pop'n Zoo Phase 3: Alpaca\",\n                        10: \"Wai Wai Pop'n Zoo Phase 4: Cow\",\n                    },\n                },\n            ]\n        }\n\n    def __format_medal_for_score(self, score: Score) -> int:\n        medal = {\n            self.PLAY_MEDAL_CIRCLE_FAILED: self.GAME_PLAY_MEDAL_CIRCLE_FAILED,\n            self.PLAY_MEDAL_DIAMOND_FAILED: self.GAME_PLAY_MEDAL_DIAMOND_FAILED,\n            self.PLAY_MEDAL_STAR_FAILED: self.GAME_PLAY_MEDAL_STAR_FAILED,\n            self.PLAY_MEDAL_EASY_CLEAR: self.GAME_PLAY_MEDAL_CIRCLE_CLEARED,  # Map approximately\n            self.PLAY_MEDAL_CIRCLE_CLEARED: self.GAME_PLAY_MEDAL_CIRCLE_CLEARED,\n            self.PLAY_MEDAL_DIAMOND_CLEARED: self.GAME_PLAY_MEDAL_DIAMOND_CLEARED,\n            self.PLAY_MEDAL_STAR_CLEARED: self.GAME_PLAY_MEDAL_STAR_CLEARED,\n            self.PLAY_MEDAL_CIRCLE_FULL_COMBO: self.GAME_PLAY_MEDAL_CIRCLE_FULL_COMBO,\n            self.PLAY_MEDAL_DIAMOND_FULL_COMBO: self.GAME_PLAY_MEDAL_DIAMOND_FULL_COMBO,\n            self.PLAY_MEDAL_STAR_FULL_COMBO: self.GAME_PLAY_MEDAL_STAR_FULL_COMBO,\n            self.PLAY_MEDAL_PERFECT: self.GAME_PLAY_MEDAL_PERFECT,\n        }[score.data.get_int(\"medal\")]\n        medal_pos = {\n            self.CHART_TYPE_EASY: self.GAME_CHART_TYPE_EASY_POSITION,\n            self.CHART_TYPE_NORMAL: self.GAME_CHART_TYPE_NORMAL_POSITION,\n            self.CHART_TYPE_HYPER: self.GAME_CHART_TYPE_HYPER_POSITION,\n            self.CHART_TYPE_EX: self.GAME_CHART_TYPE_EX_POSITION,\n        }[score.chart]\n        return medal << (medal_pos * 4)\n\n    def format_profile(self, userid: UserID, profile: Profile) -> Node:\n        root = Node.void(\"playerdata\")\n\n        # Set up the base profile\n        base = Node.void(\"base\")\n        root.add_child(base)\n        base.add_child(Node.string(\"name\", profile.get_str(\"name\", \"なし\")))\n        base.add_child(Node.string(\"g_pm_id\", ID.format_extid(profile.extid)))\n        base.add_child(Node.u8(\"mode\", profile.get_int(\"mode\", 0)))\n        base.add_child(Node.s8(\"button\", profile.get_int(\"button\", 0)))\n        base.add_child(Node.s8(\"last_play_flag\", profile.get_int(\"last_play_flag\", -1)))\n        base.add_child(\n            Node.u8(\"medal_and_friend\", profile.get_int(\"medal_and_friend\", 0))\n        )\n        base.add_child(Node.s8(\"category\", profile.get_int(\"category\", -1)))\n        base.add_child(Node.s8(\"sub_category\", profile.get_int(\"sub_category\", -1)))\n        base.add_child(Node.s16(\"chara\", profile.get_int(\"chara\", -1)))\n        base.add_child(Node.s8(\"chara_category\", profile.get_int(\"chara_category\", -1)))\n        base.add_child(Node.u8(\"collabo\", 255))\n        base.add_child(Node.u8(\"sheet\", profile.get_int(\"sheet\", 0)))\n        base.add_child(Node.s8(\"tutorial\", profile.get_int(\"tutorial\", 0)))\n        base.add_child(Node.s8(\"music_open_pt\", profile.get_int(\"music_open_pt\", 0)))\n        base.add_child(Node.s8(\"is_conv\", -1))\n        base.add_child(Node.s32(\"option\", profile.get_int(\"option\", 0)))\n        base.add_child(Node.s16(\"music\", profile.get_int(\"music\", -1)))\n        base.add_child(Node.u16(\"ep\", profile.get_int(\"ep\", 0)))\n        base.add_child(\n            Node.s32_array(\"sp_color_flg\", profile.get_int_array(\"sp_color_flg\", 2))\n        )\n        base.add_child(Node.s32(\"read_news\", profile.get_int(\"read_news\", 0)))\n        base.add_child(\n            Node.s16(\n                \"consecutive_days_coupon\", profile.get_int(\"consecutive_days_coupon\", 0)\n            )\n        )\n        base.add_child(Node.s8(\"staff\", 0))\n        # These are probably from an old event, but if they aren't present and defaulted,\n        # then different songs show up in the Zoo event.\n        base.add_child(\n            Node.u16_array(\n                \"gitadora_point\",\n                profile.get_int_array(\"gitadora_point\", 3, [2000, 2000, 2000]),\n            )\n        )\n        base.add_child(\n            Node.u8(\"gitadora_select\", profile.get_int(\"gitadora_select\", 2))\n        )\n\n        # Statistics section and scores section\n        statistics = self.get_play_statistics(userid)\n        base.add_child(Node.s32(\"total_play_cnt\", statistics.total_plays))\n        base.add_child(Node.s16(\"today_play_cnt\", statistics.today_plays))\n        base.add_child(Node.s16(\"consecutive_days\", statistics.consecutive_days))\n\n        # Number of rivals that are active for this version.\n        links = self.data.local.user.get_links(self.game, self.version, userid)\n        rivalcount = 0\n        for link in links:\n            if link.type != \"rival\":\n                continue\n\n            if not self.has_profile(link.other_userid):\n                continue\n\n            # This profile is valid.\n            rivalcount += 1\n        base.add_child(Node.u8(\"active_fr_num\", rivalcount))\n\n        last_played = [\n            x[0]\n            for x in self.data.local.music.get_last_played(\n                self.game, self.music_version, userid, 3\n            )\n        ]\n        most_played = [\n            x[0]\n            for x in self.data.local.music.get_most_played(\n                self.game, self.music_version, userid, 20\n            )\n        ]\n        while len(last_played) < 3:\n            last_played.append(-1)\n        while len(most_played) < 20:\n            most_played.append(-1)\n\n        hiscore_array = [0] * int((((self.GAME_MAX_MUSIC_ID * 4) * 17) + 7) / 8)\n        clear_medal = [0] * self.GAME_MAX_MUSIC_ID\n        clear_medal_sub = [0] * self.GAME_MAX_MUSIC_ID\n\n        scores = self.data.remote.music.get_scores(\n            self.game, self.music_version, userid\n        )\n        for score in scores:\n            if score.id > self.GAME_MAX_MUSIC_ID:\n                continue\n\n            # Skip any scores for chart types we don't support\n            if score.chart not in [\n                self.CHART_TYPE_EASY,\n                self.CHART_TYPE_NORMAL,\n                self.CHART_TYPE_HYPER,\n                self.CHART_TYPE_EX,\n            ]:\n                continue\n            if score.data.get_int(\"medal\") == self.PLAY_MEDAL_NO_PLAY:\n                continue\n\n            clear_medal[score.id] = clear_medal[\n                score.id\n            ] | self.__format_medal_for_score(score)\n            hiscore_index = (score.id * 4) + {\n                self.CHART_TYPE_EASY: self.GAME_CHART_TYPE_EASY_POSITION,\n                self.CHART_TYPE_NORMAL: self.GAME_CHART_TYPE_NORMAL_POSITION,\n                self.CHART_TYPE_HYPER: self.GAME_CHART_TYPE_HYPER_POSITION,\n                self.CHART_TYPE_EX: self.GAME_CHART_TYPE_EX_POSITION,\n            }[score.chart]\n            hiscore_byte_pos = int((hiscore_index * 17) / 8)\n            hiscore_bit_pos = int((hiscore_index * 17) % 8)\n            hiscore_value = score.points << hiscore_bit_pos\n            hiscore_array[hiscore_byte_pos] = hiscore_array[hiscore_byte_pos] | (\n                hiscore_value & 0xFF\n            )\n            hiscore_array[hiscore_byte_pos + 1] = hiscore_array[\n                hiscore_byte_pos + 1\n            ] | ((hiscore_value >> 8) & 0xFF)\n            hiscore_array[hiscore_byte_pos + 2] = hiscore_array[\n                hiscore_byte_pos + 2\n            ] | ((hiscore_value >> 16) & 0xFF)\n\n        hiscore = bytes(hiscore_array)\n\n        base.add_child(Node.s16_array(\"my_best\", most_played))\n        base.add_child(Node.s16_array(\"latest_music\", last_played))\n        base.add_child(Node.u16_array(\"clear_medal\", clear_medal))\n        base.add_child(Node.u8_array(\"clear_medal_sub\", clear_medal_sub))\n\n        # Goes outside of base for some reason\n        root.add_child(Node.binary(\"hiscore\", hiscore))\n\n        # Avatar section\n        avatar_dict = profile.get_dict(\"avatar\")\n        avatar = Node.void(\"avatar\")\n        root.add_child(avatar)\n        avatar.add_child(Node.u8(\"hair\", avatar_dict.get_int(\"hair\", 0)))\n        avatar.add_child(Node.u8(\"face\", avatar_dict.get_int(\"face\", 0)))\n        avatar.add_child(Node.u8(\"body\", avatar_dict.get_int(\"body\", 0)))\n        avatar.add_child(Node.u8(\"effect\", avatar_dict.get_int(\"effect\", 0)))\n        avatar.add_child(Node.u8(\"object\", avatar_dict.get_int(\"object\", 0)))\n        avatar.add_child(\n            Node.u8_array(\"comment\", avatar_dict.get_int_array(\"comment\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\"get_hair\", avatar_dict.get_int_array(\"get_hair\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\"get_face\", avatar_dict.get_int_array(\"get_face\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\"get_body\", avatar_dict.get_int_array(\"get_body\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\"get_effect\", avatar_dict.get_int_array(\"get_effect\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\"get_object\", avatar_dict.get_int_array(\"get_object\", 2))\n        )\n        avatar.add_child(\n            Node.s32_array(\n                \"get_comment_over\", avatar_dict.get_int_array(\"get_comment_over\", 3)\n            )\n        )\n        avatar.add_child(\n            Node.s32_array(\n                \"get_comment_under\", avatar_dict.get_int_array(\"get_comment_under\", 3)\n            )\n        )\n\n        # Avatar add section\n        avatar_add_dict = profile.get_dict(\"avatar_add\")\n        avatar_add = Node.void(\"avatar_add\")\n        root.add_child(avatar_add)\n        avatar_add.add_child(\n            Node.s32_array(\"get_hair\", avatar_add_dict.get_int_array(\"get_hair\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"get_face\", avatar_add_dict.get_int_array(\"get_face\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"get_body\", avatar_add_dict.get_int_array(\"get_body\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"get_effect\", avatar_add_dict.get_int_array(\"get_effect\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"get_object\", avatar_add_dict.get_int_array(\"get_object\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\n                \"get_comment_over\", avatar_add_dict.get_int_array(\"get_comment_over\", 2)\n            )\n        )\n        avatar_add.add_child(\n            Node.s32_array(\n                \"get_comment_under\",\n                avatar_add_dict.get_int_array(\"get_comment_under\", 2),\n            )\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"new_hair\", avatar_add_dict.get_int_array(\"new_hair\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"new_face\", avatar_add_dict.get_int_array(\"new_face\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"new_body\", avatar_add_dict.get_int_array(\"new_body\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"new_effect\", avatar_add_dict.get_int_array(\"new_effect\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\"new_object\", avatar_add_dict.get_int_array(\"new_object\", 2))\n        )\n        avatar_add.add_child(\n            Node.s32_array(\n                \"new_comment_over\", avatar_add_dict.get_int_array(\"new_comment_over\", 2)\n            )\n        )\n        avatar_add.add_child(\n            Node.s32_array(\n                \"new_comment_under\",\n                avatar_add_dict.get_int_array(\"new_comment_under\", 2),\n            )\n        )\n\n        # Net VS section\n        netvs = Node.void(\"netvs\")\n        root.add_child(netvs)\n        netvs.add_child(Node.s32(\"rank_point\", 0))\n        netvs.add_child(Node.s16_array(\"record\", [0, 0, 0, 0, 0, 0]))\n        netvs.add_child(Node.u8(\"rank\", 0))\n        netvs.add_child(Node.s8(\"vs_rank_old\", 0))\n        netvs.add_child(Node.s8_array(\"ojama_condition\", [0] * 74))\n        netvs.add_child(Node.s8_array(\"set_ojama\", [0, 0, 0]))\n        netvs.add_child(Node.s8_array(\"set_recommend\", [0, 0, 0]))\n        netvs.add_child(Node.u8(\"netvs_play_cnt\", 0))\n        for dialog in [0, 1, 2, 3, 4, 5]:\n            netvs.add_child(Node.string(\"dialog\", f\"dialog#{dialog}\"))\n\n        sp_data = Node.void(\"sp_data\")\n        root.add_child(sp_data)\n        sp_data.add_child(Node.s32(\"sp\", profile.get_int(\"sp\", 0)))\n\n        gakuen = Node.void(\"gakuen_data\")\n        root.add_child(gakuen)\n        gakuen.add_child(Node.s32(\"music_list\", -1))\n\n        saucer = Node.void(\"flying_saucer\")\n        root.add_child(saucer)\n        saucer.add_child(Node.s32(\"music_list\", -1))\n        saucer.add_child(Node.s32(\"tune_count\", -1))\n        saucer.add_child(Node.u32(\"clear_norma\", 0))\n        saucer.add_child(Node.u32(\"clear_norma_add\", 0))\n\n        # Wai Wai Pop'n Zoo event\n        zoo_dict = profile.get_dict(\"zoo\")\n        zoo = Node.void(\"zoo\")\n        root.add_child(zoo)\n        zoo.add_child(Node.u16_array(\"point\", zoo_dict.get_int_array(\"point\", 5)))\n        zoo.add_child(\n            Node.s32_array(\"music_list\", zoo_dict.get_int_array(\"music_list\", 2))\n        )\n        zoo.add_child(\n            Node.s8_array(\n                \"today_play_flag\", zoo_dict.get_int_array(\"today_play_flag\", 4)\n            )\n        )\n\n        # Pop'n Walker event\n        personal_event_dict = profile.get_dict(\"personal_event\")\n        personal_event = Node.void(\"personal_event\")\n        root.add_child(personal_event)\n        personal_event.add_child(\n            Node.s16_array(\"pos\", personal_event_dict.get_int_array(\"pos\", 2))\n        )\n        personal_event.add_child(\n            Node.s16(\"point\", personal_event_dict.get_int(\"point\"))\n        )\n        personal_event.add_child(\n            Node.u32_array(\n                \"walk_data\", personal_event_dict.get_int_array(\"walk_data\", 128)\n            )\n        )\n        personal_event.add_child(\n            Node.u32_array(\n                \"event_data\", personal_event_dict.get_int_array(\"event_data\", 4)\n            )\n        )\n\n        # We don't support triple journey, so this is stubbed out.\n        triple = Node.void(\"triple_journey\")\n        root.add_child(triple)\n        triple.add_child(Node.s32(\"music_list\", -1))\n        triple.add_child(Node.s32_array(\"boss_damage\", [65534, 65534, 65534, 65534]))\n        triple.add_child(Node.s32_array(\"boss_stun\", [0, 0, 0, 0]))\n        triple.add_child(Node.s32(\"magic_gauge\", 0))\n        triple.add_child(Node.s32(\"today_party\", 0))\n        triple.add_child(Node.bool(\"union_magic\", False))\n        triple.add_child(Node.float(\"base_attack_rate\", 1.0))\n        triple.add_child(Node.s32(\"iidx_play_num\", 0))\n        triple.add_child(Node.s32(\"reflec_play_num\", 0))\n        triple.add_child(Node.s32(\"voltex_play_num\", 0))\n        triple.add_child(Node.bool(\"iidx_play_flg\", True))\n        triple.add_child(Node.bool(\"reflec_play_flg\", True))\n        triple.add_child(Node.bool(\"voltex_play_flg\", True))\n\n        ios = Node.void(\"ios\")\n        root.add_child(ios)\n        ios.add_child(Node.s32(\"continueRightAnswer\", 30))\n        ios.add_child(Node.s32(\"totalRightAnswer\", 30))\n\n        kac2013 = Node.void(\"kac2013\")\n        root.add_child(kac2013)\n        kac2013.add_child(Node.s8(\"music_num\", 0))\n        kac2013.add_child(Node.s16(\"music\", 0))\n        kac2013.add_child(Node.u8(\"sheet\", 0))\n\n        baseball = Node.void(\"baseball_data\")\n        root.add_child(baseball)\n        baseball.add_child(Node.s64(\"music_list\", -1))\n\n        for id in [3, 5, 7]:\n            node = Node.void(\"floor_infection\")\n            root.add_child(node)\n            node.add_child(Node.s32(\"infection_id\", id))\n            node.add_child(Node.s32(\"music_list\", -1))\n\n        return root\n\n    def format_conversion(self, userid: UserID, profile: Profile) -> Node:\n        root = Node.void(\"playerdata\")\n\n        root.add_child(Node.string(\"name\", profile.get_str(\"name\", \"なし\")))\n        root.add_child(Node.s16(\"chara\", profile.get_int(\"chara\", -1)))\n        root.add_child(Node.s32(\"option\", profile.get_int(\"option\", 0)))\n        root.add_child(Node.u8(\"version\", 0))\n        root.add_child(Node.u8(\"kind\", 0))\n        root.add_child(Node.u8(\"season\", 0))\n\n        clear_medal = [0] * self.GAME_MAX_MUSIC_ID\n\n        scores = self.data.remote.music.get_scores(\n            self.game, self.music_version, userid\n        )\n        for score in scores:\n            if score.id > self.GAME_MAX_MUSIC_ID:\n                continue\n\n            # Skip any scores for chart types we don't support\n            if score.chart not in [\n                self.CHART_TYPE_EASY,\n                self.CHART_TYPE_NORMAL,\n                self.CHART_TYPE_HYPER,\n                self.CHART_TYPE_EX,\n            ]:\n                continue\n            if score.data.get_int(\"medal\") == self.PLAY_MEDAL_NO_PLAY:\n                continue\n\n            clear_medal[score.id] = clear_medal[\n                score.id\n            ] | self.__format_medal_for_score(score)\n\n        root.add_child(Node.u16_array(\"clear_medal\", clear_medal))\n\n        return root\n\n    def unformat_profile(\n        self, userid: UserID, request: Node, oldprofile: Profile\n    ) -> Profile:\n        newprofile = oldprofile.clone()\n        newprofile.replace_int(\"option\", request.child_value(\"option\"))\n        newprofile.replace_int(\"chara\", request.child_value(\"chara\"))\n        newprofile.replace_int(\"mode\", request.child_value(\"mode\"))\n        newprofile.replace_int(\"button\", request.child_value(\"button\"))\n        newprofile.replace_int(\"music\", request.child_value(\"music\"))\n        newprofile.replace_int(\"sheet\", request.child_value(\"sheet\"))\n        newprofile.replace_int(\"last_play_flag\", request.child_value(\"last_play_flag\"))\n        newprofile.replace_int(\"category\", request.child_value(\"category\"))\n        newprofile.replace_int(\"sub_category\", request.child_value(\"sub_category\"))\n        newprofile.replace_int(\"chara_category\", request.child_value(\"chara_category\"))\n        newprofile.replace_int(\n            \"medal_and_friend\", request.child_value(\"medal_and_friend\")\n        )\n        newprofile.replace_int(\"ep\", request.child_value(\"ep\"))\n        newprofile.replace_int_array(\n            \"sp_color_flg\", 2, request.child_value(\"sp_color_flg\")\n        )\n        newprofile.replace_int(\"read_news\", request.child_value(\"read_news\"))\n        newprofile.replace_int(\n            \"consecutive_days_coupon\", request.child_value(\"consecutive_days_coupon\")\n        )\n        newprofile.replace_int(\"tutorial\", request.child_value(\"tutorial\"))\n        newprofile.replace_int(\"music_open_pt\", request.child_value(\"music_open_pt\"))\n        newprofile.replace_int_array(\n            \"gitadora_point\", 3, request.child_value(\"gitadora_point\")\n        )\n        newprofile.replace_int(\n            \"gitadora_select\", request.child_value(\"gitadora_select\")\n        )\n\n        sp_node = request.child(\"sp_data\")\n        if sp_node is not None:\n            newprofile.replace_int(\"sp\", sp_node.child_value(\"sp\"))\n\n        zoo_dict = newprofile.get_dict(\"zoo\")\n        zoo_node = request.child(\"zoo\")\n        if zoo_node is not None:\n            zoo_dict.replace_int_array(\"point\", 5, zoo_node.child_value(\"point\"))\n            zoo_dict.replace_int_array(\n                \"music_list\", 2, zoo_node.child_value(\"music_list\")\n            )\n            zoo_dict.replace_int_array(\n                \"today_play_flag\", 4, zoo_node.child_value(\"today_play_flag\")\n            )\n        newprofile.replace_dict(\"zoo\", zoo_dict)\n\n        personal_event_dict = newprofile.get_dict(\"personal_event\")\n        personal_event_node = request.child(\"personal_event\")\n        if personal_event_node is not None:\n            personal_event_dict.replace_int_array(\n                \"pos\", 2, personal_event_node.child_value(\"pos\")\n            )\n            personal_event_dict.replace_int(\n                \"point\", personal_event_node.child_value(\"point\")\n            )\n            personal_event_dict.replace_int_array(\n                \"walk_data\", 128, personal_event_node.child_value(\"walk_data\")\n            )\n            personal_event_dict.replace_int_array(\n                \"event_data\", 4, personal_event_node.child_value(\"event_data\")\n            )\n        newprofile.replace_dict(\"personal_event\", personal_event_dict)\n\n        avatar_dict = newprofile.get_dict(\"avatar\")\n        avatar_dict.replace_int(\"hair\", request.child_value(\"hair\"))\n        avatar_dict.replace_int(\"face\", request.child_value(\"face\"))\n        avatar_dict.replace_int(\"body\", request.child_value(\"body\"))\n        avatar_dict.replace_int(\"effect\", request.child_value(\"effect\"))\n        avatar_dict.replace_int(\"object\", request.child_value(\"object\"))\n        avatar_dict.replace_int_array(\"comment\", 2, request.child_value(\"comment\"))\n        avatar_dict.replace_int_array(\"get_hair\", 2, request.child_value(\"get_hair\"))\n        avatar_dict.replace_int_array(\"get_face\", 2, request.child_value(\"get_face\"))\n        avatar_dict.replace_int_array(\"get_body\", 2, request.child_value(\"get_body\"))\n        avatar_dict.replace_int_array(\n            \"get_effect\", 2, request.child_value(\"get_effect\")\n        )\n        avatar_dict.replace_int_array(\n            \"get_object\", 2, request.child_value(\"get_object\")\n        )\n        avatar_dict.replace_int_array(\n            \"get_comment_over\", 3, request.child_value(\"get_comment_over\")\n        )\n        avatar_dict.replace_int_array(\n            \"get_comment_under\", 3, request.child_value(\"get_comment_under\")\n        )\n        newprofile.replace_dict(\"avatar\", avatar_dict)\n\n        avatar_add_dict = newprofile.get_dict(\"avatar_add\")\n        avatar_add_node = request.child(\"avatar_add\")\n        if avatar_add_node is not None:\n            avatar_add_dict.replace_int_array(\n                \"get_hair\", 2, avatar_add_node.child_value(\"get_hair\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_face\", 2, avatar_add_node.child_value(\"get_face\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_body\", 2, avatar_add_node.child_value(\"get_body\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_effect\", 2, avatar_add_node.child_value(\"get_effect\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_object\", 2, avatar_add_node.child_value(\"get_object\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_comment_over\", 2, avatar_add_node.child_value(\"get_comment_over\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"get_comment_under\", 2, avatar_add_node.child_value(\"get_comment_under\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_hair\", 2, avatar_add_node.child_value(\"new_hair\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_face\", 2, avatar_add_node.child_value(\"new_face\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_body\", 2, avatar_add_node.child_value(\"new_body\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_effect\", 2, avatar_add_node.child_value(\"new_effect\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_object\", 2, avatar_add_node.child_value(\"new_object\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_comment_over\", 2, avatar_add_node.child_value(\"new_comment_over\")\n            )\n            avatar_add_dict.replace_int_array(\n                \"new_comment_under\", 2, avatar_add_node.child_value(\"new_comment_under\")\n            )\n        newprofile.replace_dict(\"avatar_add\", avatar_add_dict)\n\n        # Keep track of play statistics\n        self.update_play_statistics(userid)\n\n        # Extract scores\n        for node in request.children:\n            if node.name == \"stage\":\n                songid = node.child_value(\"no\")\n                chart = {\n                    self.GAME_CHART_TYPE_EASY: self.CHART_TYPE_EASY,\n                    self.GAME_CHART_TYPE_NORMAL: self.CHART_TYPE_NORMAL,\n                    self.GAME_CHART_TYPE_HYPER: self.CHART_TYPE_HYPER,\n                    self.GAME_CHART_TYPE_EX: self.CHART_TYPE_EX,\n                }[node.child_value(\"sheet\")]\n                medal = (node.child_value(\"n_data\") >> (chart * 4)) & 0x000F\n                medal = {\n                    self.GAME_PLAY_MEDAL_CIRCLE_FAILED: self.PLAY_MEDAL_CIRCLE_FAILED,\n                    self.GAME_PLAY_MEDAL_DIAMOND_FAILED: self.PLAY_MEDAL_DIAMOND_FAILED,\n                    self.GAME_PLAY_MEDAL_STAR_FAILED: self.PLAY_MEDAL_STAR_FAILED,\n                    self.GAME_PLAY_MEDAL_CIRCLE_CLEARED: self.PLAY_MEDAL_CIRCLE_CLEARED,\n                    self.GAME_PLAY_MEDAL_DIAMOND_CLEARED: self.PLAY_MEDAL_DIAMOND_CLEARED,\n                    self.GAME_PLAY_MEDAL_STAR_CLEARED: self.PLAY_MEDAL_STAR_CLEARED,\n                    self.GAME_PLAY_MEDAL_CIRCLE_FULL_COMBO: self.PLAY_MEDAL_CIRCLE_FULL_COMBO,\n                    self.GAME_PLAY_MEDAL_DIAMOND_FULL_COMBO: self.PLAY_MEDAL_DIAMOND_FULL_COMBO,\n                    self.GAME_PLAY_MEDAL_STAR_FULL_COMBO: self.PLAY_MEDAL_STAR_FULL_COMBO,\n                    self.GAME_PLAY_MEDAL_PERFECT: self.PLAY_MEDAL_PERFECT,\n                }[medal]\n                points = node.child_value(\"score\")\n                self.update_score(userid, songid, chart, points, medal)\n\n        return newprofile\n\n    def handle_game_get_request(self, request: Node) -> Node:\n        game_config = self.get_game_config()\n        event_phase = game_config.get_int(\"event_phase\")\n        music_phase = game_config.get_int(\"music_phase\")\n\n        root = Node.void(\"game\")\n        root.add_child(Node.s32(\"ir_phase\", 0))\n        root.add_child(Node.s32(\"music_open_phase\", music_phase))\n        root.add_child(Node.s32(\"collabo_phase\", 8))\n        root.add_child(Node.s32(\"personal_event_phase\", event_phase))\n        root.add_child(Node.s32(\"shop_event_phase\", 6))\n        root.add_child(Node.s32(\"netvs_phase\", 0))\n        root.add_child(Node.s32(\"card_phase\", 9))\n        root.add_child(Node.s32(\"other_phase\", 9))\n        root.add_child(Node.s32(\"local_matching_enable\", 1))\n        root.add_child(Node.s32(\"n_matching_sec\", 60))\n        root.add_child(Node.s32(\"l_matching_sec\", 60))\n        root.add_child(Node.s32(\"is_check_cpu\", 0))\n        root.add_child(Node.s32(\"week_no\", 0))\n        root.add_child(Node.s16_array(\"sel_ranking\", [-1, -1, -1, -1, -1]))\n        root.add_child(Node.s16_array(\"up_ranking\", [-1, -1, -1, -1, -1]))\n        return root\n\n    def handle_game_active_request(self, request: Node) -> Node:\n        # Update the name of this cab for admin purposes\n        machine = self.get_machine()\n        machine.name = request.child_value(\"shop_name\") or machine.name\n        machine.data.replace_int(\"pref\", request.child_value(\"pref\"))\n        self.update_machine(machine)\n        return Node.void(\"game\")\n\n    def handle_game_taxphase_request(self, request: Node) -> Node:\n        return Node.void(\"game\")\n\n    def handle_playerdata_expire_request(self, request: Node) -> Node:\n        return Node.void(\"playerdata\")\n\n    def handle_playerdata_logout_request(self, request: Node) -> Node:\n        return Node.void(\"playerdata\")\n\n    def handle_playerdata_get_request(self, request: Node) -> Node:\n        modelstring = request.attribute(\"model\")\n        refid = request.child_value(\"ref_id\")\n        root = self.get_profile_by_refid(\n            refid,\n            self.NEW_PROFILE_ONLY if modelstring is None else self.OLD_PROFILE_ONLY,\n        )\n        if root is None:\n            root = Node.void(\"playerdata\")\n            root.set_attribute(\"status\", str(Status.NO_PROFILE))\n        return root\n\n    def handle_playerdata_conversion_request(self, request: Node) -> Node:\n        refid = request.child_value(\"ref_id\")\n        name = request.child_value(\"name\")\n        chara = request.child_value(\"chara\")\n        root = self.new_profile_by_refid(refid, name, chara)\n        if root is None:\n            root = Node.void(\"playerdata\")\n            root.set_attribute(\"status\", str(Status.NO_PROFILE))\n        return root\n\n    def handle_playerdata_new_request(self, request: Node) -> Node:\n        refid = request.child_value(\"ref_id\")\n        name = request.child_value(\"name\")\n        root = self.new_profile_by_refid(refid, name)\n        if root is None:\n            root = Node.void(\"playerdata\")\n            root.set_attribute(\"status\", str(Status.NO_PROFILE))\n        return root\n\n    def handle_playerdata_set_request(self, request: Node) -> Node:\n        refid = request.attribute(\"ref_id\")\n        machine = self.get_machine()\n\n        root = Node.void(\"playerdata\")\n        root.add_child(\n            Node.s8(\"pref\", machine.data.get_int(\"pref\", self.get_machine_region()))\n        )\n        if refid is None:\n            root.add_child(Node.string(\"name\", \"\"))\n            root.add_child(Node.s8(\"get_coupon_cnt\", -1))\n            root.add_child(Node.s16(\"chara\", -1))\n            root.add_child(Node.u8(\"hair\", 0))\n            root.add_child(Node.u8(\"face\", 0))\n            root.add_child(Node.u8(\"body\", 0))\n            root.add_child(Node.u8(\"effect\", 0))\n            root.add_child(Node.u8(\"object\", 0))\n            root.add_child(Node.u8(\"comment_1\", 0))\n            root.add_child(Node.u8(\"comment_2\", 0))\n            return root\n\n        userid = self.data.remote.user.from_refid(self.game, self.version, refid)\n        if userid is None:\n            root.add_child(Node.string(\"name\", \"\"))\n            root.add_child(Node.s8(\"get_coupon_cnt\", -1))\n            root.add_child(Node.s16(\"chara\", -1))\n            root.add_child(Node.u8(\"hair\", 0))\n            root.add_child(Node.u8(\"face\", 0))\n            root.add_child(Node.u8(\"body\", 0))\n            root.add_child(Node.u8(\"effect\", 0))\n            root.add_child(Node.u8(\"object\", 0))\n            root.add_child(Node.u8(\"comment_1\", 0))\n            root.add_child(Node.u8(\"comment_2\", 0))\n            return root\n\n        oldprofile = self.get_profile(userid) or Profile(\n            self.game, self.version, refid, 0\n        )\n        newprofile = self.unformat_profile(userid, request, oldprofile)\n\n        if newprofile is not None:\n            self.put_profile(userid, newprofile)\n            avatar_dict = newprofile.get_dict(\"avatar\")\n\n            root.add_child(Node.string(\"name\", newprofile[\"name\"]))\n            root.add_child(Node.s8(\"get_coupon_cnt\", -1))\n            root.add_child(Node.s16(\"chara\", newprofile.get_int(\"chara\", -1)))\n            root.add_child(Node.u8(\"hair\", avatar_dict.get_int(\"hair\", 0)))\n            root.add_child(Node.u8(\"face\", avatar_dict.get_int(\"face\", 0)))\n            root.add_child(Node.u8(\"body\", avatar_dict.get_int(\"body\", 0)))\n            root.add_child(Node.u8(\"effect\", avatar_dict.get_int(\"effect\", 0)))\n            root.add_child(Node.u8(\"object\", avatar_dict.get_int(\"object\", 0)))\n            root.add_child(\n                Node.u8(\"comment_1\", avatar_dict.get_int_array(\"comment\", 2)[0])\n            )\n            root.add_child(\n                Node.u8(\"comment_2\", avatar_dict.get_int_array(\"comment\", 2)[1])\n            )\n\n        return root\n\n    def handle_playerdata_friend_request(self, request: Node) -> Node:\n        refid = request.attribute(\"ref_id\")\n        root = Node.void(\"playerdata\")\n\n        # Look up our own user ID based on the RefID provided.\n        userid = self.data.remote.user.from_refid(self.game, self.version, refid)\n        if userid is None:\n            root.set_attribute(\"status\", str(Status.NO_PROFILE))\n            return root\n\n        # Grab the links that we care about.\n        links = self.data.local.user.get_links(self.game, self.version, userid)\n        profiles: Dict[UserID, Profile] = {}\n        rivals: List[Link] = []\n        for link in links:\n            if link.type != \"rival\":\n                continue\n\n            other_profile = self.get_profile(link.other_userid)\n            if other_profile is None:\n                continue\n            profiles[link.other_userid] = other_profile\n            rivals.append(link)\n\n        for rival in links[:2]:\n            rivalid = rival.other_userid\n            rivalprofile = profiles[rivalid]\n            scores = self.data.remote.music.get_scores(\n                self.game, self.music_version, rivalid\n            )\n\n            # First, output general profile info.\n            friend = Node.void(\"friend\")\n            root.add_child(friend)\n\n            # This might be for having non-active or non-confirmed friends, but setting to 0 makes the\n            # ranking numbers disappear and the player icon show a questionmark.\n            friend.add_child(Node.s8(\"open\", 1))\n\n            # Set up some sane defaults.\n            friend.add_child(Node.string(\"name\", rivalprofile.get_str(\"name\", \"なし\")))\n            friend.add_child(\n                Node.string(\"g_pm_id\", ID.format_extid(rivalprofile.extid))\n            )\n            friend.add_child(Node.s16(\"chara\", rivalprofile.get_int(\"chara\", -1)))\n\n            # Set up player avatar.\n            avatar_dict = rivalprofile.get_dict(\"avatar\")\n            friend.add_child(Node.u8(\"hair\", avatar_dict.get_int(\"hair\", 0)))\n            friend.add_child(Node.u8(\"face\", avatar_dict.get_int(\"face\", 0)))\n            friend.add_child(Node.u8(\"body\", avatar_dict.get_int(\"body\", 0)))\n            friend.add_child(Node.u8(\"effect\", avatar_dict.get_int(\"effect\", 0)))\n            friend.add_child(Node.u8(\"object\", avatar_dict.get_int(\"object\", 0)))\n            friend.add_child(\n                Node.u8_array(\"comment\", avatar_dict.get_int_array(\"comment\", 2))\n            )\n\n            # Perform hiscore/medal conversion.\n            hiscore_array = [0] * int((((self.GAME_MAX_MUSIC_ID * 4) * 17) + 7) / 8)\n            clear_medal = [0] * self.GAME_MAX_MUSIC_ID\n            for score in scores:\n                if score.id > self.GAME_MAX_MUSIC_ID:\n                    continue\n\n                # Skip any scores for chart types we don't support\n                if score.chart not in [\n                    self.CHART_TYPE_EASY,\n                    self.CHART_TYPE_NORMAL,\n                    self.CHART_TYPE_HYPER,\n                    self.CHART_TYPE_EX,\n                ]:\n                    continue\n                if score.data.get_int(\"medal\") == self.PLAY_MEDAL_NO_PLAY:\n                    continue\n\n                clear_medal[score.id] = clear_medal[\n                    score.id\n                ] | self.__format_medal_for_score(score)\n                hiscore_index = (score.id * 4) + {\n                    self.CHART_TYPE_EASY: self.GAME_CHART_TYPE_EASY_POSITION,\n                    self.CHART_TYPE_NORMAL: self.GAME_CHART_TYPE_NORMAL_POSITION,\n                    self.CHART_TYPE_HYPER: self.GAME_CHART_TYPE_HYPER_POSITION,\n                    self.CHART_TYPE_EX: self.GAME_CHART_TYPE_EX_POSITION,\n                }[score.chart]\n                hiscore_byte_pos = int((hiscore_index * 17) / 8)\n                hiscore_bit_pos = int((hiscore_index * 17) % 8)\n                hiscore_value = score.points << hiscore_bit_pos\n                hiscore_array[hiscore_byte_pos] = hiscore_array[hiscore_byte_pos] | (\n                    hiscore_value & 0xFF\n                )\n                hiscore_array[hiscore_byte_pos + 1] = hiscore_array[\n                    hiscore_byte_pos + 1\n                ] | ((hiscore_value >> 8) & 0xFF)\n                hiscore_array[hiscore_byte_pos + 2] = hiscore_array[\n                    hiscore_byte_pos + 2\n                ] | ((hiscore_value >> 16) & 0xFF)\n\n            hiscore = bytes(hiscore_array)\n            friend.add_child(Node.u16_array(\"clear_medal\", clear_medal))\n            friend.add_child(Node.binary(\"hiscore\", hiscore))\n\n        return root\n\n    def handle_lobby_requests(self, request: Node) -> Node:\n        # Stub out the entire lobby service\n        return Node.void(\"lobby\")\n","repo_name":"DragonMinded/bemaniutils","sub_path":"bemani/backend/popn/sunnypark.py","file_name":"sunnypark.py","file_ext":"py","file_size_in_byte":39283,"program_lang":"python","lang":"en","doc_type":"code","stars":185,"dataset":"github-code","pt":"77"}
+{"seq_id":"73893703287","text":"# 给定长度为 2n 的数组, 你的任务是将这些数分成 n 对, 例如 (a1, b1), (a2, b2), ..., (an, bn) ，使得从1 到 n 的 min(ai, bi) 总和最大。\n#\n# 示例 1:\n#\n# 输入: [1,4,3,2]\n#\n# 输出: 4\n# 解释: n 等于 2, 最大总和为 4 = min(1, 2) + min(3, 4).\n# 提示:\n#\n# n 是正整数,范围在 [1, 10000].\n# 数组中的元素范围在 [-10000, 10000].\n\nclass Solution:\n    def arrayPairSum(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        nums.sort()\n        res = 0\n        for i in range(0, len(nums), 2):\n           res += nums[i]\n        return res\n\n        # 更简单的形式 ------------------------\n        nums.sort()\n        return sum(nums[::2])\n        # -----------------------------------\ns = Solution()\ntest = [-9, -3, 1, 4, 3, 2]\nprint(s.arrayPairSum(test))\n","repo_name":"ChunLi-onion/leetcode_practices","sub_path":"561数组拆分.py","file_name":"561数组拆分.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18117770789","text":"import torch\nimport numpy as np\nimport math\nimport operator\n\nfrom global_random_seed import RANDOM_SEED\n\n# make everything reproducible\nnp.random.seed(RANDOM_SEED)\ntorch.manual_seed(RANDOM_SEED)\ntorch.backends.cudnn.deterministic = True\ntorch.cuda.manual_seed(RANDOM_SEED)\ntorch.cuda.manual_seed_all(RANDOM_SEED)\n\n\n# Already implemented: Ausgabe mean position/variance der scores\n# 1. rausfinden, welche axis wörter, welche axis sind die positionen\n# 2. für jedes über die positionen axis softmax (wird nur für zusätzliche Analyse verwendet)\n# 3. Pro Wort: w = softmax(attention_scores), r = alle positions = \"np.arange(len(w))\"\n# 4. mean =  weighted_average = np.average(r, weights=w)\n# 5. std_dev = https://stackoverflow.com/questions/2413522/weighted-standard-deviation-in-numpy\n# 6. Verteilung pro Satz ausgeben, für folgende Wörter (und erste 20 Sätze):\n#   - größtes/kleinstes mean\n#   - größte/kleinste std_dev\n\n\n# TODO: finish me\ndef plot_generator_with_softmax(whole_sentence_graph_dict):\n    import numpy as np\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n\n    for single_word in whole_sentence_graph_dict:\n        for head in whole_sentence_graph_dict[single_word]:\n\n            word = single_word\n            sns.set(style=\"whitegrid\")\n            fig = plt.figure(figsize=(8, 6))\n\n            def softmax(x):\n                return np.exp(x) / np.exp(x).sum(axis=0)\n\n            # attn\n            data = whole_sentence_graph_dict[single_word][head][\"attn\"]\n\n            # attn_pos\n            data2 = whole_sentence_graph_dict[single_word][head][\"attn_pos\"]\n\n            # combined\n            data3 = whole_sentence_graph_dict[single_word][head][\"combined\"]\n\n            softmax_flag = True\n\n            if softmax_flag:\n                # plt.bar(range(len(data)), softmax(data), color='b', alpha=0.3, label ='attn') #, hatch=\"/\")\n                # plt.bar(range(len(data2)), softmax(data2), color='g', alpha = 0.3, label ='attn_rel_pos') #, hatch=\"o\")\n                # plt.bar(range(len(data3)), softmax(data3), color='r',alpha = 0.3, label ='attn_comb') #, hatch=\"\\\\\")\n                plt.plot(range(len(data[:43])), softmax(data[:43]), '-bx', alpha=0.5, label=r'$softmax(y_{inner})$')\n                plt.plot(range(len(data2[:43])), softmax(data2[:43]), '-go', alpha=0.5, lw=2, ms=4,\n                         label=r'$softmax(y_{rel\\_pos})$')\n                plt.plot(range(len(data3[:43])), softmax(data3[:43]), '-r+', alpha=0.5, lw=2,\n                         label=r'$softmax(y_{inner} + y_{rel\\_pos})$')\n                ax = fig.add_subplot(111)\n                ax.fill_between(range(len(data[:43])), 0, softmax(data[:43]), color='dodgerblue', alpha=0.4)\n                ax.fill_between(range(len(data[:43])), 0, softmax(data2[:43]), color='mediumseagreen', alpha=0.4)\n                ax.fill_between(range(len(data[:43])), 0, softmax(data3[:43]), color='indianred', alpha=0.4)\n\n            position = whole_sentence_graph_dict[single_word][head][\"position\"]\n\n            graph_title = \"\".join([\"Head \", head, \" (\", \"Word: \", word, \" Position:\", position, \")\"])\n            plt.title(graph_title)\n            plt.xlabel(\"Word Position in the Sentence\")\n            plt.ylabel(\"Attention Probability\")\n\n            # plt.grid()\n            plt.legend(fontsize=16)\n\n            graph_filename = \"\".join([\"utils/plots/head_\", head, \"_\", word, \"_\", position, \"_softmax_lines.png\"])\n            plt.savefig(graph_filename, dpi=350)\n\n            plt.close(fig)\n\n\n# TODO: finish me\ndef plot_generator_without_softmax():\n    import numpy as np\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n\n    sns.set(style=\"whitegrid\")\n\n    fig = plt.figure(figsize=(8, 6))\n\n    # Not showing the data lists here\n\n    def softmax(x):\n        return np.exp(x) / np.exp(x).sum(axis=0)\n\n    # attn\n    data = [-0.005951299, -0.029044457, -2.4142258, 2.190176, 1.6590111, -3.6578588, -4.684568, -3.6578588, -3.6578588,\n            6.084622, 13.555143, 13.555143, -4.34307, -1.1962872, -0.47124174, -0.43637118, -8.555032, -6.269103,\n            -4.5881658, -5.9282355, -4.585698, -5.2294097, -6.944389, -3.5933948, -4.3854713, -2.5584598, -4.093828,\n            -7.9165735, -4.7697864, -5.935749, -5.9056034, -3.213047, -8.166379, -5.539582, -6.075837, -4.260547,\n            -2.8777754, -3.6983604, -4.148365, -4.4483614, -2.085313, -3.1456637, -3.706437, 2.0111775, 2.0111775,\n            2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775,\n            2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775, 2.0111775,\n            2.0111775, 2.0111775]\n\n    # attn_pos\n    data2 = [0.77760446, -0.3654992, -0.3654992, -0.3654992, -0.3654992, -0.3654992, -0.3654992, -0.3654992, -0.3654992,\n             0.5600485, 0.5600485, 0.5600485, 0.5600485, 1.0015192, 1.0015192, -0.8097563, -0.14138925, 0.66161174,\n             -0.43525112, -0.43525112, 0.29473847, 0.29473847, 0.30402362, 0.4407499, 0.99283254, 1.0506727, 1.0493455,\n             1.0368179, 1.0545973, 1.1171892, 1.0651786, 1.1270455, 0.8863518, 0.58156127, 0.6494905, 0.7443865,\n             0.69479305, 0.85680157, 0.63184834, 0.45794958, 0.38933513, 0.1833995, 0.32618314, 0.31002286, 0.23198071,\n             0.53673124, 0.34084955, 0.2578649, -0.8240579, -1.3246806, -1.287403, -1.3355032, -1.1736788, -1.1917881,\n             -1.1917881, -1.7546477, -1.5299661, -1.5299661, -1.6786951, -1.5299661, -1.1985171, -0.49476224,\n             -0.4208252, -0.1696207, -0.33454028]\n\n    # combined\n    data3 = [0.7716532, -0.39454365, -2.779725, 1.8246768, 1.2935119, -4.023358, -5.050067, -4.023358, -4.023358,\n             6.6446705, 14.115191, 14.115191, -3.7830215, -0.19476795, 0.5302775, -1.2461275, -8.696421, -5.6074915,\n             -5.023417, -6.363487, -4.29096, -4.9346714, -6.6496506, -3.2986562, -4.2205296, -2.393518, -3.9288864,\n             -7.7516317, -4.6048446, -5.7708073, -5.7406616, -3.0481052, -8.452318, -5.8255215, -6.361777, -4.546487,\n             -3.163715, -3.9843, -4.4343047, -4.734301, -2.3712525, -3.4316032, -3.9923766, 1.725238, 1.725238,\n             1.725238, 1.725238, 1.725238, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384,\n             0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384, 0.74440384,\n             0.74440384, 0.74440384]\n\n    softmax_flag = False\n\n    if softmax_flag:\n        plt.bar(range(len(data)), softmax(data), color='b', alpha=0.3, label='attn')  # , hatch=\"/\")\n        plt.bar(range(len(data2)), softmax(data2), color='g', alpha=0.3, label='attn_rel_pos')  # , hatch=\"o\")\n        plt.bar(range(len(data3)), softmax(data3), color='r', alpha=0.3, label='attn_comb')  # , hatch=\"\\\\\")\n        plt.plot(range(len(data)), softmax(data), 'bx', alpha=0.5)\n        plt.plot(range(len(data2)), softmax(data2), 'go', alpha=0.5, ms=4)\n        plt.plot(range(len(data3)), softmax(data3), 'r+', alpha=0.5)\n    else:\n        plt.bar(range(len(data[:43])), data[:43], color='b', alpha=0.3, label=r'$y_{inner}$')  # , hatch=\"/\")\n        plt.bar(range(len(data2[:43])), data2[:43], color='g', alpha=0.3, label=r'$y_{rel\\_pos}$')  # , hatch=\"o\")\n        plt.bar(range(len(data3[:43])), data3[:43], color='r', alpha=0.3,\n                label=r'$y_{inner} + y_{rel\\_pos}$')  # , hatch=\"\\\\\")\n        plt.plot(range(len(data[:43])), data[:43], 'bx', alpha=0.5)\n        plt.plot(range(len(data2[:43])), data2[:43], 'go', alpha=0.5, ms=4)\n        plt.plot(range(len(data3[:43])), data3[:43], 'r+', alpha=0.5)\n\n    plt.title(\"Head 1\")\n    plt.xlabel(\"Word Position in the Sentence\")\n    plt.ylabel(\"Attention Weight\")\n\n    # plt.grid()\n    plt.legend(fontsize=16)\n\n    plt.savefig('head_1_in_32_no_softmax.png', dpi=350)\n\n    # plt.close(fig)\n\n\ndef investigate_attention(attn, attn_pos, sentence_words, outer_vocab):\n    ########################################\n    # options for the investigations BEGIN #\n    ########################################\n\n    # choose which head to investigate\n    list_of_head = [1.0, 2.0, 3.0]\n    list_of_combinations = [\"attn\", \"attn_pos\", \"combined\"]\n\n    # save data for plots\n    # TODO: generate plots automatically\n\n    from collections import defaultdict\n    whole_sentence_graph_dict = defaultdict(lambda: defaultdict(dict))\n    of_data = dict()\n    in_32_data = dict()\n    in_16_data = dict()\n\n    for head in list_of_head:\n\n        for what_attention_to_investigate in list_of_combinations:\n            # options to investigate from:\n            #   attn:  basic attention from self-attention paper without any pos encodings\n            #   attn_pos:  attention with our relative pos encodings\n            #   combined:  both of the above combined\n            # what_attention_to_investigate = \"combined\"\n\n            if what_attention_to_investigate == \"combined\":\n                all_attn = attn + attn_pos.transpose(1, 2)\n\n            numpy_sentences = sentence_words.cpu().numpy()\n\n            if what_attention_to_investigate == \"attn\":\n                numpy_attention = attn.cpu().numpy()\n            elif what_attention_to_investigate == \"attn_pos\":\n                numpy_attention = attn_pos.cpu().numpy()\n            elif what_attention_to_investigate == \"combined\":\n                numpy_attention = all_attn.cpu().numpy()\n\n            # generate file to save output to based on params above\n            investigations_filename = \"\".join(\n                [\n                    'saved_models/out/attention_mean_and_std_head_',\n                    str(int(head)),\n                    '_',\n                    str(what_attention_to_investigate),\n                    '.txt'\n                ]\n            )\n\n            ######################################\n            # options for the investigations END #\n            ######################################\n\n            def softmax(x):\n                \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n                e_x = np.exp(x - np.max(x))\n                return e_x / e_x.sum(axis=0)  # only difference\n\n            def weighted_avg_and_std(values, weights):\n                \"\"\"\n                Return the weighted average and standard deviation.\n                values, weights -- Numpy ndarrays with the same shape.\n                \"\"\"\n\n                # have to substract the index position from the mean\n                # TODO: in mean and std\n                average = np.average(values, weights=weights)\n                # Fast and numerically precise:\n                variance = np.average((values - average) ** 2, weights=weights)\n                return average, math.sqrt(variance)\n\n            # following run is used for the investigations: python eval.py --model_dir saved_models/lm4\n            # --model checkpoint_epoch_60.pt\n            sentence_to_search = \"They cited the case of OBJ-ORGANIZATION OBJ-ORGANIZATION OBJ-ORGANIZATION OBJ-ORGANIZATION subcontractor SUBJ-PERSON SUBJ-PERSON , who was working in Cuba on a tourist visa and possessed satellite communications equipment , who has been held in a maximum security prison since his arrest Dec 3 .\"\n            for sentence_index, each_sentence in enumerate(numpy_sentences):\n\n                unmapped_sentence = outer_vocab.unmap(each_sentence)\n\n                # DONe\n                # TODO: skip the padded words in both vectors\n\n                # get id-to-word sentence representation\n\n                # print(unmapped_sentence, len(unmapped_sentence))\n\n                # find the position of the first pad\n                first_pad_position = None\n                for i, single_word in enumerate(unmapped_sentence):\n                    if single_word == '<PAD>':\n                        first_pad_position = i\n                        break\n\n                if first_pad_position:\n                    # get all words before the first pad appears\n                    unmapped_sentence_final = unmapped_sentence[:first_pad_position]  # [:first_pad_position]\n                    # iterate over attention scores\n                    # attention heads, 1: +0 2: +50 3: +100. i.e. head 2: numpy_attention[sentence_index+50]...\n                    if head == 1.0:\n                        unpadded_attention = numpy_attention[sentence_index][:first_pad_position]\n                    elif head == 2.0:\n                        if len(numpy_attention) != 27:  # skip the last batch of size 27\n                            unpadded_attention = numpy_attention[sentence_index + 50][:first_pad_position]\n                    elif head == 3.0:\n                        if len(numpy_attention) != 27:  # skip the last batch of size 27\n                            unpadded_attention = numpy_attention[sentence_index + 100][:first_pad_position]\n                else:\n                    unmapped_sentence_final = unmapped_sentence\n                    # attention heads, 1: +0 2: +50 3: +100. i.e. head 2: numpy_attention[sentence_index+50]...\n                    if head == 1.0:\n                        unpadded_attention = numpy_attention[sentence_index]  # numpy_attention[sentence_index+50]\n                    elif head == 2.0:\n                        if len(numpy_attention) != 27:  # skip the last batch of size 27\n                            unpadded_attention = numpy_attention[\n                                sentence_index + 50]  # numpy_attention[sentence_index+50]\n                    elif head == 3.0:\n                        # print(numpy_attention)\n                        if len(numpy_attention) != 27:  # skip the last batch of size 27\n                            unpadded_attention = numpy_attention[\n                                sentence_index + 100]  # numpy_attention[sentence_index+50]\n\n                # TODO: get rid of this later\n                # print(\" \".join(unmapped_sentence))\n\n                if \" \".join(unmapped_sentence_final) == sentence_to_search:\n\n                    # make sure the slicing was correct on both tensors\n                    assert len(unmapped_sentence_final) == len(unpadded_attention)\n\n                    # print(len(unpadded_attention), len(unmapped_sentence_final))\n                    print(unmapped_sentence_final, len(unmapped_sentence_final))\n                    # print(unpadded_attention, len(unpadded_attention))\n                    print()\n\n                    all_means = []\n                    all_stds = []\n\n                    highest_attention_score_per_word = dict()\n\n                    for i, each_word in enumerate(unmapped_sentence_final):\n                        # print(\"WORD:\", each_word)\n                        # print(\"POS. VECTOR:\", unpadded_attention[i])\n\n                        # for each word pos vector, select the highest attention score to other word but itself\n\n                        # mask out the word itself\n\n                        if first_pad_position:\n                            current_word_pos_vector = unpadded_attention[i][:first_pad_position]\n                        else:\n                            current_word_pos_vector = unpadded_attention[i]\n\n                        indices = [i]\n                        mask = np.zeros(current_word_pos_vector.size, dtype=bool)\n                        mask[indices] = True\n                        a = np.ma.array(current_word_pos_vector, mask=mask)\n\n                        # select highest attention score for given word\n                        # print(len(a))\n                        highest_attention_index, highest_attention_score = max(enumerate(a), key=operator.itemgetter(1))\n                        highest_attention_score_per_word[i] = (highest_attention_index, highest_attention_score)\n                        # print(i, highest_attention_index, highest_attention_score)\n\n                        # get weighted average\n                        w = softmax(current_word_pos_vector)\n                        # print(w, len(w))\n                        r = np.arange(len(w)) - i\n\n                        weighted_average_and_std = weighted_avg_and_std(r, w)\n\n                        # print(\"WORD:\", each_word)\n                        # print(\"Weighted Average:\", weighted_average_and_std[0])\n                        # print(\"Weighted Standard Deviation:\", weighted_average_and_std[1])\n\n                        # print(i, first_pad_position, unmapped_sentence_final)\n\n                        if first_pad_position:\n                            if i < first_pad_position:\n                                all_means.append(weighted_average_and_std[0])\n                                all_stds.append(weighted_average_and_std[1])\n                        else:\n                            all_means.append(weighted_average_and_std[0])\n                            all_stds.append(weighted_average_and_std[1])\n\n                        # highest_attention_score_per_word[i] = max(enumerate(all_means), key=operator.itemgetter(1))\n                    # print(all_means)\n\n                    smallest_mean_index, smallest_mean = min(enumerate(all_means), key=operator.itemgetter(1))\n                    biggest_mean_index, biggest_mean = max(enumerate(all_means), key=operator.itemgetter(1))\n\n                    print()\n                    print(\"without padding\")\n                    print(\"smallest_mean:\", smallest_mean)\n                    print(\"smallest_mean_word:\", unmapped_sentence_final[smallest_mean_index], \"index:\",\n                          smallest_mean_index)\n                    print(\"^ av_mean:\", all_means[smallest_mean_index], \"av_std:\", all_stds[smallest_mean_index])\n                    print(\n                        \"highest attention score:\", highest_attention_score_per_word[smallest_mean_index][1], \"word:\",\n                        unmapped_sentence_final[highest_attention_score_per_word[smallest_mean_index][0]],\n                        \"index:\", highest_attention_score_per_word[smallest_mean_index][0])\n                    print()\n\n                    print(\"biggest_mean:\", biggest_mean)\n                    print(\"biggest_mean_word:\", unmapped_sentence_final[biggest_mean_index], \"index:\",\n                          biggest_mean_index)\n                    print(\"^ av_mean:\", all_means[biggest_mean_index], \"av_std:\", all_stds[biggest_mean_index])\n                    print(\n                        \"highest attention score:\", highest_attention_score_per_word[biggest_mean_index][1], \"word:\",\n                        unmapped_sentence_final[highest_attention_score_per_word[biggest_mean_index][0]],\n                        \"index:\", highest_attention_score_per_word[biggest_mean_index][0])\n                    print()\n\n                    smallest_std_index, smallest_std = min(enumerate(all_stds), key=operator.itemgetter(1))\n                    biggest_std_index, biggest_std = max(enumerate(all_stds), key=operator.itemgetter(1))\n\n                    print(\"smallest_std:\", smallest_std)\n                    print(\"smallest_std_word:\", unmapped_sentence_final[smallest_std_index], \"index:\",\n                          smallest_std_index)\n                    print(\"^ av_mean:\", all_means[smallest_std_index], \"av_std:\", all_stds[smallest_std_index])\n                    print(\n                        \"highest attention score:\", highest_attention_score_per_word[smallest_std_index][1], \"word:\",\n                        unmapped_sentence_final[highest_attention_score_per_word[smallest_std_index][0]],\n                        \"index:\", highest_attention_score_per_word[smallest_std_index][0])\n                    print()\n\n                    print(\"biggest_std:\", biggest_std)\n                    print(\"biggest_std_word:\", unmapped_sentence_final[biggest_std_index], \"index:\", biggest_std_index)\n                    print(\"^ av_mean:\", all_means[biggest_std_index], \"av_std:\", all_stds[biggest_std_index])\n                    print(\n                        \"highest attention score:\", highest_attention_score_per_word[biggest_std_index][1], \"word:\",\n                        unmapped_sentence_final[highest_attention_score_per_word[biggest_std_index][0]],\n                        \"index:\", highest_attention_score_per_word[biggest_std_index][0])\n                    print()\n\n                    with open(investigations_filename, 'a') as outfile:\n\n                        # print each att score\n                        outfile.write(\" \".join(\n                            [\" \".join(unmapped_sentence_final), \"\\n\", str(len(unmapped_sentence_final)), \"\\n\"]))\n\n                        for i, element in enumerate(unmapped_sentence_final):\n                            outfile.write(\" \".join([\"index:\", str(i), \" /// word:\", str(element), \"\\n\"]))\n\n                            outfile.write(\" \".join(\n                                [\"Attention vector:\", \"[\", \", \".join([str(x) for x in unpadded_attention[i]]), \"]\",\n                                 \"\\n\",\n                                 str(len(unpadded_attention)), \"\\n\",\n                                 \"av_mean: \", str(all_means[i]), \"av_str: \", str(all_stds[i]), \"\\n\"]))\n\n                            outfile.write(\n                                \" \".join(\n                                    [\"[\", \", \".join([str(x) for x in softmax(current_word_pos_vector)]), \"]\", \"\\n\"]))\n\n                            if what_attention_to_investigate == \"attn\":\n                                if head == 1.0:\n                                    whole_sentence_graph_dict[str(element)][\"1\"][\"position\"] = str(i)\n                                    whole_sentence_graph_dict[str(element)][\"1\"][\"attn\"] = unpadded_attention[i]\n                                elif head == 2.0:\n                                    whole_sentence_graph_dict[str(element)][\"2\"][\"position\"] = str(i)\n                                    whole_sentence_graph_dict[str(element)][\"2\"][\"attn\"] = unpadded_attention[i]\n                                elif head == 3.0:\n                                    whole_sentence_graph_dict[str(element)][\"3\"][\"position\"] = str(i)\n                                    whole_sentence_graph_dict[str(element)][\"3\"][\"attn\"] = unpadded_attention[i]\n                            elif what_attention_to_investigate == \"attn_pos\":\n                                if head == 1.0:\n                                    whole_sentence_graph_dict[str(element)][\"1\"][\"attn_pos\"] = unpadded_attention[i]\n                                elif head == 2.0:\n                                    whole_sentence_graph_dict[str(element)][\"2\"][\"attn_pos\"] = unpadded_attention[i]\n                                elif head == 3.0:\n                                    whole_sentence_graph_dict[str(element)][\"3\"][\"attn_pos\"] = unpadded_attention[i]\n                            elif what_attention_to_investigate == \"combined\":\n                                if head == 1.0:\n                                    whole_sentence_graph_dict[str(element)][\"1\"][\"combined\"] = unpadded_attention[i]\n                                elif head == 2.0:\n                                    whole_sentence_graph_dict[str(element)][\"2\"][\"combined\"] = unpadded_attention[i]\n                                elif head == 3.0:\n                                    whole_sentence_graph_dict[str(element)][\"3\"][\"combined\"] = unpadded_attention[i]\n\n                        # outfile.write(\" \".join([\" \".join([str(x) for x in unpadded_attention]), \"\\n\", str(len(unpadded_attention)), \"\\n\"]))\n\n                        outfile.write(\n                            \" \".join(\n                                [\" \".join(unmapped_sentence_final), \"\\n\", str(len(unmapped_sentence_final)), \"\\n\"]))\n                        outfile.write(\" \".join([\"without padding\", \"\\n\"]))\n                        outfile.write(\" \".join([\"smallest_mean:\", str(smallest_mean), \"\\n\"]))\n                        outfile.write(\" \".join(\n                            [\"smallest_mean_word:\", str(unmapped_sentence_final[smallest_mean_index]), \" index:\",\n                             str(smallest_mean_index), \"\\n\"]))\n                        outfile.write(\" \".join(\n                            [\"^ av_mean:\", str(all_means[smallest_mean_index]), \"av_std:\",\n                             str(all_stds[smallest_mean_index]),\n                             \"\\n\"]))\n                        outfile.write(\" \".join([\n                            \"highest attention score:\", str(highest_attention_score_per_word[smallest_mean_index][1]),\n                            \"word:\",\n                            str(unmapped_sentence_final[highest_attention_score_per_word[smallest_mean_index][0]]),\n                            \"index:\", str(highest_attention_score_per_word[smallest_mean_index][0]), \"\\n\", \"\\n\"]))\n\n                        outfile.write(\" \".join([\"biggest_mean:\", str(biggest_mean), \"\\n\"]))\n                        outfile.write(\" \".join(\n                            [\"biggest_mean_word:\", str(unmapped_sentence_final[biggest_mean_index]), \" index:\",\n                             str(biggest_mean_index), \"\\n\"]))\n                        outfile.write(\" \".join([\"^ av_mean:\", str(all_means[biggest_mean_index]), \"av_std:\",\n                                                str(all_stds[biggest_mean_index]), \"\\n\"]))\n                        outfile.write(\" \".join([\n                            \"highest attention score:\", str(highest_attention_score_per_word[biggest_mean_index][1]),\n                            \"word:\",\n                            str(unmapped_sentence_final[highest_attention_score_per_word[biggest_mean_index][0]]),\n                            \"index:\", str(highest_attention_score_per_word[biggest_mean_index][0]), \"\\n\", \"\\n\"]))\n\n                        outfile.write(\" \".join([\"smallest_std:\", str(smallest_std), \"\\n\"]))\n                        outfile.write(\" \".join(\n                            [\"smallest_std_word:\", str(unmapped_sentence_final[smallest_std_index]), \" index:\",\n                             str(smallest_std_index), \"\\n\"]))\n                        outfile.write(\" \".join([\"^ av_mean:\", str(all_means[smallest_std_index]), \"av_std:\",\n                                                str(all_stds[smallest_std_index]), \"\\n\"]))\n                        outfile.write(\" \".join([\n                            \"highest attention score:\", str(highest_attention_score_per_word[smallest_std_index][1]),\n                            \"word:\",\n                            str(unmapped_sentence_final[highest_attention_score_per_word[smallest_std_index][0]]),\n                            \"index:\", str(highest_attention_score_per_word[smallest_std_index][0]), \"\\n\", \"\\n\"]))\n\n                        outfile.write(\" \".join([\"biggest_std:\", str(biggest_std), \"\\n\"]))\n                        outfile.write(\n                            \" \".join([\"biggest_std_word:\", str(unmapped_sentence_final[biggest_std_index]), \" index:\",\n                                      str(biggest_std_index), \"\\n\"]))\n                        outfile.write(\" \".join([\"^ av_mean:\", str(all_means[biggest_std_index]), \"av_std:\",\n                                                str(all_stds[biggest_std_index]), \"\\n\"]))\n                        outfile.write(\" \".join([\n                            \"highest attention score:\", str(highest_attention_score_per_word[biggest_std_index][1]),\n                            \"word:\",\n                            str(unmapped_sentence_final[highest_attention_score_per_word[biggest_std_index][0]]),\n                            \"index:\", str(highest_attention_score_per_word[biggest_std_index][0]), \"\\n\", \"\\n\"]))\n\n                        outfile.write(\"\\n\")\n\n    # automatically generate plots\n    plot_generator_with_softmax(whole_sentence_graph_dict)\n","repo_name":"ivan-bilan/Relation-Extraction-Transformer","sub_path":"utils/attention_investigation.py","file_name":"attention_investigation.py","file_ext":"py","file_size_in_byte":27685,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"77"}
+{"seq_id":"35013848974","text":"import numpy as np\nimport pickle\nimport os\nimport cv2\nimport torch\nfrom smpl.render_texture import Renderer\n\naction_files = [\n    # '104/104_09.pkl',  # run\n    '104/104_19.pkl',  # walk\n    '39/39_14.pkl',  # walk\n    # '36/36_32.pkl'     # up stairs\n]\nresult = []\nrotate_total_div = 8\n\n\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\n    sparse_mx = sparse_mx.tocoo().astype(np.float32)\n    indices = torch.from_numpy(np.vstack((sparse_mx.row, sparse_mx.col)))\n    indices = indices.long()\n    values = torch.from_numpy(sparse_mx.data)\n    shape = torch.Size(sparse_mx.shape)\n    return torch.sparse.FloatTensor(indices, values, shape)\n\n\nfor file_name in action_files:\n    path = os.path.join('neutrSMPL_CMU', file_name)\n    with open(path, 'rb') as f:\n        data = pickle.load(f)\n        renderer = Renderer('smpl/models/body.obj', 'smpl/models/neutral.pkl', w=224, h=224)\n        texture_bgr = cv2.imread('smpl/models/default_texture2.jpg')\n        texture_bgr = cv2.resize(texture_bgr, dsize=(224, 224))\n        for rotate_div in range(0, rotate_total_div):\n            for i in range(0, len(data['poses']), 20):\n                thetas = np.concatenate((data['trans'][i], data['poses'][i], data['betas']))\n                thetas[3:6] = [np.pi, 0, 0]\n                rn, deviation, silhouette = renderer.render(thetas, texture_bgr,\n                                                            rotate=np.array([0,\n                                                                             2 * np.pi * rotate_div / rotate_total_div\n                                                                                , 0]))\n\n                result.append({\n                    'mat': deviation,\n                    'mask': silhouette\n                })\n            print('process: {} / {}'.format(rotate_div, rotate_total_div))\n\nnp.save('walk_224', result)\n","repo_name":"yt4766269/TextureGeneration","sub_path":"deprecated/get_render_matrix.py","file_name":"get_render_matrix.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"77"}
+{"seq_id":"29432768899","text":"# por favor escribe aquí tu función\ndef es_primo(numero):\n    suma=0\n    for i in range(1,numero):\n        if numero%i==0:\n            suma=suma+i\n    if suma==1:\n        return True\n    else:\n        return False","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_p1/tema2_p1_79ca24162b4cc1e23bd30c1c8a5fbfde.py","file_name":"tema2_p1_79ca24162b4cc1e23bd30c1c8a5fbfde.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22810621003","text":"'''\n    CS5001\n    Spring 2020\n    Project\n    grid.py\n\n    Danielle Tolliver\n\n'''\n\nimport turtle\nimport math\n\nfrom game import *\n\nROWS = 6\nCOLUMNS = 7\n\nclass Grid:\n    ''' class: Grid\n        Attributes: width, height, rows, columns, grid_matrix\n        Methods: make_board, draw_board, draw_column, update_board, is_full\n    '''\n    \n    def __init__(self, width, height):\n        '''\n        Constructor: creates an new instance of game\n        Parameters: width (int), height (int)\n        '''\n        self.width = width\n        self.height = height\n        self.rows = ROWS\n        self.columns = COLUMNS\n        self.board = []\n            \n    def make_board(self):\n        '''\n        Method: make_board\n        Parameters: none\n        '''\n        # for all the columns, make an empty list\n        for i in range(self.columns):\n            self.temp_list = []\n\n            # for each row, put a \"O\" for empty\n            for j in range(self.rows):\n                self.temp_list.append(\"O\")\n\n            # add each full column to the main board list\n            self.board.append(self.temp_list)\n\n        # once finished, draw the board\n        self.draw_board()\n\n    \n    def draw_board(self):\n        '''\n        Method: draw_board\n        Parameters: none\n        '''\n        # for each column in the range of board, draw the column\n        for col in range(len(self.board)):\n\n            self.draw_column(col)\n    \n    \n    def draw_column(self, col):\n        '''\n        Method: draw_column\n        Parameters: col (int)\n        '''\n        # for each row in the column...\n        for i in range(len(self.board[col])):\n\n            # create turtle and change coordinates according to the row value\n            self.col_coord = (self.width / 2) * -1 + (100 * col) + 100 \n            self.row_coord = (self.height / 2) - (100 * i) - 200\n            self.new_dot = turtle.Turtle()\n            self.new_dot.speed(10)\n            self.new_dot.penup()\n            self.new_dot.goto(self.col_coord, self.row_coord)\n\n            # depending on player value, add shape to the board\n            if self.board[col][i] == \"O\":\n                self.new_dot.shape(\"empty.gif\")\n                \n            elif self.board[col][i] == \"R\":\n                self.new_dot.shape(\"red_1.gif\")\n\n            elif self.board[col][i] == \"B\":\n                self.new_dot.shape(\"blue_1.gif\")\n\n\n    def update_board(self, selection, color):\n        '''\n        Method: update_board\n        Parameters: selection (int), color (str)\n        '''\n        self.color = color\n        self.selection = selection\n\n        # for each row in the column, iterate backwards from the bottom\n        for i in range(len(self.board[selection])-1, -1, -1):\n\n            # if it's empty add player's letter, otherwise continue through the list\n            if self.board[selection][i] == \"O\":\n                self.board[selection][i] = self.color\n                break\n\n        # then draw the column for the user\n        self.draw_column(self.selection)\n\n\n    def is_full(self):\n        '''\n        Method: is_full\n        Parameters: none\n        '''\n        count = 0\n\n        # cound all of the \"O\"s in the board\n        for i in range(len(self.board)):\n            for j in range(len(self.board[i])):\n                if self.board[i][j] == \"O\":\n                    count += 1\n\n        return count\n        \n","repo_name":"tolliverdani/5001-Foundations-CS-Homework","sub_path":"8_connect four/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":3380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28440511284","text":"import os\r\nimport BDAstronomieUtils as BDU\r\nfrom configDB import dbConnect\r\n\r\n\"\"\" FONCTIONS POUR CLEAR COMPLET AVEC MENU \"\"\"\r\n\r\ndef clearTableMateriel() -> None:\r\n    BDU.clearMateriel()\r\n    print(\"\\n*** Done.\\n\")\r\n\r\ndef clearTableObjet() -> None:\r\n    BDU.clearObjet()\r\n    print(\"\\n*** Done.\\n\")\r\n\r\ndef clearTableObserve() -> None:\r\n    BDU.clearObserve()\r\n    print(\"\\n*** Done.\\n\")\r\n\r\ndef clearTableProposition() -> None:\r\n    BDU.clearProposition()\r\n    print(\"\\n*** Done.\\n\")\r\n\r\ndef printMateriel() -> None:\r\n    print(BDU.getMateriel())\r\n\r\ndef printObjet() -> None:\r\n    print(BDU.getObjet())\r\n\r\ndef printObservation() -> None:\r\n    print(BDU.getObserve())\r\n\r\ndef printProposition() -> None:\r\n    print(BDU.getProposition())\r\n\r\n\"\"\" FONCTION D'INSERTION DE DONNEES MANUELLEMENT \"\"\"\r\n\r\ndef addMateriel() -> None:\r\n    \"\"\" Ajout de materiel \"\"\"\r\n    id_materiel_req = int(input(\"Identifiant matériel (-1 pour AUTO INCREMENT): \"))\r\n    type_req = input(\"Type de matériel : \")\r\n    prix_req = int(input(\"Prix du matériel : \"))\r\n    try:\r\n        if id_materiel_req == -1:\r\n            id_materiel_req = \"default\"\r\n        BDU.insertMateriel(id_materiel_req, type_req, prix_req)\r\n    except Exception as e:\r\n        print(f\"Error : {e}\")\r\n\r\ndef addObjet() -> None:\r\n    \"\"\" Ajout d'un objet \"\"\"\r\n    id_objet_req = int(input(\"Identifiant objet (-1 pour AUTO INCREMENT): \"))\r\n    nom_req = input(\"Nom de l'astre : \")\r\n    type_req = input(\"Type de l'astre : \")\r\n    distanceTerre_req  = input(\"Distance de l'astre à la Terre (ex : 140 000 km, peut être vide) : \")\r\n    DistanceSoleil_req  = input(\"Distance de l'astre au Soleil (ex : 140 000 km) : \")\r\n    DistanceGalaxie_req  = input(\"Distance de l'astre à notre Galaxie (ex : 140 000 km, peut être vide) : \")\r\n    luminosite_req = input(\"Luminosité de l'astre (ex : 100, peut être vide) : \")\r\n    vitesse_req  = input(\"Vitess de l'astre en m/s (ex: 430, peut être vide) : \")\r\n    age_req = input(\"Âge de l'astre (ex : 140 millions d'années) : \")\r\n    try:\r\n        if id_objet_req == -1:\r\n            id_objet_req = \"default\"\r\n        distanceTerre_req = None if distanceTerre_req == '' else distanceTerre_req\r\n        DistanceGalaxie_req = None if DistanceGalaxie_req == '' else DistanceGalaxie_req\r\n        luminosite_req = None if luminosite_req == '' else luminosite_req\r\n        vitesse_req = None if vitesse_req == '' else vitesse_req\r\n        BDU.insertObjet(id_objet_req, nom_req, type_req, distanceTerre_req, DistanceSoleil_req, DistanceGalaxie_req, luminosite_req, vitesse_req, age_req)\r\n    except Exception as e:\r\n        print(f\"Error : {e}\")\r\n\r\ndef addObservation() -> None:\r\n    \"\"\" Ajout d'une observation \"\"\"\r\n    id_observation_req = int(input(\"Identifiant d'observation (-1 pour AUTO INCREMENT): \"))\r\n    jour_req = input(\"Jour de l'observation : \")\r\n    mois_req = input(\"Mois de l'observation : \")\r\n    annee_req = input(\"Année de l'observation : \")\r\n    date = f\"{int(annee_req):04d}-{int(mois_req):02d}-{int(jour_req):02d}\"\r\n    lieu_req = input(\"Lieu de l'observation : \")\r\n    observatoire_req  = input(\"Observatoire visité (peut être vide) : \")\r\n    filmObjet_req  = input(\"Film en rapport avec l'observation (peut être vide) : \")\r\n    type_req  = input(\"Type de l'observation (soirée/journée) : \")\r\n    print(\"\\nRappel : {}\\n\".format(BDU.getMateriel()))\r\n    materiel_req = input(\"Materiel utilisé (Identifiant matériel, peut être vide) : \")\r\n    print(\"\\nRappel : {}\\n\".format(BDU.getObjet()))\r\n    objetObserve_req  = input(\"Objet observé (Identifiant objet, peut être vide) : \")\r\n    try:\r\n        if id_observation_req == -1:\r\n            id_observation_req = \"default\"\r\n        observatoire_req = None if observatoire_req == '' else observatoire_req\r\n        filmObjet_req = None if filmObjet_req == '' else filmObjet_req\r\n        materiel_req = None if materiel_req == '' else materiel_req\r\n        objetObserve_req = None if objetObserve_req == '' else objetObserve_req\r\n        BDU.insertObserve(id_observation_req, date, lieu_req, observatoire_req, filmObjet_req, type_req, materiel_req, objetObserve_req)\r\n    except Exception as e:\r\n        print(f\"Error : {e}\")\r\n\r\ndef addProposition() -> None:\r\n    \"\"\" Ajout d'une observation \"\"\"\r\n    id_proposition_req = int(input(\"Identifiant de la proposition (-1 pour AUTO INCREMENT): \"))\r\n    prenom_req = input(\"Prénom : \")\r\n    nom_req = input(\"Nom : \")\r\n    jour_req = input(\"Jour de l'observation : \")\r\n    mois_req = input(\"Mois de l'observation : \")\r\n    annee_req = input(\"Année de l'observation : \")\r\n    date = f\"{int(annee_req):04d}-{int(mois_req):02d}-{int(jour_req):02d}\"\r\n    lieu_req = input(\"Lieu de l'observation : \")\r\n    heure_req  = input(\"Heure de l'événement (ex : 15h) : \")\r\n    print(\"\\nRappel : {}\\n\".format(BDU.getMateriel()))\r\n    materiel_req = input(\"Materiel utilisé (Identifiant matériel, peut être vide) : \")\r\n    print(\"\\nRappel : {}\\n\".format(BDU.getObjet()))\r\n    objetObserve_req  = input(\"Objet observé (Identifiant objet, peut être vide) : \")\r\n    try:\r\n        if id_proposition_req == -1:\r\n            id_proposition_req = \"default\"\r\n        materiel_req = None if materiel_req == '' else materiel_req\r\n        objetObserve_req = None if objetObserve_req == '' else objetObserve_req\r\n        BDU.insertProposition(id_proposition_req, prenom_req, nom_req, date, lieu_req, heure_req, materiel_req, objetObserve_req)\r\n    except Exception as e:\r\n        print(f\"Error : {e}\")\r\n\r\n\"\"\" FONCTION SUPPRIMER UN ITEM PAR SON ID \"\"\"\r\n\r\ndef removeMaterielById() -> None:\r\n    \"\"\" supprime du materiel par son numéro de clé primaire \"\"\"\r\n    printMateriel()\r\n    id = int(input(\"Entrez le numéro du matériel : \"))\r\n    try:\r\n        BDU.deleteMateriel(id)\r\n        print(\"\\n*** Done.\\n\")\r\n    except ValueError as e:\r\n        print(f\"\\n*** ERREUR : {e}\\n\")\r\n    printMateriel()\r\n\r\ndef removeObjetById() -> None:\r\n    \"\"\" supprime des objets par leur numéro de clé primaire \"\"\"\r\n    printObjet()\r\n    id = int(input(\"Entrez le numéro de l'objet : \"))\r\n    try:\r\n        BDU.deleteObjet(id)\r\n        print(\"\\n*** Done.\\n\")\r\n    except ValueError as e:\r\n        print(f\"\\n*** ERREUR : {e}\\n\")\r\n    printObjet()\r\n\r\ndef removeObservationById() -> None:\r\n    \"\"\" supprime des observation par leur numéro de clé primaire \"\"\"\r\n    printObservation()\r\n    id = int(input(\"Entrez le numéro de l'observation : \"))\r\n    try:\r\n        BDU.deleteObserve(id)\r\n        print(\"\\n*** Done.\\n\")\r\n    except ValueError as e:\r\n        print(f\"\\n*** ERREUR : {e}\\n\")\r\n    printObservation()\r\n\r\ndef removePropositionById() -> None:\r\n    \"\"\" supprime des proposition par leur numéro de clé primaire \"\"\"\r\n    printProposition()\r\n    id = int(input(\"Entrez le numéro de la proposition : \"))\r\n    try:\r\n        BDU.deleteProposition(id)\r\n        print(\"\\n*** Done.\\n\")\r\n    except ValueError as e:\r\n        print(f\"\\n*** ERREUR : {e}\\n\")\r\n    printProposition()\r\n\r\n\"\"\" FONCTION D'AFFICHAGE DE MENU \"\"\"\r\n\r\ndef afficheMenu(choixActions : list ) -> None :\r\n    \"\"\" Affichage du menu \"\"\"\r\n    print (\"Choix possibles :\")\r\n    for ch  in choixActions:\r\n        print (f'{choixActions.index(ch)+1} : {ch[0]}')\r\n    print (f'{len(choixActions)+1} : Quitter')\r\n\r\ndef afficheMenuCore(liste) -> None:\r\n\r\n    while True :\r\n        afficheMenu(liste)\r\n        try :\r\n            choix = int(input(\"\\nVotre Choix ? : \"))\r\n            if ( choix == len(liste) + 1 ):\r\n                break\r\n            elif 1 <= choix and choix <= len(liste):\r\n                label, fct = liste[choix-1]\r\n                fct()\r\n            else :\r\n                print (\"*** Choix non valide, recommencez\")\r\n        except IndexError as e:\r\n            print(e)\r\n            print ('*** Choix non valide, recommencez')\r\n        except ValueError as e :\r\n            print(e)\r\n            print ('*** Erreur, entrez un entier.')\r\n\r\n\"\"\" FONCTION DE CHARGEMENT DE DONNEES PAR CSV\"\"\"\r\n\r\ndef loadFromCSV() -> None:\r\n    import glob\r\n    \"\"\" Demande quel fichier charger pour une table puis importe les données \"\"\"\r\n    csvFile = r'*.csv'\r\n    print(f\"Les fichiers CSV disponibles sont :\")\r\n    \r\n    listeCSV = glob.glob(csvFile)\r\n    for i, csv in enumerate(listeCSV, start=1):\r\n        print(f\"{i} : Charger le fichier : {csv}\")\r\n\r\n    choix = int(input(\"\\nVotre choix ? : \"))\r\n\r\n    if choix > 0 and choix <= len(listeCSV):\r\n        fichierChoisi = listeCSV[choix - 1]\r\n        print(f\"Vous avez choisi le fichier : {fichierChoisi}\")\r\n        if fichierChoisi == \"materiel.csv\":\r\n            BDU.loadMaterielFromCSVFile()\r\n        elif fichierChoisi == \"objet.csv\":\r\n            BDU.loadObjetFromCSVFile()\r\n        elif fichierChoisi == \"observation.csv\":\r\n            BDU.loadObserveFromCSVFile()\r\n        elif fichierChoisi == \"proposition.csv\":\r\n                BDU.loadPropositionFromCSVFile()\r\n    else:\r\n        print(\"Choix invalide.\")\r\n\r\ndef dropBase() -> None:\r\n    try:\r\n        BDU.dropDataBase()\r\n        print(\"\\n*** Done.\\n\")\r\n        BDU.initBase()\r\n    except Exception as e:\r\n        print(f\"Error : {e}\")\r\n\r\n\"\"\" FONCTION PERMETTANT DE MODIFIER TOUTES LES TABLES\"\"\"\r\n\r\ndef getAllTables() -> None:\r\n    \"\"\" Affiche les tables de la base de données \"\"\"\r\n    tables = BDU.getTables()\r\n    for i, table in enumerate(tables, start=1):\r\n        table_name = str(table).replace(\"(\", \"\").replace(\")\", \"\").replace(\"'\", \"\").replace(\",\", \"\")\r\n        print(f\"{i} : Table : {table_name}\")\r\n\r\n    choix = int(input(\"\\nVotre choix ? : \"))\r\n\r\n    if choix > 0 and choix <= len(BDU.getTables()):\r\n        tableChoisie = tables[choix - 1]\r\n        table_name = str(tableChoisie).replace(\"(\", \"\").replace(\")\", \"\").replace(\"'\", \"\").replace(\",\", \"\")\r\n        print(f\"Vous avez choisi la table : {table_name}\")\r\n        if table_name == \"materiel\":\r\n            listeMateriel = [(\"Afficher la table Materiel\",printMateriel),\r\n                             (\"Vider la table Materiel\", clearTableMateriel),\r\n                             (\"Supprimer du materiel (par son numéro)\", removeMaterielById),\r\n                             (\"Charger la table avec un fichier (CSV)\", BDU.loadMaterielFromCSVFile),\r\n                             (\"Insérer un élément matériel\", addMateriel)\r\n                                ]\r\n            afficheMenuCore(listeMateriel)\r\n        elif table_name == \"objet\":\r\n            listeObjet = [(\"Afficher la table Objet\", printObjet),\r\n                          (\"Vider la table Objet\", clearTableObjet),\r\n                          (\"Supprimer des objets (par leur numéro)\", removeObjetById),\r\n                          (\"Charger la table avec un fichier (CSV)\", BDU.loadObjetFromCSVFile),\r\n                          (\"Insérer un objet céleste\", addObjet)\r\n                            ]\r\n            afficheMenuCore(listeObjet)\r\n        elif table_name == \"observation\":\r\n            listeObservation = [(\"Afficher la table Observation\", printObservation),\r\n                                (\"Vider la table Observation\", clearTableObserve),\r\n                                (\"Supprimer des Observation (par leur numéro)\", removeObservationById),\r\n                                (\"Charger la table avec un fichier (CSV)\", BDU.loadObserveFromCSVFile),\r\n                                (\"Insérer une observation\", addObservation)\r\n                                ]\r\n            afficheMenuCore(listeObservation)\r\n        elif table_name == \"proposition\":\r\n            listeProposition = [(\"Afficher la table Proposition\", printProposition),\r\n                                (\"Vider la table Proposition\", clearTableProposition),\r\n                                (\"Supprimer des Proposition (par leur numéro)\", removePropositionById),\r\n                                (\"Charger la table avec un fichier (CSV)\", BDU.loadPropositionFromCSVFile),\r\n                                (\"Insérer une proposition\", addProposition)\r\n                                ]\r\n            afficheMenuCore(listeProposition)\r\n    else:\r\n        print(\"Choix invalide.\")\r\n\r\nif __name__ == '__main__':\r\n    import glob\r\n    csvFile = r'*.csv'\r\n    BDU.initBase()\r\n    listeChoix1 = [\r\n             (\"Charger un fichier (CSV)\",loadFromCSV),\r\n             (\"Afficher la table Materiel\",printMateriel),\r\n             (\"Afficher la table Objet\", printObjet),\r\n             (\"Afficher la table Observation\", printObservation),\r\n             (\"Afficher la table Proposition\", printProposition),\r\n             (\"Modifier des tables\", getAllTables),\r\n             (\"Supprimer la base de données\", dropBase)\r\n            ]\r\n    \r\n    while True :\r\n        afficheMenu(listeChoix1)\r\n        try :\r\n            choix = int(input(\"\\nVotre Choix ? : \"))\r\n            if ( choix == len(listeChoix1) + 1 ):\r\n                    break\r\n            elif 1 <= choix and choix <= len(listeChoix1):\r\n                label, fct = listeChoix1[choix-1]\r\n                fct()\r\n            else :\r\n                print (\"*** Choix non valide, recommencez\")\r\n        except IndexError as e:\r\n            print(e)\r\n            print ('*** Choix non valide, recommencez')\r\n        except ValueError as e :\r\n            print(e)\r\n            print ('*** Erreur, entrez un entier.')\r\n    print (\"A bientôt !\")","repo_name":"outsyde/Beaudi_Mathis_SAE23_final","sub_path":"AstronomieCLI.py","file_name":"AstronomieCLI.py","file_ext":"py","file_size_in_byte":13238,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29447138875","text":"# Description\n# Given a string containing only digits, restore it by returning all possible valid IP address combinations.\n\n# (Your task is to add three dots to this string to make it a valid IP address. Return all possible IP address.)\n\n# You can return all valid IP address in any order.\n\n# Example\n# Example 1:\n\n# Input: \"25525511135\"\n# Output: [\"255.255.11.135\", \"255.255.111.35\"]\n# Explanation: [\"255.255.111.35\", \"255.255.11.135\"] will be accepted as well.\n# Example 2:\n\n# Input: \"1116512311\"\n# Output: [\"11.165.123.11\",\"111.65.123.11\"]\n\nfrom typing import (\n    List,\n)\n\nclass Solution:\n    \"\"\"\n    @param s: the IP string\n    @return: All possible valid IP addresses\n    \"\"\"\n    def restore_ip_addresses(self, s: str) -> List[str]:\n        result = []\n        self.helper(s, [], result)\n        return result\n\n    def helper(self, s, path, result):\n        # exit.\n        if len(path) == 4 and s == '':\n            result.append('.'.join(path))\n            return\n\n        if len(path) > 4:\n            return\n\n        for i in range(1, 4):\n            if i > len(s):\n                break\n\n            curr_str = s[:i]\n            # take care the leading 0s\n            to_num = int(curr_str)\n            to_str = str(to_num)\n            if len(curr_str) != len(to_str):\n                continue\n\n            if int(curr_str) > 255 or int(curr_str) < 0:\n                continue\n\n            path.append(curr_str)\n            self.helper(s[i:], path, result)\n            path.pop()\n\n\n\n\n\n\n\n","repo_name":"jerry-jma/Data-Structure-and-Algorithm-Python3","sub_path":"DFS/Restore IP Addresses.py","file_name":"Restore IP Addresses.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37030671163","text":"import sys, curses\n\nimport psycopg2 as pg2\nimport cPickle as pickle\nimport xmltodict\n\nfrom beatport_api import beatport\nfrom time import time, sleep\n\nclass collectionlister(object):\n    '''\n    Class to build track list from M3U8 generated by Rekordbox\n\n    INPUT\n        fname - M3U8 file name, STR\n    '''\n\n    def __init__(self, fname):\n        self.filename = fname\n\n    def read_file(self):\n        '''\n        Read lines of file and store\n        '''\n        self.lines = []\n        string = ''\n        with open(self.filename, 'r') as f:\n\n            for line in f.read():\n\n                string += line\n\n                if line == '\\n':\n                    self.lines.append(string)\n                    string = ''\n\n        return\n\n    def read_tracks(self):\n        '''\n        Transform file into better format\n        '''\n        soln = []\n\n        for track in self.lines:\n            if track[0:7] == '#EXTINF':\n                pos = track.find(',') + 1\n                soln.append(track[pos:])\n\n        self.tracks = self.strip_return(soln)\n\n        return\n\n    def strip_return(self, tracks):\n        '''\n        Remove the carriage return from last track\n        '''\n\n        tracks = [track.replace('\\r\\n','') for track in tracks]\n\n        return(tracks)\n\n    def build(self):\n        '''\n        Build track list and return list\n        '''\n        self.collection = []\n\n        self.read_file()\n\n        self.read_tracks()\n\n        for track in self.tracks:\n            trk = track.split(' - ')\n            self.collection.append(trk)\n\n        return\n\nclass collectiontagger(object):\n    '''\n    Class to harmonize Rekordbox collection to Beatport artist and track ids\n\n    INPUT\n        beatport - Initialized Beatport class, CLASS\n        collection - List of artist and track list, LIST\n    '''\n\n    def __init__(self, beatport, collection):\n        self.bprt = beatport\n        self.rkbx_col = collection\n\n    def sql_connect(self):\n        '''\n        Connect to Postgres database with Beatport data\n        '''\n        conn = pg2.connect('dbname=beatport user=danius')\n        cur = conn.cursor()\n        return(conn, cur)\n\n\n    def _setup_progress_bar(self, num):\n        '''\n        Setup counter for long search processes\n        '''\n        curses.initscr()\n        curses.curs_set(0)\n\n        sentence = 'Track {} of {} complete.' \\\n                     .format(' '*len(str(num)), str(num))\n\n        sys.stdout.write(sentence)\n        sys.stdout.flush()\n\n        sys.stdout.write('\\b' * (len(sentence) - 6))\n\n        return\n\n    def _update_progress_bar(self, curr):\n        '''\n        Update counter with current iteration\n        '''\n        pg = str(curr)\n\n        sys.stdout.write(pg)\n        sys.stdout.flush()\n        sys.stdout.write('\\b' * len(pg))\n\n        return\n\n    def _escape_progress_bar(self):\n        '''\n        Properly exit the counter\n        '''\n        sys.stdout.write('\\n')\n        curses.curs_set(1)\n        curses.reset_shell_mode()\n        return\n\n    def _convert_raw_artist(self, artists):\n        '''\n        Split artist string into individuals or producer pair\n\n        INPUT\n            artists - artist(s) name(s) for a track, STR\n        OUTPUT\n            soln - list of each artist in string, LIST\n        '''\n\n        artists = artists.replace(', ','*') \\\n                         .replace(' feat ','*') \\\n                         .replace(' feat. ','*') \\\n                         .replace(' ft ','*') \\\n                         .replace(' ft. ','*') \\\n                         .lower() \\\n                         .split('*')\n        soln = []\n        for a in artists:\n            soln.append(a)\n\n            if ' & ' in a:\n                soln.extend(a.split(' & '))\n\n        return(soln)\n\n    def _artist_set(self, tracks):\n        '''\n        Clean up artist names and build set\n\n        INPUT\n            tracks - tracks in collection with\n                                each track as list [artist, track], LIST\n        OUTPUT\n            artists - unique artists in collection, SET\n        '''\n        artists = set()\n\n        for track in tracks:\n            track = self._convert_raw_artist(track[0])\n\n            for art in track:\n                artists.add(art)\n\n        return(artists)\n\n    def _beatport_artists(self):\n        '''\n        Fetch all artists and ids from Postgres\n\n        INPUT\n            None\n        OUTPUT\n            dict - dictionary of artist name (key) and id (val), DICT\n        '''\n        conn, cur = self.sql_connect()\n\n        cur.execute('SELECT * FROM bprt_artist;')\n        data = cur.fetchall()\n\n        cur.close()\n        conn.close()\n\n        return({val[1] : val[0] for val in data})\n\n    def _recode_tracks(self):\n        '''\n        Recode collection into artist id and track name\n\n        INPUT\n\n        OUTPUT\n            r_tracks - tracks in collection with\n                                each track as list [artist_id, track], LIST\n        '''\n        r_tracks = []\n        for trk in self.rkbx_col:\n            arts = self._convert_raw_artist(trk[0])\n\n            for a in arts:\n                if self.tag_art.has_key(a):\n                    r_tracks.append([self.tag_art[a], trk[1]])\n\n        return(r_tracks)\n\n    def tag_artists(self):\n        '''\n        Match collection artist to Beatport artist ID\n\n        INPUT\n            None\n        OUTPUT\n            None\n        '''\n        rkbx_art = self._artist_set(self.rkbx_col)\n        bprt_art = self._beatport_artists()\n\n        self.tag_art = {}\n        for val in rkbx_art:\n            if bprt_art.has_key(val):\n                self.tag_art[val] = bprt_art[val]\n\n        self.artist_tag()\n        return\n\n    def artist_tag(self):\n        '''\n        Reverse artist dictionary {id : name}\n        '''\n        self.art_tag = {val : key for key, val in self.tag_art.iteritems()}\n\n        return\n\n    def tag_tracks(self):\n        '''\n        Match collection tracks to Beatport track ID\n        '''\n        self.tag_trk = {}\n        self.no_trk = []\n\n        trks = self._recode_tracks()\n\n        self._setup_progress_bar(len(trks))\n\n        for i, trk in enumerate(trks):\n\n            self._update_progress_bar(i)\n\n            qry = self.bprt.tracks_w_track_terms_artist_id(trk[1], trk[0])\n\n            found = 0\n            i = 0\n\n            while (found == 0) and (i < len(qry)):\n            #A track may appear more than once due to various reasons such\n            #re-release or year-end collection from record label.\n            #Unfortunately, Beatport assigns a new track id each time a\n            #track appears even if it is an identical track.\n                title = trk[1].decode('utf8')\n                len_title = len(title) / 2\n                if (title[:len_title] in qry[i]['name']) or \\\n                   (title[:len_title] in qry[i]['title']):\n\n                    self.tag_trk[qry[i]['id']] = (trk[0], qry[i]['name'], trk[1])\n                    found = 1\n\n                i += 1\n\n        self._escape_progress_bar()\n\n        return\n\n    def track_playcount(self, fname):\n        '''\n        Read XML file and match track ID with collection play count and\n        date added to collection\n        '''\n        self.play_count = {}\n\n        with open(fname, 'r') as fd:\n            doc = xmltodict.parse(fd.read())\n\n        self._setup_progress_bar(len(self.tag_trk))\n\n        i = 1\n\n        for key, val in self.tag_trk.iteritems():\n\n            self._update_progress_bar(i)\n\n            for track in doc['DJ_PLAYLISTS']['COLLECTION']['TRACK']:\n\n                if (val[2].decode('utf8') in track['@Name']) and \\\n                   (self.art_tag[val[0]] in track['@Artist'].lower()):\n\n                    self.play_count[key] = (track['@PlayCount'],\n                                            track['@DateAdded'])\n\n            i += 1\n\n        self._escape_progress_bar()\n        return\n\n\n    def save_artists(self):\n        '''\n        Store list of artists from collection in Postgres\n        '''\n        conn, cur = self.sql_connect()\n\n        cur.execute('DROP TABLE IF EXISTS rkbx_artist')\n        cur.execute('''CREATE TABLE rkbx_artist (id INTEGER PRIMARY KEY)''')\n        conn.commit()\n\n        for key, val in self.tag_art.iteritems():\n            cur.execute('INSERT INTO rkbx_artist (id) VALUES (%s)', [val])\n\n        conn.commit()\n\n        cur.close()\n        conn.close()\n        return\n\n    def save_tracks(self):\n        '''\n        Store list of artists from collection in Postgres\n        '''\n        conn, cur = self.sql_connect()\n\n        cur.execute('DROP TABLE IF EXISTS rkbx_tracks')\n        cur.execute('''CREATE TABLE rkbx_tracks (id INTEGER PRIMARY KEY,\n                                               artist_id INTEGER,\n                                               bprt_name TEXT,\n                                               col_name TEXT)''')\n        conn.commit()\n\n        for key, val in self.tag_trk.iteritems():\n            cur.execute('''INSERT INTO rkbx_tracks (id,\n                                                  artist_id,\n                                                  bprt_name,\n                                                  col_name)\n                                                  VALUES (%s, %s, %s, %s);''',\n                                                  (key, val[0], val[1], val[2]))\n\n        conn.commit()\n\n        cur.close()\n        conn.close()\n        return\n\n    def save_playcount(self):\n        '''\n        Store play count and date added\n        for each track from collection in Postgres\n        '''\n        conn, cur = self.sql_connect()\n\n        cur.execute('DROP TABLE IF EXISTS rkbx_playcount')\n        cur.execute('''CREATE TABLE rkbx_playcount (id INTEGER PRIMARY KEY,\n                                                  playcount INTEGER,\n                                                  DateAdded DATE)''')\n        conn.commit()\n\n        for key, val in self.play_count.iteritems():\n            cur.execute('''INSERT INTO rkbx_playcount (id,\n                                                     playcount,\n                                                     DateAdded)\n                                                  VALUES (%s, %s, %s);''',\n                                                  (key, val[0], val[1]))\n\n        conn.commit()\n\n        cur.close()\n        conn.close()\n        return\n\n\nif __name__ == '__main__':\n    now = time()\n\n    '''\n    TO DO- Rekordbox playlist doesn't contain track details\n    such as Date Added or Play Count. Rekordbox can export an XML file which\n    contains that information. collectionlister should be refactored to use\n    the XML so all track data can be harmonized at once instead of two passes.\n    '''\n    tracks = collectionlister('../data/my_collection.m3u8')\n    tracks.build()\n\n    bprt = beatport('/Users/danius/galvanize/API/mykeys.yaml')\n    bprt.initialize()\n\n    ritchey = collectiontagger(bprt, tracks.collection)\n    ritchey.tag_artists()\n    ritchey.save_artists()\n    print('Collection Artists Saved')\n\n    ritchey.tag_tracks()\n    ritchey.save_tracks()\n    print('Collection Tracks Saved')\n\n    ritchey.track_playcount('/Users/danius/Documents/Github/beatnet/data/xml_collection_020817.xml')\n    ritchey.save_playcount()\n    print('Collection Metadata Saved')\n\n    later = time()\n\n    t = (later - now) * (1/60.)\n\n    print('Collection tagging took {} minutes'.format(t))\n","repo_name":"silkaitis/new_release_prediction","sub_path":"src/tag_collection.py","file_name":"tag_collection.py","file_ext":"py","file_size_in_byte":11427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25580473652","text":"import os.path\n\nimport logging\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\nfrom googleapiclient.errors import HttpError\n\nfrom RunEventDTO import RunEventDTO\n\nSCOPES = [\"https://www.googleapis.com/auth/spreadsheets.readonly\"]\nRUNS = []\n\n\ndef __init__(self, name):\n    set_gs_reader_logging()\n    self.name = name\n\n\ndef start_gs_reader():\n    creds = None\n    creds = check_token(creds)\n    check_credentials(creds)\n\n    service = create_sheet_service(creds)\n\n    result = get_results(service)\n    # print_result_values(result)\n    # run_event_dto_test_creation()\n\n    create_run_dto(result)\n\n    print(f'RUNS length: {len(RUNS)}')\n\n\n    print(\"Done!\")\n\n\ndef set_gs_reader_logging():\n    logging.basicConfig(\n        filename=\"output.log\",\n        level=logging.DEBUG,\n    )\n\n\ndef check_credentials(creds):\n    logging.debug(\"Checking credentials...\")\n    # If there are no (valid) credentials available, let the user log in.\n    if 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                \"./resources/credentials.json\", SCOPES\n            )\n            creds = flow.run_local_server(port=0)\n        # Save the credentials for the next run\n        with open(\"resources/token.json\", \"w\") as token:\n            token.write(creds.to_json())\n\n\ndef check_token(creds):\n    logging.debug(\"Checking token...\")\n\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.\n    if os.path.exists(\"resources/token.json\"):\n        creds = Credentials.from_authorized_user_file(\"resources/token.json\", SCOPES)\n    return creds\n\n\ndef create_sheet_service(creds):\n    logging.debug(\"Creating sheet service...\")\n\n    try:\n        service = build(\"sheets\", \"v4\", credentials=creds)\n        return service\n    except HttpError as err:\n        print(err)\n\n\ndef get_spreadsheet_id():\n    logging.debug(\"Getting spreadsheet id...\")\n\n    spreadsheet_id = input(\"Please enter spreadsheet id, or press enter for Sample\")\n    if not spreadsheet_id:\n        spreadsheet_id = \"1ixaAZVjxvNWjtUm6Ey41aeKV46VW5lQXmiNDm7nRcjA\"\n    return spreadsheet_id\n\n\ndef get_range_name():\n    logging.debug(\"Getting spreadsheet range...\")\n\n    range_name = input(\"Please enter spreadsheet range, or press enter for Sample\")\n    if not range_name:\n        range_name = \"Sheet1!A2:C\"\n        # Might be a good idea to write functions for sheet name, row, col\n    return range_name\n\n\ndef get_results(service):\n    logging.debug(\"Creating result object...\")\n    spreadsheet_id = get_spreadsheet_id()\n    range_name = get_range_name()\n\n    try:\n        sheet = service.spreadsheets()\n        result = (\n            sheet.values()\n            .get(spreadsheetId=spreadsheet_id, range=range_name)\n            .execute()\n        )\n        return result\n    except HttpError as err:\n        logging.debug(err)\n\n\ndef print_result_values(result):\n    logging.debug(\"Printing result values...\")\n\n    try:\n        values = result.get(\"values\", [])\n        # check_result_length(values)\n        print(\"Day, Date, Run Plan\")\n        for row in values:\n            print(f\"{row}\")\n    except HttpError as err:\n        print(f'Error when printing: {err}')\ndef check_result_length(values):\n    for row in values:\n        if len(row) != 3:\n            print(\"Length of less than three found.\")\n\n\n\ndef create_run_dto(results):\n    \"\"\"\n    There are instances where the sheet being imported will have a value in the day and date column, but with no run.\n    Example: The files ends on a rest day. like 12/6/23;\n        - day : Wednesday\n        - date : 12/6/23\n        - run :\n    Therefore, I need a way to validate the row, including under this scenario.\n    \"\"\"\n\n    values = results.get(\"values\", [])\n\n    for value in values:\n        try:\n            temp_run = value\n            run = RunEventDTO(temp_run[0],temp_run[1],temp_run[2])\n        except IndexError:\n            temp_run = value\n            run = RunEventDTO(temp_run[0],temp_run[1],None)\n        finally:\n            RUNS.append(run)\n            print(\"Run object should have been created.\")\n\n    return\n\ndef validate_final_result(results):\n    print(f'the final result is: {results[-1]}')\n    #if(results[-1])","repo_name":"LiamCahill/GetMyRun","sub_path":"gs_reader.py","file_name":"gs_reader.py","file_ext":"py","file_size_in_byte":4542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22352306916","text":"import gym\nfrom gym import spaces\nimport math\nimport carla\nimport argparse\n\ntry:\n    import numpy as np\n    import sys\n    from os import path as osp\nexcept ImportError:\n    raise RuntimeError('import error!')\nfrom carla_env.sim_carla import SimInit\nfrom carla_env.sim_vehicle import VehicleInit\nfrom utils.common import *\nfrom carla_env.feature import *\n\n\n\nclass CarlaEnv(gym.Env):\n\n    def __init__(self):\n        argparser = argparse.ArgumentParser(description='Carla ArgParser practice')\n        argparser.add_argument('--host', metavar='H', default='127.0.0.1', help='IP of the host server')\n        argparser.add_argument('-p', '--port', default=2000, type=int, help='TCP port to listen to')\n        args = argparser.parse_args()\n\n        self.sim = SimInit(args)\n        self.car = VehicleInit(self.sim)\n        self.sigma = {\"sigma_pos\": 0.3, \"sigma_vel_upper\": 0.6,\n                       \"sigma_vel_lower\": 1.0, \"sigma_yaw\": 0.4}\n\n        self.car_fea_ext = self.car.ego_car_config.fea_ext\n        obs_shape = len(self.car_fea_ext.observation)\n        obs_high = np.array([np.inf] * obs_shape)\n        self.observation_space = spaces.Box(-obs_high, obs_high)\n        self.obs_index = self.car_fea_ext.obs_index\n        self.pre_obs_index = self.car_fea_ext.pre_obs_index\n        self.zombie_num = self.car_fea_ext.zombie_num\n        self.action_space = spaces.Discrete(5)\n\n    def step(self, decision):\n        total_step = 30 if decision == 0 else 3\n        step_num = 0\n        done = False\n        reward = 0\n        print(\"Step Decision:\", decision)\n        while step_num < total_step or self.car_fea_ext.vehicle_info.status != STATUS.FOLLOWING:\n            fasle_dec = self.car.step_decision(decision)\n            self.car_fea_ext = self.car.ego_car_config.fea_ext\n            self.zombie_num = self.car_fea_ext.zombie_num\n            self.sim.update()\n            step_num += 1\n            done = True if self.sim.term_check() is True or fasle_dec is True else False\n            reward = self._get_reward(self.sigma)\n\n            if done:\n                break\n        ob = self._get_obs()\n        print(\"Reward:\", reward)\n        print(\"DONE?\", done)\n        print(\"Time cost:\", step_num * 0.1)\n        return ob, reward, done, {}\n\n    def reset(self):\n        self.sim.reset()\n        self.car.reset(self.sim)\n        self.sim.update()\n        ob = self._get_obs()\n        return ob\n\n    def test_step(self):\n        self.sim.update()\n        self.car.rule_based_step()\n        ob = self._get_obs()\n        reward = self._get_reward(self.sigma)\n        done = False if self.sim.term_check() else True\n        return ob, reward, done, {}\n\n    def _get_obs(self):\n        return self.car_fea_ext.observation\n\n    def _get_reward(self, sigmas):\n        [rx, ry, ryaw, vel_des] = self.car.reference\n        car_v = self.car_fea_ext.vehicle_info.v\n\n        # velocity reward\n        sigma_vel_upper, sigma_vel_lower = sigmas[\"sigma_vel_upper\"], sigmas[\"sigma_vel_lower\"]\n        sigma_vel = sigma_vel_upper if car_v <= vel_des else sigma_vel_lower\n        v_err = car_v - vel_des\n        v_rewd = np.exp(-v_err**2 / (2*sigma_vel**2))\n\n        accident_cost = -10 if self.sim.collision_event is True else 0\n        reward = v_rewd + accident_cost\n\n        return reward\n","repo_name":"BOmmBAO/Imitation_driving_MT","sub_path":"rl_algorithm/PETS_decision/dmbrl/env/carla.py","file_name":"carla.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23609480992","text":"from collections import defaultdict\nfrom tool.runners.python import SubmissionPy\n\n\ndef _explore(g, start):\n    Q = [(start, 0)]\n    res = 0\n    seen = set()\n    while Q:\n        current, lvl = Q.pop()\n        res += lvl\n        seen.add(current)\n        for child in g[current]:\n            if child not in seen:\n                Q.append((child, lvl+1))\n    return res\n\n\nclass AyoubSubmission(SubmissionPy):\n    def run(self, s):\n        g = defaultdict(list)\n        for l in s.split('\\n'):\n            a, b = l.strip().split(')')\n            g[a].append(b)\n        return _explore(g, 'COM')\n","repo_name":"lypnol/adventofcode-2019","sub_path":"day-06/part-1/ayoub.py","file_name":"ayoub.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"9333906605","text":"from abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nfrom typing import Any, TypeVar\n\nfrom brayns.network import Instance\nfrom brayns.utils import Color4\n\nT = TypeVar(\"T\", bound=\"Renderer\")\n\n\n@dataclass\nclass Renderer(ABC):\n    \"\"\"Base class for all renderer types.\n\n    Accumulation reduce the aliasing but increase the computation time.\n\n    Ray bounces allow non-emissive objects to light other objects.\n\n    :param samples_per_pixel: Accumulation, defaults to 1.\n    :type samples_per_pixel: int, optional.\n    :param max_ray_bounces: Ray bounces, defaults to 3.\n    :type max_ray_bounces: int, optional.\n    :param background_color: Background color, defaults to BBP transparent.\n    :type background_color: Color4, optional.\n    \"\"\"\n\n    samples_per_pixel: int = 1\n    max_ray_bounces: int = 3\n    background_color: Color4 = Color4.bbp_background.transparent\n\n    @classmethod\n    @property\n    @abstractmethod\n    def name(cls) -> str:\n        \"\"\"Get renderer name.\n\n        :return: Renderer name.\n        :rtype: str\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def get_additional_properties(self) -> dict[str, Any]:\n        \"\"\"Low level API to serialize to JSON.\"\"\"\n        pass\n\n    @abstractmethod\n    def update_additional_properties(self, message: dict[str, Any]) -> None:\n        \"\"\"Low level API to deserialize from JSON.\"\"\"\n        pass\n\n    def get_properties(self) -> dict[str, Any]:\n        \"\"\"Low level API to serialize to JSON.\"\"\"\n        return {\n            \"samples_per_pixel\": self.samples_per_pixel,\n            \"max_ray_bounces\": self.max_ray_bounces,\n            \"background_color\": list(self.background_color),\n            **self.get_additional_properties(),\n        }\n\n    @classmethod\n    def from_properties(cls: type[T], message: dict[str, Any]) -> T:\n        \"\"\"Low level API to deserialize from JSON.\"\"\"\n        renderer = cls()\n        renderer.update_properties(message)\n        return renderer\n\n    def get_properties_with_name(self) -> dict[str, Any]:\n        \"\"\"Low level API to serialize to JSON.\"\"\"\n        return {\n            \"name\": self.name,\n            \"params\": self.get_properties(),\n        }\n\n    def update_properties(self, message: dict[str, Any]) -> None:\n        \"\"\"Low level API to deserialize from JSON.\"\"\"\n        self.samples_per_pixel = message[\"samples_per_pixel\"]\n        self.max_ray_bounces = message[\"max_ray_bounces\"]\n        self.background_color = Color4(*message[\"background_color\"])\n        self.update_additional_properties(message)\n\n\n@dataclass\nclass InteractiveRenderer(Renderer):\n    \"\"\"Default renderer used for fast rendering (streaming, tests).\n\n    :param enable_shadows: Enable shadows, defaults to True.\n    :type enable_shadows: bool, optional.\n    :param ambient_occlusion_samples: AO samples, defaults to 0.\n    :type ambient_occlusion_samples: int, optional.\n    \"\"\"\n\n    enable_shadows: bool = True\n    ambient_occlusion_samples: int = 0\n\n    @classmethod\n    @property\n    def name(cls) -> str:\n        \"\"\"Get renderer name.\n\n        :return: Renderer name.\n        :rtype: str\n        \"\"\"\n        return \"interactive\"\n\n    def get_additional_properties(self) -> dict[str, Any]:\n        \"\"\"Low level API to serialize to JSON.\"\"\"\n        return {\n            \"enable_shadows\": self.enable_shadows,\n            \"ao_samples\": self.ambient_occlusion_samples,\n        }\n\n    def update_additional_properties(self, message: dict[str, Any]) -> None:\n        \"\"\"Low level API to deserialize from JSON.\"\"\"\n        self.enable_shadows = message[\"enable_shadows\"]\n        self.ambient_occlusion_samples = message[\"ao_samples\"]\n\n\n@dataclass\nclass ProductionRenderer(Renderer):\n    \"\"\"Production renderer for expensive quality rendering.\n\n    Overrides default parameters for higher quality.\n\n    :param samples_per_pixel: Accumulation, defaults to 128.\n    :type samples_per_pixel: int, optional.\n    :param max_ray_bounces: Ray bounces, defaults to 7.\n    :type max_ray_bounces: int, optional.\n    \"\"\"\n\n    samples_per_pixel: int = 128\n    max_ray_bounces: int = 7\n\n    @classmethod\n    @property\n    def name(cls) -> str:\n        \"\"\"Get renderer name.\n\n        :return: Renderer name.\n        :rtype: str\n        \"\"\"\n        return \"production\"\n\n    def get_additional_properties(self) -> dict[str, Any]:\n        \"\"\"Low level API to serialize to JSON.\"\"\"\n        return {}\n\n    def update_additional_properties(self, message: dict[str, Any]) -> None:\n        \"\"\"Low level API to deserialize from JSON.\"\"\"\n        pass\n\n\ndef get_renderer_name(instance: Instance) -> str:\n    \"\"\"Retreive the name of the current renderer of an instance.\n\n    :param instance: Instance.\n    :type instance: Instance\n    :return: Renderer name.\n    :rtype: str\n    \"\"\"\n    return instance.request(\"get-renderer-type\")\n\n\ndef get_renderer(instance: Instance, renderer_type: type[T]) -> T:\n    \"\"\"Retreive the current renderer from an instance.\n\n    Current renderer and given renderer type must be the same.\n\n    Current renderer can be queried using ``get_rendere_name``.\n\n    :param instance: Instance.\n    :type instance: Instance\n    :param renderer_type: Type of the current renderer.\n    :type renderer_type: type[T]\n    :return: Current renderer.\n    :rtype: T\n    \"\"\"\n    name = renderer_type.name\n    result = instance.request(f\"get-renderer-{name}\")\n    return renderer_type.from_properties(result)\n\n\ndef set_renderer(instance: Instance, renderer: Renderer) -> None:\n    \"\"\"Set the current renderer of an instance.\n\n    :param instance: Instance.\n    :type instance: Instance\n    :param renderer: New renderer.\n    :type renderer: Renderer\n    \"\"\"\n    name = renderer.name\n    params = renderer.get_properties()\n    instance.request(f\"set-renderer-{name}\", params)\n","repo_name":"BlueBrain/Brayns","sub_path":"python/brayns/core/renderer.py","file_name":"renderer.py","file_ext":"py","file_size_in_byte":5752,"program_lang":"python","lang":"en","doc_type":"code","stars":284,"dataset":"github-code","pt":"77"}
+{"seq_id":"13471008185","text":"from tkinter import *\n\nequa = \"\"\ndata = \"\"\ndef btnPress(num):\n    global equa\n    equa = equa + str(num)\n    equation.set(equa)\n\ndef operPress(op):\n    global equa\n    if len(equa) != 0:\n        btnPress(op)\n    else:\n        equa = data + str(op)\n        equation.set(equa)\n\ndef evaluate():\n    global equa\n    global data\n    data = str(eval(equa))\n    equation.set(equa + \" = \" + data)\n    equa = \"\"\n\ndef clear():\n    global equa\n    equa=\"\"\n    equation.set(equa)\n\nmaster = Tk()\n\nroot = Frame(master, padx=20, pady=10, bg=\"white\")\nroot.grid()\n\n# Equation label\nequation = StringVar()\ncalculation = Label(root, textvariable=equation,  bg=\"white\")\nequation.set(\"\")\ncalculation.grid(columnspan=4, sticky=E)\n\nbutton1 = Button(root, text=\"1\", command=lambda: btnPress(1), font=(\"\",18))\nbutton1.grid(row=1, column=0)\nbutton2 = Button(root, text=\"2\", command=lambda: btnPress(2), font=(\"\",18))\nbutton2.grid(row=1, column=1)\nbutton3 = Button(root, text=\"3\", command=lambda: btnPress(3), font=(\"\",18))\nbutton3.grid(row=1, column=2)\nbutton4 = Button(root, text=\"4\", command=lambda: btnPress(4), font=(\"\",18))\nbutton4.grid(row=2, column=0)\nbutton5 = Button(root, text=\"5\", command=lambda: btnPress(5), font=(\"\",18))\nbutton5.grid(row=2, column=1)\nbutton6 = Button(root, text=\"6\", command=lambda: btnPress(6), font=(\"\",18))\nbutton6.grid(row=2, column=2)\nbutton7 = Button(root, text=\"7\", command=lambda: btnPress(7), font=(\"\",18))\nbutton7.grid(row=3, column=0)\nbutton8 = Button(root, text=\"8\", command=lambda: btnPress(8), font=(\"\",18))\nbutton8.grid(row=3, column=1)\nbutton9 = Button(root, text=\"9\", command=lambda: btnPress(9), font=(\"\",18))\nbutton9.grid(row=3, column=2)\nbutton0 = Button(root, text=\"0\", command=lambda: btnPress(0), font=(\"\",18))\nbutton0.grid(row=4, column=1)\n\nminus = Button(root, text=\"-\", command=lambda: operPress(\"-\"), font=(\"\",18))\nminus.grid(row=1, column=3)\nplus = Button(root, text=\"+\", command=lambda: operPress(\"+\"), font=(\"\",16))\nplus.grid(row=2, column=3)\nmultiply = Button(root, text=\"*\", command=lambda: operPress(\"*\"), font=(\"\",18))\nmultiply.grid(row=3, column=3)\ndivide = Button(root, text=\"/\", command=lambda: operPress(\"/\"), font=(\"\",18))\ndivide.grid(row=4, column=3)\nequals = Button(root, text=\"=\", command=evaluate, font=(\"\",18))\nequals.grid(row=4, column=2)\nclear = Button(root, text=\"C\", command=clear, font=(\"\",18))\nclear.grid(row=4, column=0)\n\nmaster.mainloop()","repo_name":"avinash-rao/Python","sub_path":"Tkinter/calculator tkinter.py","file_name":"calculator tkinter.py","file_ext":"py","file_size_in_byte":2395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40720420348","text":"class Solution:\n    def isValidPalindrome(self, s: str, k: int) -> bool:\n        \"\"\"\n        dp[i][j] means how many deleteions needed to make s[i:j + 1] a palinedrome\n        dp[i][j] = min(1 + dp[i + 1][j], 1 + dp[i][j - 1])\n                            \n        \"\"\"\n        N = len(s)\n        dp = [[float(\"inf\")] * N for _ in range(N)]\n        \n        for i in range(N):\n            dp[i][i] = 0\n            \n        for size in range(2, N + 1):\n            for i in range(N - size + 1):\n                j = i + size - 1\n                dp[i][j] = min(dp[i][j], 1 + min(dp[i + 1][j], dp[i][j - 1]))\n                if s[i] == s[j]:\n                    if size == 2:\n                        dp[i][j] = 0\n                    else:\n                        dp[i][j] = min(dp[i][j], dp[i + 1][j - 1])\n                \n        \n        return dp[0][N - 1] <= k\n","repo_name":"wondershow/CodingTraining","sub_path":"Python/LC1216_ValidPalindrome.py","file_name":"LC1216_ValidPalindrome.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73147601210","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nurlpatterns = patterns('',\n    # Examples:\n    url(r'^$', 'realescrape.views.home', name='home'),\n    url(r'^list$', 'realescrape.views.adlisting', name='list'),\n    url(r'^star$', 'realescrape.views.star', name='star'),\n    url(r'^savenotes$', 'realescrape.views.save_notes', name='save_notes'),\n    url(r'^remove$', 'realescrape.views.remove', name='remove'),\n    url(r'^scrape$', 'realescrape.views.scrape', name='scrape'),\n\n    url(r'^admin/', include(admin.site.urls)),\n)\n","repo_name":"blagarde/realescrape","sub_path":"realescrape/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29302341559","text":"#Cajero Automático\n# Definimos las variables iniciales\nusuario = 10334151\nclave = 1803\nsaldo_cuenta = 100000\nsaldo_cajero = 1000000\nintentos = 0\n\nwhile True:\n    # Solicitamos el usuario y la clave\n    usuario_ingresado = int(input(\"Ingrese su usuario: \"))\n    clave_ingresada = int(input(\"Ingrese su clave: \"))\n\n    # Verificamos la clave\n    if usuario_ingresado == usuario and clave_ingresada == clave:\n        # Solicitamos el monto a retirar\n        monto = int(input(\"Ingrese el monto a retirar: \"))\n\n        # Verificamos si el monto es válido\n        if monto <= saldo_cuenta and monto <= saldo_cajero:\n            # Actualizamos los saldos\n            saldo_cuenta -= monto\n            saldo_cajero -= monto\n\n            # Imprimimos los nuevos saldos\n            print(\"saldo cuenta=\" + str(saldo_cuenta))\n            print(\"saldo cajero=\" + str(saldo_cajero))\n        else:\n            print(\"monto no permitido\")\n    else:\n        intentos += 1\n        if intentos == 3:\n            print(\"tarjeta bloqueada\")\n            break\n        else:\n            print(\"clave invalida\")\n\n    # Preguntamos si desea continuar\n    continuar = input(\"¿Desea continuar? (N para salir): \")\n    if continuar != \"N\":\n        break\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej10/hito1_ej10_798bf2695028ec9e8ed87af4ba8df0ad.py","file_name":"hito1_ej10_798bf2695028ec9e8ed87af4ba8df0ad.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10168640441","text":"from cs50 import get_string\nimport sys\n\ndef main():\n    if len(sys.argv) == 2:\n        f = sys.argv[1]\n        k = int(f)\n        print(\"plaintext: \", end=\"\")\n        s = get_string()\n        print(\"ciphertext: \", end=\"\")\n        for c in s:\n            #checking if alphabet\n            if c.isalpha():\n                #checking if uppercase\n                if c.isupper():\n                    d = (ord(c) - 65 + k) % 26 + 65\n                    c = chr(d)\n                    print(c, end=\"\")\n                #checking if lowercase\n                elif c.islower():\n                    d = (ord(c) - 97 + k) % 26 + 97\n                    c = chr(d)\n                    print(c, end=\"\")\n            else:\n                print(c, end=\"\")\n        print(\"\")\n\n    else:\n        print(\"Usage: ./caesar k\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"kris-dey/Harvard-CS50","sub_path":"PSet6/caesar/caesar.py","file_name":"caesar.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72243798968","text":"#Multidimensional lists with snacknames\n\nfriends =[[\"Peter\", \"Jane\", \"Simon\"], []]\nsnacks = [friends[1]]\nfor index, friend in enumerate(friends[0]):\n    namelen = str(len(friend))\n    snack = input(friend + \" What is your favourite snack? \")\n    snacks.append(snack)\n    snack = str(snacks[index+1])\n    friends.append(snack)\n    print(friend +\"'s name is \" + namelen + \" characters long.\")\n    print(friend + \"'s favourite snack is: \" + snack) ","repo_name":"JahouNyan/learningpython","sub_path":"snacknames.py","file_name":"snacknames.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29393745069","text":"#La función debe retornar la distancia como un string\n# +1 : si la distancia es mayor que 1\n# IB : si la distancia es 1, y para llegar de una palabra a la otra hay que\n#      insertar o borrar una letra\n# 1S : si la distancia es 1 porque hay que sustituir una letra\n# 0D : si las palabras son iguales\ndef levenshtein (palabra1,palabra2):\n    listaf=[]\n    j=0\n    i=0\n    if len(palabra1)==len(palabra2):\n        while i<len(palabra1):\n            if palabra1[i]!=palabra2[i]:\n                j+=1\n            i+=1\n        if j==0:\n            return (\"0D\")\n        if j==1:\n            return(\"1S\")\n        if j>1:\n            return (\"+1\")\n    else:\n        if (len(palabra1)-len(palabra2))>1 or (len(palabra2)-len(palabra1))>1:\n            return (\"+1\")\n            \n        lista1=list(palabra1)\n        lista2=list(palabra2)\n        i=0\n        if len(lista1)>len(lista2):\n            while i<len(lista1):\n                if lista1[i]!=lista2[i]:\n                    listaf.append(lista1[i])\n                    lista1.pop(i)\n                    break   \n                else:\n                    i+=1\n            i=0\n            while i<len(lista1):\n                if lista1[i]!=lista2[i]:\n                    listaf.append(lista1[i])\n                i+=1\n        if len(lista2)>len(lista1):\n            while i<len(lista2):\n                if lista1[i]!=lista2[i]:\n                    listaf.append(lista1[i])\n                    lista2.pop(i)\n                    break   \n                else:\n                    i+=1\n            i=0\n            while i<len(lista2):\n                if lista1[i]!=lista2[i]:\n                    listaf.append(lista2[i])\n                i+=1\n        \n                    \n            \n        if len(listaf)==1:\n            return(\"IB\")\n        if len(listaf)>1:\n            return (\"+1\")\n\n\nif __name__==\"__main__\":\n    pass\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema10_ej2/tema10_ej2_15633616.py","file_name":"tema10_ej2_15633616.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74213038328","text":"'''\r\nUnittests for SunsetViews.py\r\nFebruary 2021 Jakub Kazimierski\r\n'''\r\n\r\nimport unittest\r\nfrom SunsetViews import sunsetViews\r\n\r\nclass test_SunsetViews(unittest.TestCase):    \r\n    '''\r\n    Class with unittests for SunsetViews.py\r\n    '''\r\n\r\n    def setUp(self):\r\n        '''\r\n        SetUp array for tests.\r\n        '''\r\n\r\n        self.buildings = [3, 5, 4, 4, 3, 1 ,3, 2]\r\n        self.direction = 'EAST'\r\n        self.output = [1, 3, 6, 7]\r\n\r\n        return self.buildings, self.direction, self.output \r\n\r\n    # region Unittests\r\n    def test_ExpectedOutput(self):\r\n        '''\r\n        Checks if returned output is as expected.\r\n        '''\r\n        buildings, direction, proper_out = self.setUp()\r\n\r\n        output = sunsetViews(buildings, direction)\r\n\r\n        self.assertEqual(output, proper_out)\r\n\r\n   # endregion\r\n\r\nif __name__ == \"__main__\":\r\n    '''\r\n    Main method for test cases.\r\n    '''\r\n    unittest.main()","repo_name":"JakubKazimierski/PythonPortfolio","sub_path":"AlgoExpert_algorithms/Medium/SunsetViews/test_SunsetViews.py","file_name":"test_SunsetViews.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"5414662686","text":"\"\"\"\nvs_wrap.py\n\nThis wrapper script encloses calls to run_StorageVET.py in a version control framework.\nIt does the following\n- edit default params based on arguments\n- make a results folder for this run that is named with the runID\n- save params in Results folder\n- note this run in a run log with associated runID and git commit hash\n\nPatricia Levi July 2020\n\"\"\"\n\nimport pandas as pd\nimport os\nimport subprocess\nimport shlex\nimport datetime\nimport sys\n\nSVet_Path = \"/Applications/storagevet2v101/StorageVET-master-git/storagevet_dervet/\"\n# SVet_Path = \"/Applications/storagevet2v101/storagevet-1.0.1/\" #old SVET, doesnt work\ndefault_params_file = \"Model_Parameters_2v1-0-2_default_updated.csv\"\nruns_log_file = \"Results/runsLog.csv\"\n\n#params = {'ICE_Active': 'yes', 'DR_days': 20, 'RA_idmode' : 'Peak by Month'} # for testing\n\ndef paramSetup(result_fol, runID_num, params, default_params_path = SVet_Path + default_params_file):\n  \"\"\"This function takes an arbitrary dict of params to modify, creates the appropriate \n  params csv, and saves it in result_fol. It returns the full filepath. The keys in params\n  must have the format 'Tag_Key'  \"\"\"\n  \n  # add Results_dir_absolute_path and Results_label to params\n  params['Results_dir_absolute_path'] = SVet_Path + \"Results/\" + result_fol + \"/\"\n  params['Results_label'] = \"_runID\" + str(runID_num)\n  params['Results_errors_log_path'] = SVet_Path + \"Results/\" + result_fol + \"/\"\n  \n  #load default params csv\n  default_params = pd.read_csv(SVet_Path + default_params_file)\n  \n  #parse params arg and change params\n  for p in params:\n    # parse out tag from key\n    tag, key = p.split(\"_\",1)\n    filter_tag = default_params.Tag == tag\n  \n    if(key == \"Active\" or key == \"active\"):\n      #change activity of that tag\n      filter_activation = default_params.Active !=  \".\"\n      filter_all = filter_tag & filter_activation\n      if sum(filter_all) != 1:\n          raise RuntimeError(\"Identified the wrong number of rows to change for Active status for tag \" +  str(tag))\n      default_params.loc[filter_all,'Active'] = params[p]\n    else:\n      # identify correct row, return error if not found\n      filter_key = default_params.Key == key\n      filter_all = filter_tag & filter_key \n      # change value\n      default_params.loc[filter_all,'Value'] = params[p]\n  \n  # save new params in results folder\n  param_filepath = SVet_Path + \"Results/\" + result_fol + \"/params_run\" + str(runID_num) + \".csv\"\n  default_params.to_csv(param_filepath, index=False)\n\n  #return params file path\n  return param_filepath\n  \ndef runStorageVET(runID_num, newParams_path, SVet_Path = SVet_Path):\n  \"Runs StorageVET via command line\"\n  print(\"Running StorageVET for runID\" + str(runID_num) + \" with parameters in \" + newParams_path)\n  # check that result folder exists with param file in it\n  if(not(os.path.exists(newParams_path))):\n    raise FileNotFoundError(\"Params file does not exist. Given path was \" + newParams_path)\n  # call StorageVET\n  # process = subprocess.Popen([\"python\",\"/Applications/storagevet2v101/StorageVET-master-git/storagevet_dervet/run_StorageVET.py\",newParams_path], stdout=sys.stdout) #stdout=subprocess.PIPE)\n  process = subprocess.Popen([\"python\",SVet_Path + \"run_StorageVET.py\",newParams_path], stdout=sys.stdout) #stdout=subprocess.PIPE)  # process = subprocess.Popen([\"python\",\"/Applications/storagevet2v101/storagevet-1.0.1/run_storagevet.py\",newParams_path], stdout=sys.stdout) #stdout=subprocess.PIPE)\n  # print output in realtime\n  # while True:\n  #   output = process.stdout.readline().decode()\n  #   if output == '' and process.poll() is not None:\n  #     break\n  #   if output:\n  #     print(output.strip())\n  # rc = process.poll()\n  # return rc\n  \n\n  \ndef updateRunLog(SVet_Path, runs_log_file, description, shortname):\n  \"Creates a new entry in Run Log, returns runID of current run\"\n  \n  # test if runs log exists, create if no\n  # with followng cols: runID, date, git hash, description\n  if(not(os.path.exists(SVet_Path + runs_log_file))):\n    raise FileNotFoundError(\"runs log file does not exist. Given path was \"+SVet_Path+runs_log_file)\n    #TODO: create empty runs log file instead\n\n  # identify new runID by examining runs log\n  runsLog = pd.read_csv(SVet_Path + runs_log_file)\n  runID = int(runsLog[['runID']].max()) + 1\n  print(\"This run has ID number \" + str(runID))\n  \n  # identify current git hash\n  # this must be called from within the git repo in order to work\n  old_path = os.getcwd()\n  os.chdir(SVet_Path)\n  try:\n    gitID_raw = subprocess.check_output(['git','rev-parse','--short','HEAD']).strip()\n    gitID = gitID_raw.strip().decode('ascii')\n  except:\n    gitID = \"No git repository\"\n  os.chdir(old_path)\n  \n  #get date\n  date = datetime.datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n  \n  # create entry in runs log\n  new_run_log_line =  \"\\n\" + str(runID) + \",\" + date + \",\" + gitID + \",\" + shortname.replace(\",\",\".\") + \",\" + description.replace(\",\",\".\")\n  # append line\n  with open(SVet_Path + runs_log_file, 'a') as rl:\n    rl.write(new_run_log_line)\n  \n  #return runID\n  return runID\n  \ndef runWithVC(shortname, description, SVet_Path = SVet_Path, default_params_file = default_params_file, \nruns_log_file = runs_log_file, **params):\n  \"\"\"calls paramSetup, creates result folder, updates runsLog, and runs StorageVET\n  params arguments should have the format 'Tag_Key = 'Value' \"\"\"\n  print(params)\n  \n  # update run log and get new runID\n  runID_num = updateRunLog(SVet_Path, runs_log_file, description, shortname)\n  \n  # make results folder for that runID\n  result_fol = \"output_run\" + str(runID_num) + \"_\" + shortname\n  os.makedirs(SVet_Path + \"Results/\" + result_fol)\n\n  # make new params file & save in results folder\n  newParams_path = paramSetup(result_fol, runID_num, params, default_params_path = SVet_Path + default_params_file)\n  \n  # call storageVET\n  runStorageVET(runID_num,newParams_path)\n\n# import importlib\n# importlib.reload(vc_wrap)  \n# vc_wrap.runWithVC(\"test\",\"testing out vc wrapper\", PV_Active = 'yes', ICE_ccost = 2000)\n# vc_wrap.runWithVC(\"DAFR_only\",\"testing out vc wrapper with DA and FR only active\", DR_Active = 'yes', FR_active = 'yes')\n\n# vc_wrap.runWithVC(\"DAFR_only\",\"testing out vc wrapper with DA and FR only active, 72 hour horizon\", DR_Active = 'yes', FR_active = 'yes', Scenario_n = \"72\")\n  \n  \n  \n  # vc_wrap.runWithVC(shortname = \"0.5h_SR\",description = \"SR duration requirement decreased to 0.5h, old svet\",SR_active = \"yes\",DA_active = \"yes\",SR_duration = 0.5)\n\n","repo_name":"pajalevi/StorageVET","sub_path":"vc_wrap.py","file_name":"vc_wrap.py","file_ext":"py","file_size_in_byte":6500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"9445156874","text":"from django.urls import path\n# from .views import (lead_create, lead_list, lead_delete, lead_details, lead_update)\nfrom .views import (AssignAgentView, LeadListView, LeadDetailsView, LeadCreateView, LeadUpdateView, LeadDeleteView)\n\n#urls of function based view\n# urlpatterns = [\n#     path('', views.lead_list, name=\"lead_list\"),\n#     path('<int:pk>/',views.lead_details, name=\"lead_details\"),\n#     path('<int:pk>/update/',views.lead_update, name=\"lead_update\"),\n#     path('create/', views.lead_create, name=\"lead_create\"),\n#     path('<int:pk>/delete/',views.lead_delete, name=\"lead_delete\"),\n# ]\n\n#urls of class based view. In this project view is working through class based view.\nurlpatterns = [\n    path('', LeadListView.as_view(), name=\"lead_list\"),\n    path('<int:pk>/', LeadDetailsView.as_view(), name=\"lead_details\"),\n    path('<int:pk>/update/',LeadUpdateView.as_view(), name=\"lead_update\"),\n    path('<int:pk>/delete/', LeadDeleteView.as_view(), name=\"lead_delete\"),\n    path('<int:pk>/assign_agent/', AssignAgentView.as_view(), name=\"assign_agent\"),\n    path('create/', LeadCreateView.as_view(), name=\"lead_create\"),\n]","repo_name":"Aman-Thapa-23/CRM2","sub_path":"leads/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74605046968","text":"from typing import NamedTuple\nfrom enum import Enum\nfrom .opcodes import Op, create_push, op\nfrom .context import ObjectId\nfrom .utils import build_unique_dict\n\n\nclass MarkPurpose(Enum):\n    Start = 'Start'\n    End = 'End'\n    Label = 'Label'\n    Other = 'Other'\n\n\nclass MarkId (NamedTuple(\n    'MarkId',\n    [\n        ('obj_id', ObjectId),\n        ('purpose', MarkPurpose)\n    ]\n)):\n    def different_ctx(self, other: 'MarkId') -> bool:\n        return self.obj_id.ctx_id != other.obj_id.ctx_id\n\n\nMark = NamedTuple('Mark', [('mid', MarkId)])\nMarkRef = NamedTuple('MarkRef', [('mid', MarkId)])\nMarkDeltaRef = NamedTuple('MarkDeltaRef', [('start', MarkId), ('end', MarkId)])\nSizedRef = NamedTuple(\n    'SizedRef',\n    [('ref', MarkRef | MarkDeltaRef), ('offset_size', int)]\n)\n\n\nAsm = Op | Mark | MarkRef | MarkDeltaRef | bytes\nSolidAsm = Op | Mark | SizedRef | bytes\n\n\ndef to_start_mark(obj_id: ObjectId) -> Mark:\n    return Mark(MarkId(obj_id, MarkPurpose.Start))\n\n\ndef to_end_mark(obj_id: ObjectId) -> Mark:\n    return Mark(MarkId(obj_id, MarkPurpose.End))\n\n\ndef to_start_mark_ref(obj_id: ObjectId) -> MarkRef:\n    return MarkRef(MarkId(obj_id, MarkPurpose.Start))\n\n\ndef to_end_mark_ref(obj_id: ObjectId) -> MarkRef:\n    return MarkRef(MarkId(obj_id, MarkPurpose.End))\n\n\ndef to_size_mark_ref(obj_id: ObjectId) -> MarkDeltaRef:\n    start = MarkId(obj_id, MarkPurpose.Start)\n    end = MarkId(obj_id, MarkPurpose.End)\n    return MarkDeltaRef(start, end)\n\n\ndef set_size(ref: SizedRef, size: int) -> SizedRef:\n    return SizedRef(ref.ref, size)\n\n\ndef min_static_size(step: Asm) -> int:\n    if isinstance(step, Op):\n        return 1 + len(step.extra_data)\n    elif isinstance(step, bytes):\n        return len(step)\n    elif isinstance(step, (MarkRef, MarkDeltaRef)):\n        return 1\n    elif isinstance(step, Mark):\n        return 0\n    else:\n        raise TypeError(f'Unhandled step {step}')\n\n\ndef get_size(step: SolidAsm) -> int:\n    if isinstance(step, Op):\n        return 1 + len(step.extra_data)\n    elif isinstance(step, bytes):\n        return len(step)\n    elif isinstance(step, SizedRef):\n        return 1 + step.offset_size\n    elif isinstance(step, Mark):\n        return 0\n    else:\n        raise TypeError(f'Unhandled step {step}')\n\n\ndef needed_bytes(x: int) -> int:\n    return max((x.bit_length() + 7) // 8, 1)\n\n\ndef get_min_static_size_bytes(asm: list[Asm]) -> int:\n    '''Compute the minimum size in bytes that'd capture any code offset'''\n    ref_count = sum(isinstance(step, (MarkRef, MarkDeltaRef)) for step in asm)\n    min_static_total_size = sum(map(min_static_size, asm))\n    ref_bytes = 1\n    while ((1 << (8 * ref_bytes)) - 1) < min_static_total_size + ref_bytes * ref_count:\n        ref_bytes += 1\n    return ref_bytes\n\n\ndef asm_to_solid(asm: list[Asm]) -> list[SolidAsm]:\n    size_bytes = get_min_static_size_bytes(asm)\n    assert size_bytes <= 6\n    return [\n        SizedRef(step, size_bytes)\n        if isinstance(step, (MarkRef, MarkDeltaRef))\n        else step\n        for step in asm\n    ]\n\n\ndef validate_asm(asm: list[Asm]) -> None:\n    '''\n    Checks that MarkDeltaRef steps have strictly different, correctly ordered references and that all\n    marks have unique IDs\n    '''\n    # Checks that all MarkIDs are unique while build indices dict\n    indices: dict[MarkId, int] = build_unique_dict([\n        (step.mid, i)\n        for i, step in enumerate(asm)\n        if isinstance(step, Mark)\n    ], lambda mid: f'Duplicate mid #{mid}')\n    for i, step in enumerate(asm):\n        if isinstance(step, MarkRef):\n            assert step.mid in indices, f'Assembly step #{i} has invalid reference to {step.mid}'\n        elif isinstance(step, MarkDeltaRef):\n            assert step.start in indices, f'Assembly step #{i} has invalid reference to {step.start}'\n            assert step.end in indices, f'Assembly step #{i} has invalid reference to {step.end}'\n            assert indices[step.end] > indices[step.start], \\\n                f'Assembly step #{i} references negative delta'\n\n\ndef get_solid_offsets(asm: list[SolidAsm]) -> dict[MarkId, int]:\n    mark_offsets: dict[MarkId, int] = {}\n    offset = 0\n    for step in asm:\n        if isinstance(step, Mark):\n            mark_offsets[step.mid] = offset\n        offset += get_size(step)\n    return mark_offsets\n\n\ndef shorten_asm_once(asm: list[SolidAsm]) -> tuple[bool, list[SolidAsm]]:\n    mark_offsets: dict[MarkId, int] = get_solid_offsets(asm)\n\n    changed_any = False\n    shortened_steps: list[SolidAsm] = []\n    for step in asm:\n        if isinstance(step, SizedRef):\n            ref = step.ref\n            if isinstance(ref, MarkRef):\n                req_size = needed_bytes(mark_offsets[ref.mid])\n                if req_size != step.offset_size:\n                    changed_any = True\n                    step = set_size(step, req_size)\n            elif isinstance(ref, MarkDeltaRef):\n                req_size = needed_bytes(\n                    mark_offsets[ref.end] - mark_offsets[ref.start]\n                )\n                if req_size != step.offset_size:\n                    changed_any = True\n                    step = set_size(step, req_size)\n            else:\n                assert False  # Sanity check\n        shortened_steps.append(step)\n\n    return changed_any, shortened_steps\n\n\ndef shorten_asm(asm: list[SolidAsm]) -> list[SolidAsm]:\n    changed_any = True\n    while changed_any:\n        changed_any, asm = shorten_asm_once(asm)\n    return asm\n\n\ndef solid_asm_to_bytecode(asm: list[SolidAsm]) -> bytes:\n    mark_offsets: dict[MarkId, int] = get_solid_offsets(asm)\n    final_bytes: bytes = bytes()\n\n    # Create skeleton for final bytecode\n    for step in asm:\n        if isinstance(step, Op):\n            final_bytes += bytes(step.get_bytes())\n        elif isinstance(step, bytes):\n            final_bytes += step\n        elif isinstance(step, SizedRef):\n            ref = step.ref\n            if isinstance(ref, MarkRef):\n                value = mark_offsets[ref.mid]\n            elif isinstance(ref, MarkDeltaRef):\n                value = mark_offsets[ref.end] - mark_offsets[ref.start]\n            else:\n                assert False\n            push = create_push(value.to_bytes(step.offset_size, 'big'))\n            final_bytes += bytes(push.get_bytes())\n        elif isinstance(step, Mark):\n            # Mark generates no bytes\n            pass\n        else:\n            raise ValueError(f'Unrecognized assembly step {step}')\n\n    return final_bytes\n\n\ndef asm_to_bytecode(asm: list[Asm]) -> bytes:\n    validate_asm(asm)\n    solid_asm = asm_to_solid(asm)\n    solid_asm = shorten_asm(solid_asm)\n    return solid_asm_to_bytecode(solid_asm)\n","repo_name":"Philogy/py-huff","sub_path":"py_huff/assembler.py","file_name":"assembler.py","file_ext":"py","file_size_in_byte":6639,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"77"}
+{"seq_id":"29470159899","text":"def buscarTodas(palabra_larga,palabra):\n    lista=[]\n    i=0\n    while i in range(len(palabra_larga)):\n        for j in palabra:\n            if palabra_larga[i]==j:\n                lista.append(i)\n                lista.append(\" \")\n        i+=1\n        lista2=[]\n        for q in lista:\n            if type(q)==str():\n                lista2.append(q)\n            else:\n                a=str(q)\n                lista2.append(a)\n    lista2.pop(len(lista2)-1)\n    p=\"\".join(lista2)\n    return p\n\nif __name__ == \"__main__\":\n    pass\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej2/tema8_ej2_d753592874ab39eb76ec62259c23feaa.py","file_name":"tema8_ej2_d753592874ab39eb76ec62259c23feaa.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2324619389","text":"# ARRAYS - Medium\n\n# Given an array nums of n integers, return an array of all the unique quadruplets [nums[a], nums[b], nums[c], nums[d]] such that:\n# 0 <= a, b, c, d < n,\n# a, b, c, and d are distinct, and\n# nums[a] + nums[b] + nums[c] + nums[d] == target\n\nclass Solution:\n    def fourSum(self, nums: List[int], target: int) -> List[List[int]]:\n        nums.sort()\n        \n        def kSum(nums: List[int], target: int, k: int) -> List[List[int]]:\n            res = []\n            if not nums:\n                return res\n            avg = target // k\n            if avg < nums[0] or avg > nums[-1]:\n                return res    \n            if k == 2:\n                return twoSum(nums, target)\n            for i in range(len(nums)):\n                if i == 0 or nums[i - 1] != nums[i]:\n                    for subset in kSum(nums[i + 1:], target - nums[i], k - 1):\n                        res.append([nums[i]] + subset)\n            return res\n\n        def twoSum(nums: List[int], target: int) -> List[List[int]]:\n            res = []\n            lo, hi = 0, len(nums) - 1\n            while (lo < hi):\n                add = nums[lo] + nums[hi]\n                if add < target or (lo > 0 and nums[lo] == nums[lo - 1]):\n                    lo += 1\n                elif add > target or (hi < len(nums) - 1 and nums[hi] == nums[hi + 1]):\n                    hi -= 1\n                else:\n                    res.append([nums[lo], nums[hi]])\n                    lo += 1\n                    hi -= 1                                  \n            return res\n\n        return kSum(nums, target, 4)\n","repo_name":"Neha-Niharika-Kar/Crack-Your-Placement-Challenge","sub_path":"Day 10 - 10.06.22/four-sum.py","file_name":"four-sum.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37390114383","text":"### imports ###\n\nimport pygame, sys\n\n### globals ###\n\nupdate = 10\ngrid = 16\nscreen_x = 32; screen_y = 32\nscore_a = 0; score_b = 0\npaddle_size = int(0.25 * screen_y)\npaddle_offset = 2\n\nwhite = (255, 255, 255)\nblack = (0, 0, 0)\n\n### screen ###\n\n# updates caption with current scores\ndef update_caption():\n    caption = str(score_a) + ' | ' + str(score_b)\n    pygame.display.set_caption(caption)\n\n# initialize screen and update caption\nscreen_size = (screen_x * grid, screen_y * grid)\nscreen = pygame.display.set_mode(screen_size)\nupdate_caption()\n\n### board ###\n\n# draws current board to screen\ndef draw_board():\n    screen.fill(black)\n    for y in range(0, screen_y):\n        for x in range(0, screen_x):\n            # if board filled\n            if board[x][y] == 1:\n                # draw rect\n                rect = ((x * grid, y * grid), (grid, grid))\n                pygame.draw.rect(screen, white, rect)\n    pygame.display.update()\n\n# initialize board\nboard = []\nfor y in range(0, screen_y):\n    row = []\n    for x in range(0, screen_x):\n        row.append(0)\n    board.append(row)\n\n### paddles ###\n\ntake_input_a = True; take_input_b = True\npaddle_a_top = int(screen_y / 2) - int(paddle_size / 2)\npaddle_b_top = int(screen_y / 2) - int(paddle_size / 2)\n\n# initialize paddles\nfor y in range(int(screen_y / 2) - int(paddle_size / 2),\n               int(screen_y / 2) + int(paddle_size / 2)):\n    board[paddle_offset][y] = 1\n    board[screen_x - paddle_offset - 1][y] = 1\n\ndef paddle_input_a(direction):\n    global paddle_a_top, take_input_a\n    # if not taking input, return\n    if not take_input_a: return\n    if direction == 'up':\n        # if out of bounds, return\n        if paddle_a_top <= 0: return\n        # move paddle and stop take input\n        paddle_a_top -= 1\n        board[paddle_offset][paddle_a_top] = 1\n        board[paddle_offset][paddle_a_top + paddle_size] = 0\n        take_input_a = False\n    elif direction == 'down':\n        # if out of bounds, return\n        if paddle_a_top >= screen_y - paddle_size: return\n        # move paddle and stop take input\n        paddle_a_top += 1\n        board[paddle_offset][paddle_a_top - 1] = 0\n        board[paddle_offset][paddle_a_top + paddle_size - 1] = 1\n        take_input_a = False\n\ndef paddle_input_b(direction):\n    global paddle_b_top, take_input_b\n    # if not taking input, return\n    if not take_input_b: return\n    if direction == 'up':\n        # if out of bounds, return\n        if paddle_b_top <= 0: return\n        # move paddle and stop take input\n        paddle_b_top -= 1\n        board[screen_x - paddle_offset - 1][paddle_b_top] = 1\n        board[screen_x - paddle_offset - 1][paddle_b_top + paddle_size] = 0\n        take_input_b = False\n    elif direction == 'down':\n        # if out of bounds, return\n        if paddle_b_top >= screen_y - paddle_size: return\n        # move paddle and stop take input\n        paddle_b_top += 1\n        board[screen_x - paddle_offset - 1][paddle_b_top - 1] = 0\n        board[screen_x - paddle_offset - 1][paddle_b_top + paddle_size - 1] = 1\n        take_input_b = False\n\n### ball ###\n\n# initialize ball\nball_x = int(screen_x / 2); ball_y = int(screen_y / 2)\nball_dir = 'ur' # ur, dr, dl, ul\nboard[ball_x][ball_y] = 1\n\ndef move_ball():\n    global ball_x, ball_y, ball_dir, score_a, score_b\n\n    if ball_dir == 'ur':\n        # recurse if bouncing\n        if ball_y <= 0 or board[ball_x][ball_y - 1] == 1:\n            ball_dir = 'dr'\n            move_ball()\n        elif ball_x >= screen_x - 1 or board[ball_x + 1][ball_y] == 1:\n            ball_dir = 'ul'\n            move_ball()\n        # if clear, move\n        else:\n            board[ball_x][ball_y] = 0\n            ball_x += 1; ball_y -= 1\n            if ball_x <= 0: score_b += 1\n            elif ball_x >= screen_x - 1: score_a += 1\n            board[ball_x][ball_y] = 1\n\n    elif ball_dir == 'dr':\n        # recurse if bouncing\n        if ball_y >= screen_y - 1 or board[ball_x][ball_y + 1] == 1:\n            ball_dir = 'ur'\n            move_ball()\n        elif ball_x >= screen_x - 1 or board[ball_x + 1][ball_y] == 1:\n            ball_dir = 'dl'\n            move_ball()\n        # if clear, move\n        else:\n            board[ball_x][ball_y] = 0\n            ball_x += 1; ball_y += 1\n            if ball_x <= 0: score_b += 1\n            elif ball_x >= screen_x - 1: score_a += 1\n            board[ball_x][ball_y] = 1\n\n    elif ball_dir == 'dl':\n        # recurse if bouncing\n        if ball_y >= screen_y - 1 or board[ball_x][ball_y + 1] == 1:\n            ball_dir = 'ul'\n            move_ball()\n        elif ball_x <= 0 or board[ball_x - 1][ball_y] == 1:\n            ball_dir = 'dr'\n            move_ball()\n        # if clear, move\n        else:\n            board[ball_x][ball_y] = 0\n            ball_x -= 1; ball_y += 1\n            if ball_x <= 0: score_b += 1\n            elif ball_x >= screen_x - 1: score_a += 1\n            board[ball_x][ball_y] = 1\n\n    elif ball_dir == 'ul':\n        # recurse if bouncing\n        if ball_y <= 0 or board[ball_x][ball_y - 1] == 1:\n            ball_dir = 'dl'\n            move_ball()\n        elif ball_x <= 0 or board[ball_x - 1][ball_y] == 1:\n            ball_dir = 'ur'\n            move_ball()\n        # if clear, move\n        else:\n            board[ball_x][ball_y] = 0\n            ball_x -= 1; ball_y -= 1\n            if ball_x <= 0: score_b += 1\n            elif ball_x >= screen_x - 1: score_a += 1\n            board[ball_x][ball_y] = 1\n\n### main ###\n\n# time tracking\nclock = pygame.time.Clock()\nframe = 0\n\n# game loop\ndraw_board()\nwhile True:\n\n    # tick\n    clock.tick(60)\n    frame += 1\n\n    # events\n    for event in pygame.event.get():\n\n        # quit\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            sys.exit()\n\n        # keys\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_w: paddle_input_a('up')\n            if event.key == pygame.K_s: paddle_input_a('down')\n            if event.key == pygame.K_UP: paddle_input_b('up')\n            if event.key == pygame.K_DOWN: paddle_input_b('down')\n\n    # update\n    if frame % update == 0:\n\n        # move ball\n        move_ball()\n\n        # draw board\n        draw_board()\n\n        # update caption\n        update_caption()\n\n        # reset take input\n        take_input_a = True; take_input_b = True\n","repo_name":"csaye/python-highlights","sub_path":"pygame/Pong.py","file_name":"Pong.py","file_ext":"py","file_size_in_byte":6323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72146262970","text":"# coding: latin-1\n#python.exe -m pip install --upgrade pip\n#python -m pip install paramiko\n\nfrom paramiko import SSHClient, AutoAddPolicy\nimport time\nimport tkinter as tk\nimport tkinter.font as font\nimport tkinter.ttk as ttk\n\nimport socket\nfrom tkinter import filedialog\n\n#pip install pyserial\nimport serial  # per la comunicació amb el USB per a la botonera\nimport serial.tools.list_ports  \nimport subprocess\n\nfrom multiprocessing  import Queue \n#from xeviClientBotonsUSB import *\n\n#python -m pip install pygame\nimport pygame # per la musica\n\nfrom multiprocessing import Process, Event  # procés en background per la reproducció dels clips generats\nimport signal #per a capturar el final del procés en background\n\nfrom os.path import expanduser # per a obtenir el directori de l'usuari expanduser(\"~\")\n\nfrom pathlib import Path # per obtenir el teu directori arrel de windows\n\n\nimport sys  # mirar el detall de la excepcio\n\nimport configparser\nfrom configparser import NoOptionError, NoSectionError\nimport os # buscar el directori actual per trobar el fitxer de configuarció de propietats\n\n\n#from machine import Timer\n#import turtle\n\n\nxeviEncenLedGPIO = \"~/rpi-ws281x-python/xevi/lib/xeviEncenLedGPIO.sh\"\nxeviAnimacioLed = \"~/rpi-ws281x-python/xevi/lib/xeviAnimacioLed.py\"\nxeviTiraRGB = \"~/rpi-ws281x-python/xevi/lib/xeviTiraRGB.py\"\nkillxevi = \"~/rpi-ws281x-python/xevi/killxevi.sh \"\nservidorTCPIP = \"~/rpi-ws281x-python/xevi/lib/servidor.py \"\n\n#\"192.168.1.144\"\nsIpRaspberry = None\n#10000\niPort = None\n\n\n\nstart_time = time.time()\nclient = None\nsock = None\nconnexioSerialUSB = None\nconfigControladora = configparser.RawConfigParser()\nconfigBotonera = configparser.RawConfigParser()\n\n\n\n\ndef INICIALITZACONNEXIONS():\n    global client\n    global sock\n    global connexioSerialUSB\n    global configBotonera\n    global sIpRaspberry\n    global iPort\n    \n    debug=False\n    MirarBotonera=False\n    BotoneraVelocitat=500000\n    \n    \n    \n    configControladora.read(       os.path.join(os.path.dirname(os.path.abspath(__file__)), 'configuracio.properties')             )\n    \n    try:\n        sIpRaspberry = configControladora.get('LEDS REMOT','raspberry.ip')\n        iPort = configControladora.get('LEDS REMOT','raspberry.port')\n        MirarBotonera = configControladora.get('LEDS REMOT','botonera.mode') == '1' #0-desactivada 1-integradaRemota 2-independentRemota 3-IntegradaServidor\n        BotoneraVelocitat = int(configControladora.get('LEDS REMOT','botonera.velocitat'))\n    except NoOptionError:\n        sIpRaspberry = \"192.168.1.144\"\n        iPort = 10000\n    \n\n    # IMPORTANT haver engegat el servidor de la tira LED adressable en remot\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' servidor.py')\n    #client.exec_command('sudo nohup python3 ' + servidorTCPIP + ' &')\n\n    if not is_socket_opened(sock):\n        print('inicialitzem el client socket TCP/IP contra la IP ' +sIpRaspberry+':'+iPort)\n        # Create a TCP/IP socket per al led adresable\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        # Connect the socket to the port on the server given by the caller\n        server_address = (sIpRaspberry, int(iPort))\n        sock.connect(server_address)\n\n\n    #Crear una sessió SSH contra la raspberry per a la resta d'elements\n    if client is None or  client.get_transport() is None:\n        print('inicialitzem el client ssh contra la IP ' +sIpRaspberry+':'+iPort)\n        client = SSHClient()\n        client.set_missing_host_key_policy(AutoAddPolicy())\n        client.connect(sIpRaspberry, username='pi', password='raspberry')\n\n\n    #Inicialitzar el PIN 10 de la raspberry en moda de sortida\n    stdin, stdout, stderr = client.exec_command('gpio -g mode 10 out')\n\n\n\n\n\n    if not MirarBotonera:\n        print('NO configurarem la botonera de forma integrada')\n    else:\n\n        configBotonera.read(               os.path.join(os.path.dirname(os.path.abspath(__file__)), 'controladora.properties')              )\n            \n        PORTBOTONERA=LlistarUSB()\n        \n        try:\n            print('provem connexió al port '+PORTBOTONERA + ' amb velocitat ' + str(BotoneraVelocitat))\n            connexioSerialUSB = serial.Serial(PORTBOTONERA,BotoneraVelocitat) # ,timeout = None\n            print('Connexió USB amb la caixa botonera realitzada correctament al port '+PORTBOTONERA)\n        except:\n            print('no tenim port USB on trobar la botonera')\n            None\n        \n\n\ndef LlistarUSB():\n\n    #Primera forma de mirar els elements connectats via USB\n    myports = [tuple(p) for p in list(serial.tools.list_ports.comports())]\n    print( myports)\n\n    #Segona forma de mirar els elements connectats via USB\n    os.system(\" powershell \\\"Get-PnpDevice -PresentOnly | Where-Object { $_.InstanceId -match '^USB' } | Where-Object { $_.FriendlyName -match 'CH340' } \\\"\")\n\n\n    #una altre manera de fer-ho\n    #port = os.popen(\" powershell \\\"$valor = Get-PnpDevice -PresentOnly | Where-Object { $_.InstanceId -match '^USB' } | Where-Object { $_.FriendlyName -match 'CH340' } | Select  -ExpandProperty FriendlyName; $valor2=$valor -match '(COM\\\\d*)' ; Write-Host $Matches.0 \\\"\").read().rstrip()\n    #print (\"Ens connectarem al port -\" + port + \"-\")\n    \n    port = subprocess.check_output(\"powershell \\\"$valor = Get-PnpDevice -PresentOnly | Where-Object { $_.InstanceId -match '^USB' } | Where-Object { $_.FriendlyName -match 'CH340' } | Select  -ExpandProperty FriendlyName; $valor2=$valor -match '(COM\\\\d*)' ; Write-Host $Matches.0 \\\"\", shell=True).rstrip()\n    #print (\"Ens connectarem al port -\" + str(port.decode(\"utf-8\")) +\"-\")\n    \n    return str(port.decode(\"utf-8\"))\n            \n            \n\ndef TANCARCONNEXIONS():\n    client.close()\n    sock.close()\n    try:\n        if connexioSerialUSB is not None:\n            connexioSerialUSB.close()\n    except serial.serialutil.SerialException:\n        None\n\n\ndef RegistrarComanda(pComanda):\n    global procesSimular\n    if  (pygame.mixer.get_init() and pygame.mixer.music.get_busy()) or (procesSimular and procesSimular.is_alive()):\n        temps = str(tempsReproduccioAcomulat + (time.time() - horaIniciReproducio ))\n        temps = temps[0:temps.index('.')+4]\n        #lbOrdres.insert ( tk.END,temps + '#' + pComanda)\n        \n        posicio=0\n        for i, listbox_entry in enumerate(lbOrdres.get(0, tk.END)):\n            comanda = listbox_entry.split()\n            if len(comanda)> 0 and float(comanda[0]) < float(temps):   posicio=i \n        \n        #lbOrdres.select_clear(0, \"end\")                            \n        lbOrdres.insert ( posicio+1,temps + ' # ' + pComanda)\n        lbOrdres.selection_set(posicio+1)\n        lbOrdres.see(posicio+1) \n\n                    \n    else:\n        temps = str(time.time() - start_time)\n        temps = temps[0:temps.index('.')+4]\n        #només es mostrarà a la consola la instrucció\n        #print(\"nomes a consola:\")\n    \n    print(temps,' # ', pComanda)\n\n\ndef EnviarComandaAServidor(pComanda):\n\n    RegistrarComanda( pComanda)\n    \n    #si estem simulant no podem enviar des d'aquí\n    if not procesSimular or not procesSimular.is_alive():\n        sock.sendall(bytearray(pComanda, 'utf-8'))\n    else:\n        #Estem executant el procés de reproducció del mp3 i no tenim accés al socket.. ho posem a la Queue cap al procés pq sigui ell qui ho envii\n        cuaOrdresUSB.put(pComanda)\n        \n\n\n    \ndef is_socket_opened(sock: socket.socket) -> bool:\n    try:\n        # this will try to read bytes without blocking and also without removing them from buffer (peek only)\n        data = sock.recv(16, socket.MSG_DONTWAIT | socket.MSG_PEEK)\n        if len(data) == 0:\n            return True\n    except BlockingIOError:\n        return False  # socket is open and reading from it would block\n    except ConnectionResetError:\n        return True  # socket was closed for some other reason\n    except Exception as e:\n        #print(\"unexpected exception when checking if a socket is closed\")\n        return False\n    return False\n    \n    \n    \n\ndef PanicCelestialOn(event):\n    #stdin, stdout, stderr = client.exec_command('sudo python3 ' + xeviTiraRGB + ' --color W  --animacio pintar --intensitat 255 &')\n    #EnviarComandaAServidor('|intensitat 255|seleccionarTiraRGB 0,1,2,3,4,5|color W|pintarTiraRGB')\n    \n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed &&  sudo python3 ' + xeviAnimacioLed + ' --animacio pintar --color W  ')\n    #EnviarComandaAServidor('|intensitat 255|color W|omplir')\n    \n    EnviarComandaAServidor('|PanicBlanc')\n    \n    \n#    stdin, stdout, stderr = client.exec_command('gpio -g write 10 1')\n#    global activatGPIO10\n#    activatGPIO10=True\n#    btn_textEncendreGPIO10.set(\"Apagar leds GPIO-10\")    \n#    RegistrarComanda( \"gpio -g write 10 1\") \n\n\n    #EnviarComandaAServidor('|activarCanalI2C 15' )\n    global activat220vI2C15\n    activat220vI2C15=True\n    btn_textEncendreI2C15.set(\"Apagar 220v I2C (canal 15)\")\n\n    #EnviarComandaAServidor('|activarCanalI2C 14' )\n    global activat220vI2C14\n    activat220vI2C14=True\n    btn_textEncendreI2C14.set(\"Apagar 220v I2C (canal 14)\")\n\n    #EnviarComandaAServidor('|activarCanalI2C 13' )\n    global activat220vI2C13\n    activat220vI2C13=True\n    btn_textEncendreI2C13.set(\"Apagar 220v I2C (canal 13)\")\n\n    #EnviarComandaAServidor('|activarCanalI2C 12' )\n    global activat220vI2C12\n    activat220vI2C12=True\n    btn_textEncendreI2C12.set(\"Apagar 220v I2C (canal 12)\")\n\n\n    #stdin, stdout, stderr = client.exec_command('gpio -g write 13 1')\n    #RegistrarComanda( \"gpio -g write 13 1\") \n    \n\n    \n    \ndef PanicCelestialOff(event):\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio pintar --color W --esborrar')\n    #netejarLedAdressable()\n\n    #stdin, stdout, stderr = client.exec_command('sudo python3 ' + xeviTiraRGB + ' --color W  --animacio pintar --esborrar &')\n    #sock.sendall(bytearray('|seleccionarTiraRGB 0,1,2,3,4,5|netejarTiraRGB', 'utf-8'))\n    #ApagarRGB()\n\n    EnviarComandaAServidor('|PanicBlack' )\n    \n    \n    #ApagarGPIO()    \n    \n#    stdin, stdout, stderr = client.exec_command('gpio -g write 10 0')\n#    global activatGPIO10\n#    activatGPIO10=False\n#    btn_textEncendreGPIO10.set(\"Encendre leds GPIO-10\")\n#    RegistrarComanda( \"gpio -g write 10 0\")\n    \n    #stdin, stdout, stderr = client.exec_command('gpio -g write 13 0')\n    #RegistrarComanda( \"gpio -g write 13 0 \")\n    \n    #EnviarComandaAServidor('|desactivarCanalI2C 15' )\n    global activat220vI2C15\n    activat220vI2C15=False\n    btn_textEncendreI2C15.set(\"Encendre 220v I2C (canal 15)\")\n    \n    #EnviarComandaAServidor('|desactivarCanalI2C 14' )\n    global activat220vI2C14\n    activat220vI2C14=False\n    btn_textEncendreI2C14.set(\"Encendre 220v I2C (canal 14)\")\n    \n    #EnviarComandaAServidor('|desactivarCanalI2C 13' )\n    global activat220vI2C1\n    activat220vI2C13=False\n    btn_textEncendreI2C13.set(\"Encendre 220v I2C (canal 13)\")\n    \n    #EnviarComandaAServidor('|desactivarCanalI2C 12' )\n    global activat220vI2C12\n    activat220vI2C12=False\n    btn_textEncendreI2C12.set(\"Encendre 220v I2C (canal 12)\")\n    \n\n    \n    \n#def ApagarGPIO(event=None):\n#    EnviarComandaAServidor('|desactivarCanalI2C 15' )\n#    global activat220vI2C15\n#    activat220vI2C15=False\n#    btn_textEncendreI2C15.set(\"Encendre 220v I2C (canal 15)\")\n#    \n#    stdin, stdout, stderr = client.exec_command('gpio -g write 10 0')\n#    global activatGPIO10\n#    activatGPIO10=False\n#    btn_textEncendreGPIO10.set(\"Encendre leds GPIO-10\")\n#    RegistrarComanda( \"gpio -g write 10 0\")\n#    \n#    #stdin, stdout, stderr = client.exec_command('gpio -g write 13 0')\n#    #RegistrarComanda( \"gpio -g write 13 0 \")\n    \n\n\n\n\n\n#def GPIO10On(event):\n#    stdin, stdout, stderr = client.exec_command('sleep 0.2 && gpio -g write 10 1')\n#\n#def GPIO10Off(event):\n#    stdin, stdout, stderr = client.exec_command('sleep 0.2 && gpio -g write 10 0')\n\nactivatGPIO10=False\ndef EncendreGPIO10(event=None):\n    global activatGPIO10\n    if activatGPIO10:  \n        stdin, stdout, stderr = client.exec_command('gpio -g write 10 0')\n        activatGPIO10=False\n        btn_textEncendreGPIO10.set(\"Encendre leds GPIO-10\")\n        RegistrarComanda( \"gpio -g write 10 0\")\n    else:                     \n        stdin, stdout, stderr = client.exec_command('gpio -g write 10 1')\n        activatGPIO10=True\n        btn_textEncendreGPIO10.set(\"Apagar leds GPIO-10\")  \n        RegistrarComanda( \"gpio -g write 10 1\")        \n    \n    #stdin, stdout, stderr = client.exec_command('sh ' + xeviEncenLedGPIO + ' 10 ' + str(float(txtTempsTotal.get())/1000.0) + ' &')\n    \n    \n    \n    \nactivat220vI2C15=False\ndef Encendre220vI2C15(event=None):\n    global activat220vI2C15\n    if activat220vI2C15:  \n        EnviarComandaAServidor('|desactivarCanalI2C 15' )\n        activat220vI2C15=False\n        btn_textEncendreI2C15.set(\"Encendre 220v I2C (canal 15)\")\n    else:                     \n        EnviarComandaAServidor('|activarCanalI2C 15' )\n        activat220vI2C15=True\n        btn_textEncendreI2C15.set(\"Apagar 220v I2C (canal 15)\")\n        \nactivat220vI2C14=False\ndef Encendre220vI2C14(event=None):\n    global activat220vI2C14\n    if activat220vI2C14:  \n        EnviarComandaAServidor('|desactivarCanalI2C 14' )\n        activat220vI2C14=False\n        btn_textEncendreI2C14.set(\"Encendre 220v I2C (canal 14)\")\n    else:                     \n        EnviarComandaAServidor('|activarCanalI2C 14' )\n        activat220vI2C14=True\n        btn_textEncendreI2C14.set(\"Apagar 220v I2C (canal 14)\")\n\n\nactivat220vI2C13=False\ndef Encendre220vI2C13(event=None):\n    global activat220vI2C13\n    if activat220vI2C13:  \n        EnviarComandaAServidor('|desactivarCanalI2C 13' )\n        activat220vI2C13=False\n        btn_textEncendreI2C13.set(\"Encendre 220v I2C (canal 13)\")\n    else:                     \n        EnviarComandaAServidor('|activarCanalI2C 13' )\n        activat220vI2C13=True\n        btn_textEncendreI2C13.set(\"Apagar 220v I2C (canal 13)\")\n        \nactivat220vI2C12=False\ndef Encendre220vI2C12(event=None):\n    global activat220vI2C12\n    if activat220vI2C12:  \n        EnviarComandaAServidor('|desactivarCanalI2C 12' )\n        activat220vI2C12=False\n        btn_textEncendreI2C12.set(\"Encendre 220v I2C (canal 12)\")\n    else:                     \n        EnviarComandaAServidor('|activarCanalI2C 12' )\n        activat220vI2C12=True\n        btn_textEncendreI2C12.set(\"Apagar 220v I2C (canal 12)\")\n\n\n\n#def RGBCanal1On(event):\n#    #if chk_color_aleatori.get():\n#        stdin, stdout, stderr = client.exec_command('sudo python3 ' + xeviTiraRGB + ' --color A  --animacio pintar --intensitat 255 &')\n#    #else:\n#    #    stdin, stdout, stderr = client.exec_command('sudo python3 ' + xeviTiraRGB + ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + '  --animacio pintar --intensitat 255 &')\n#    \n#def RGBCanal1Off(event):\n#    stdin, stdout, stderr = client.exec_command('sudo python3 ' + xeviTiraRGB + ' --colorR 1 --colorG 1 --colorB 1  --animacio pintar --intensitat 1 &')\n\n    \nsTotesTires = '0,1'\nsTiresCanal1 = '0'\nsTiresCanal2 = '1'\n    \ndef ApagarRGB(event=None):\n    EnviarComandaAServidor('|seleccionarTiraRGB '+sTotesTires+'|netejarTiraRGB')\n    \n    \ndef EncendreRGB(event=None):\n    #if chk_color_aleatori.get():\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --color A  --animacio pintar --intensitat ' + str(int(txtIntensitat.get())+1) + ' --long ' + txtTempsTotal.get() + ' --esborrar --tira ' + sCanal.get() + ' &')\n    #else:\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + '  --animacio pintar --intensitat ' + str(int(txtIntensitat.get())+1) + ' --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    comanda=\"\"\n    if chk_color_aleatori.get():\n        comanda+='|color A '\n    else:\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n        \n    if sCanal.get() == 'T' : comanda+='|seleccionarTiraRGB '+sTotesTires+' '\n    elif sCanal.get() == '1' : comanda+='|seleccionarTiraRGB '+sTiresCanal1+' '\n    elif sCanal.get() == '2' : comanda+='|seleccionarTiraRGB '+sTiresCanal2+' '\n    \n    comanda+='|intensitat ' + txtIntensitat.get() + '|pintarTiraRGB ' #+ txtTempsTotal.get()\n    EnviarComandaAServidor(comanda)\n    \n\ndef CreixerRGB(event=None):\n    #if chk_color_aleatori.get():\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --color A  --animacio creixer  --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    #else:\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + '  --animacio creixer  --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    #print('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --color A  --animacio creixer  --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    comanda=\"\"\n    if chk_color_aleatori.get():\n        comanda+='|color A '\n    else:\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n        \n    if sCanal.get() == 'T' : comanda+='|seleccionarTiraRGB '+sTotesTires+' '\n    elif sCanal.get() == '1' : comanda+='|seleccionarTiraRGB '+sTiresCanal1+' '\n    elif sCanal.get() == '2' : comanda+='|seleccionarTiraRGB '+sTiresCanal2+' '\n    comanda+='|intensitat ' + txtIntensitat.get() + '|creixerTiraRGB ' + txtTempsTotal.get() \n    EnviarComandaAServidor(comanda)\n    \n    \ndef DecreixerRGB(event=None):\n    #if chk_color_aleatori.get():\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --color A  --animacio decreixer  --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    #else:\n    #    stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviTiraRGB && sudo python3 ' + xeviTiraRGB + ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + '  --animacio decreixer  --long ' + txtTempsTotal.get() + ' --tira ' + sCanal.get() + ' --esborrar &')\n    comanda=\"\"\n    if chk_color_aleatori.get():\n        comanda+='|color A '\n    else:\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n        \n    if sCanal.get() == 'T' : comanda+='|seleccionarTiraRGB '+sTotesTires+' '\n    elif sCanal.get() == '1' : comanda+='|seleccionarTiraRGB '+sTiresCanal1+' '\n    elif sCanal.get() == '2' : comanda+='|seleccionarTiraRGB '+sTiresCanal2+' '\n    comanda+='|intensitat ' + txtIntensitat.get() + '|decreixerTiraRGB ' + txtTempsTotal.get() \n    EnviarComandaAServidor(comanda)\n\n\n    \n    \n    \n    \n\ndef rainbowTempsTotal(event=None):\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio rainbowTempsTotal --wait_ms ' + txtTempsIteracio.get() + ' --long ' + txtTempsTotal.get() + '  --intensitat  ' + str(int(txtIntensitat.get())+1) + ' --esborrar ')\n    comanda='|intensitat ' + txtIntensitat.get() + '|rainbowTempsTotal ' + txtTempsIteracio.get() + ' ' + txtTempsTotal.get() \n    EnviarComandaAServidor(comanda)\n    \ndef rainbowCycleTempsTotal(event=None):\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio rainbowCycleTempsTotal --wait_ms ' + txtTempsIteracio.get() + ' --long ' + txtTempsTotal.get() + '  --intensitat  ' + str(int(txtIntensitat.get())+1) + ' --esborrar')\n    comanda='|intensitat ' + txtIntensitat.get() + '|rainbowCycleTempsTotal ' + txtTempsIteracio.get() + ' ' + txtTempsTotal.get() \n    EnviarComandaAServidor(comanda)\n\ndef theaterChaseTempsTotal(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        #color_seleccionat = ' --color A '\n        comanda+='|color A '\n    else:\n        #color_seleccionat = ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + ' '\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio theaterChaseTempsTotal --wait_ms ' + txtTempsIteracio.get() + ' --long ' + txtTempsTotal.get() + '  --intensitat  ' + str(int(txtIntensitat.get())+1) + ' --esborrar --subconjunt ' + txtSubconjunt.get() + ' ' + color_seleccionat)\n    comanda+='|intensitat ' + txtIntensitat.get() + '|theaterChaseTempsTotal ' + txtTempsIteracio.get() + ' ' + txtTempsTotal.get() +' ' + txtSubconjunt.get()\n    EnviarComandaAServidor(comanda)\n    \n    \ndef theaterChaseRainbow(event=None):\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio theaterChaseRainbow --wait_ms ' + txtTempsIteracio.get() + ' --long ' + txtTempsTotal.get() + '  --intensitat  ' + str(int(txtIntensitat.get())+1) + ' --esborrar')\n    comanda='|intensitat ' + txtIntensitat.get() + '|theaterChaseRainbow ' + txtTempsIteracio.get() + ' ' + txtTempsTotal.get() \n    EnviarComandaAServidor(comanda)\n\n\n\n    \n    \ndef netejarLedAdressable(event=None):\n    EnviarComandaAServidor('|netejar ')\n    \n    \ndef omplirLedAdressable(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        comanda+='|color A '\n    else:\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n    comanda+='|intensitat ' + txtIntensitat.get() + '|omplir'\n    EnviarComandaAServidor(comanda)\n    \n    \n    \n    \ndef creixerLedAdressable(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        #color_seleccionat = ' --color A '\n        comanda+='|color A '\n    else:\n        #color_seleccionat = ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + ' '\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio creixer --long ' + txtTempsTotal.get() + ' --esborrar ' + color_seleccionat)\n    comanda+='|creixer ' + txtTempsTotal.get()  \n    EnviarComandaAServidor(comanda)\n\ndef decreixerLedAdressable(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        #color_seleccionat = ' --color A '\n        comanda+='|color A '\n    else:\n        #color_seleccionat = ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + ' '\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio creixer --invers --long ' + txtTempsTotal.get() + ' --esborrar ' + color_seleccionat)\n    comanda+='|decreixer ' + txtTempsTotal.get()  \n    EnviarComandaAServidor(comanda)\n   \ndef wipeLedAdressable(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        #color_seleccionat = ' --color A '\n        comanda+='|color A '\n    else:\n        #color_seleccionat = ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + ' '\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio vano --subconjunt ' + txtSubconjunt.get() + ' --long ' + txtTempsTotal.get() + ' --esborrar ' + color_seleccionat)\n    #print('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio vano --subconjunt ' + txtSubconjunt.get() + ' --long ' + txtTempsTotal.get() + ' --esborrar ' + color_seleccionat)\n    comanda+='|intensitat ' + txtIntensitat.get() + '|vano ' + txtTempsTotal.get() + ' ' + txtSubconjunt.get() \n    EnviarComandaAServidor(comanda)\n   \ndef incremental(event=None):\n    comanda=''\n    if chk_color_aleatori.get():\n        #color_seleccionat = ' --color A '\n        comanda+='|color A '\n    else:\n        #color_seleccionat = ' --colorR ' + str(int(txtR.get())+1) + ' --colorG ' + str(int(txtG.get())+1) + ' --colorB ' + str(int(txtB.get())+1) + ' '\n        comanda+='|colorRGB ' + txtR.get() + ' ' + txtG.get() + ' ' + txtB.get()\n\n    #stdin, stdout, stderr = client.exec_command('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio incrementalParts --subconjunt ' + txtSubconjunt.get() + ' --wait_ms ' + txtTempsIteracio.get() + ' --esborrar ' + color_seleccionat)\n    #print('sh ' + killxevi +  ' xeviAnimacioLed && sudo python3 ' + xeviAnimacioLed + ' --animacio incrementalParts --subconjunt ' + txtSubconjunt.get() + ' --wait_ms ' + txtTempsIteracio.get() + ' --esborrar ' + color_seleccionat)\n    comanda+='|intensitat ' + txtIntensitat.get() + '|incremental ' + txtTempsIteracio.get() + ' ' + txtSubconjunt.get() \n    EnviarComandaAServidor(comanda)\n    \n    \n    \n    \n    \ndef chekAleatoriChange():    \n    if chk_color_aleatori.get():\n        txtR['fg']=\"grey\"\n        txtG['fg']=\"grey\"\n        txtB['fg']=\"grey\"\n        lblR['fg']=\"grey\"\n        lblG['fg']=\"grey\"\n        lblB['fg']=\"grey\"\n    else: \n        txtR['fg']=\"black\"\n        txtG['fg']=\"black\"\n        txtB['fg']=\"black\"\n        lblR['fg']=\"black\"\n        lblG['fg']=\"black\"\n        lblB['fg']=\"black\"\n\n\ndef componentHex(valor):\n    retorn = str(hex(int(valor)))[2:4]\n    #print(valor, retorn, hex(int(valor)), hex (65))\n    if len(retorn) == 1:\n        return \"0\"+retorn\n    else:\n        return retorn\n\n        \ndef ColorPredefinitChange(even):\n    #print (choiceVar.get())\n    #print(choices.where(choiceVar.get()))\n    chk_color_aleatori.set(False)\n    chekAleatoriChange()\n    for index, item in enumerate(choices):\n        if item == choiceVar.get():\n            #print (item, index)\n            #print (valorsRGB[index][0], valorsRGB[index][1],valorsRGB[index][2])\n            txtR.delete(0, 'end')\n            txtG.delete(0, 'end')\n            txtB.delete(0, 'end')\n            \n            txtR.insert(tk.END,valorsRGB[index][0])\n            txtG.insert(tk.END, valorsRGB[index][1])\n            txtB.insert(tk.END, valorsRGB[index][2])\n            \n    button_mostrar_color.configure(bg = \"#\"  +  componentHex (txtR.get())  +   componentHex (txtG.get())   +   componentHex (txtB.get()) )\n    #print()\n\n    \n\ndef EnviarTextPantalla(even=None):    \n    EnviarComandaAServidor('|PANTALLA cs:0,'+txtR.get()+','+txtG.get()+','+txtB.get())\n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA tx:'+txtPantalla.get())\n\ndef EnviarTextComanda(even=None):    \n    EnviarComandaAServidor(txtComanda.get())\n    \ndef EnviarBanderaCat(even=None):        \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA ct:')\ndef EnviarBanderaGay(even=None):        \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA gy:')\ndef EnviarSmile(even=None):        \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA sm:9500')\ndef EnviarCountdown(even=None):        \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA cd:')\ndef EnviarRosa(even=None):      \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA rs:')\ndef EnviarAutomatic(even=None):      \n    EnviarComandaAServidor('|PANTALLA in:'+txtIntensitat.get())\n    EnviarComandaAServidor('|PANTALLA au:')\n  \n\n\n    \n#FITXER_MP3=str(Path.home())+'.\\\\data\\\\FUMFUMFUM.mp3'\nFITXER_MP3='.\\\\data\\\\FUMFUMFUM.mp3'\n#FITXER_INSTRUCCIONS=str(Path.home())+'\\\\Downloads\\\\prova.time.xev'\nFITXER_INSTRUCCIONS='.\\\\data\\\\FUMFUMFUM.time'\ndef menuObrirMp3():\n    global FITXER_MP3\n    FITXER_MP3 = filedialog.askopenfilename(initialdir = expanduser(\"~\"),title = \"Select MP3 file:\",filetypes = ((\"mp3 files\",\"*.mp3\"),))\n    #if not pygame.mixer.get_init():\n    #    #pygame.mixer.pre_init(115000, -16, 2, 2048)\n    #    pygame.mixer.init()\n    \n    pygame.mixer.music.load(FITXER_MP3)\n    pygame.mixer.music.set_volume(0.3)\n\n    #print (FITXER_MP3)\n    \ndef menuObrirInstruccions():    \n    global FITXER_INSTRUCCIONS\n    FITXER_INSTRUCCIONS = filedialog.askopenfilename(initialdir = expanduser(\"~\"),title = \"Selecciona un fitxer amb instruccions:\",filetypes = ((\"xev files\",\"*.xev\"),))\n    \n    file1 = open(FITXER_INSTRUCCIONS, 'r') \n    Lines = file1.readlines() \n    \n    lbOrdres.delete(0,lbOrdres.size())\n    for line in Lines: \n        lbOrdres.insert(tk.END,line )\n    file1.close() \n\n\n\ndef menuGuardarInstruccions():    \n    global FITXER_INSTRUCCIONS\n    #initialdir = expanduser(\"~\")\n    FITXER_INSTRUCCIONS = filedialog.asksaveasfilename(initialdir = FITXER_INSTRUCCIONS,title = \"Indica el nom del fitxer amb instruccions a guardar:\",filetypes = ((\"xev files\",\"*.xev\"),))\n    print(FITXER_INSTRUCCIONS)\n\n    \n    file1 = open(FITXER_INSTRUCCIONS, 'w') \n    #for i, listbox_entry in enumerate(lbOrdres.get(0, tk.END)):\n    for listbox_entry in lbOrdres.get(0, tk.END):\n        file1.writelines(listbox_entry)\n    file1.close() \n\n\n    \n\ncuaOrdresUSB=Queue()\ndef procesSimularExecucio(pFitxerInstruccions, pFitxerMp3,tempsReproduccioAcomulat,eventInici,cuaOrdresUSB):\n\n    global client\n    global sock\n    \n    print(pFitxerInstruccions, pFitxerMp3,tempsReproduccioAcomulat)\n    file1 = open(pFitxerInstruccions, 'r') \n    Lines = file1.readlines() \n    file1.close()\n    \n    INICIALITZACONNEXIONS()\n    buscantInici=True\n    \n    #pygame.init()\n    #pygame.mixer.pre_init(115000, -16, 2, 2048)\n    pygame.mixer.init(frequency=48000)\n    pygame.mixer.music.load(pFitxerMp3)\n    pygame.mixer.music.set_volume(0.1)\n    pygame.mixer.music.play(start=tempsReproduccioAcomulat)  # start=POSICIO_MP3/1000\n    eventInici.set()\n    start_time = time.time()-tempsReproduccioAcomulat\n    \n    try:\n        for lina in Lines:\n            talls = lina.split()\n            if len (talls)>1:\n                temps = float(talls[0])\n\n                #Esperem a poder executar la seguent comanda i de mentres revisem els botons USB \n                while  time.time() - start_time < temps:\n                    buscantInici=False\n                    time.sleep(0.05)\n                    \n                    if not cuaOrdresUSB.empty():\n                        print(\"HEM REBUT UNA ORDRE PER USB !! \")\n                        sock.sendall(bytearray(cuaOrdresUSB.get(), 'utf-8'))\n                    \n                                \n\n                #És hora de executar una de les comandes predefinides\n                if not buscantInici:\n                    print(lina)\n                    comanda = lina.split('#')[1]\n\n                    if '|' in comanda:\n                        sock.sendall(bytearray(comanda.strip(), 'utf-8'))\n                    elif '@' in comanda:\n                        print('És un comentari')\n                    else:\n                        stdin, stdout, stderr = client.exec_command(comanda)\n    finally:\n        #print(\"FINAL POSICIO PROCES: \",pygame.mixer.music.get_pos())\n        client.close()\n        sock.close()\n\n\n\ndef  MATAR_PROCES_SEGON_PLA():\n    global procesSimular\n    global client\n    global sock\n    global horaIniciReproducio\n    \n    \n    #matem el possible procés que estigui simulant les instruccions actuals\n    if procesSimular and procesSimular.is_alive():   \n            #print(\"Hem matat el procés de simulació de segon pla\")\n            procesSimular.terminate();\n            #print(\"Ens hem quedat a la marca de temps: \",time.time() - horaIniciReproducio )\n\n\n\n\n            \nUltimaOrdreExecutada=0\nUltimaOrdreSeleccionada=-1\ndef seguirExecucioOrdres():\n    global UltimaOrdreExecutada\n    global horaIniciReproducio\n    global tempsReproduccioAcomulat\n    global UltimaOrdreSeleccionada\n    \n    # si estem executant el play faig el seguiment\n    if horaIniciReproducio > 0:\n        tempsSeguiment = tempsReproduccioAcomulat + time.time() - horaIniciReproducio\n        posicio=0\n        for i, listbox_entry in enumerate(lbOrdres.get(UltimaOrdreExecutada, tk.END)):\n            comanda = listbox_entry.split()\n            if len(comanda)> 5:\n                if float(comanda[0]) < tempsSeguiment:\n                    posicio=i\n                else: \n                    #print(float(comanda[0]) , tempsSeguiment)\n                    break\n                \n        UltimaOrdreExecutada = UltimaOrdreExecutada + posicio\n        if UltimaOrdreExecutada > 0:\n            if UltimaOrdreSeleccionada >= 0: lbOrdres.select_clear(UltimaOrdreSeleccionada)\n            lbOrdres.selection_set(UltimaOrdreExecutada) \n            lbOrdres.see(UltimaOrdreExecutada+10) \n            UltimaOrdreSeleccionada = UltimaOrdreExecutada\n\n\n        #actualitzem el comptador de temps actual\n        txtTempsRepro.delete(0,tk.END)\n        txtTempsRepro.insert(tk.END ,str(tempsSeguiment)[:str(tempsSeguiment ).find('.')+4])\n        \n        \n        #crida recursiva cada 1 segon\n        root.after(250, seguirExecucioOrdres)\n\n    #reinicialitzem    \n    else: UltimaOrdreSeleccionada=-1\n        \n\n\ndef lbOrdresEsborrarFila(event):\n    lbOrdres.delete(lbOrdres.curselection())\n\ndef lbSeleccionarTemps(event):\n    if not pygame.mixer.get_init() or not pygame.mixer.music.get_busy():\n        print(lbOrdres.curselection())\n        #agafem l'ultim element seleccionat, pot no ser el que ha seleccionat el usuari\n        text = lbOrdres.get(lbOrdres.curselection()[-1]).split()\n        if len(text) > 1:\n            txtTempsRepro.delete(0,tk.END)\n            txtTempsRepro.insert(tk.END ,text[0][:str(tempsReproduccioAcomulat).find('.')+4])\n            #txtTempsRepro.insert(tk.END ,text[0][:6])\n\n    \n    \n    \n    \nPLAY_MP3='OFF'    \nprocesSimular=None\ntempsReproduccioAcomulat=0.0\nhoraIniciReproducio=time.time()\ndef buttonPlayPausePress(event=None):\n    \n    global FITXER_MP3\n    global PLAY_MP3\n    global POSICIO_MP3\n    global procesSimular\n    global tempsReproduccioAcomulat\n    global horaIniciReproducio\n    global UltimaOrdreExecutada\n    global cuaOrdresUSB\n\n    MATAR_PROCES_SEGON_PLA()\n    \n    if PLAY_MP3=='ON':\n        print(\"Pausa\")\n        #print(\"Iteracio:\" ,time.time() - horaIniciReproducio ,pygame.mixer.music.get_pos() )\n        if  pygame.mixer.get_init() and pygame.mixer.music.get_busy():\n            #pygame.mixer.music.pause()\n            #if tempsReproduccioAcomulat>0.0: \n            #    tempsReproduccioAcomulat = tempsReproduccioAcomulat -1.8\n            #    print(\"resto\")\n            #tempsReproduccioAcomulat += pygame.mixer.music.get_pos() / 1000\n            pygame.mixer.music.stop()\n            #print(\"calculat el temps per aqui\")\n        tempsReproduccioAcomulat += time.time() - horaIniciReproducio \n        \n        \n        PLAY_MP3='PAUSE'\n        buttonPlay.config(text=\"4\")\n        \n        horaIniciReproducio=0\n        print(\"Ens hem quedat a la marca de temps: \",tempsReproduccioAcomulat)\n        txtTempsRepro.delete(0,tk.END)\n        txtTempsRepro.insert(tk.END ,str(tempsReproduccioAcomulat)[:str(tempsReproduccioAcomulat).find('.')+4])\n\n        #Seleccionem l'últim element executat\n        pos_selected=0\n        for i, listbox_entry in enumerate(lbOrdres.get(0, tk.END)):\n            comanda = listbox_entry.split()\n            if len(comanda)> 5:\n                if float(comanda[0]) < tempsReproduccioAcomulat:\n                    pos_selected=i\n                else: break\n        if pos_selected > 0:\n            #lbOrdres.select_clear(0, \"end\")\n            lbOrdres.selection_set(pos_selected) \n            lbOrdres.see(pos_selected) \n        \n        INICIALITZACONNEXIONS()\n        \n        \n    else:\n        print(\"plaY\")\n\n        #deseleccionem tots els elements de la llista\n        lbOrdres.select_clear(0, \"end\") \n        \n        if pygame.mixer.get_init() is None:\n            #pygame.mixer.pre_init(115000, -16, 2, 2048)\n            pygame.mixer.init(frequency=48000)\n            pygame.mixer.music.load(FITXER_MP3)\n        \n        buttonPlay.config(text=\";\")\n        PLAY_MP3='ON'\n        tempsReproduccioAcomulat= float(txtTempsRepro.get())\n        \n        if checkSimular.get() == 1:\n            #TANCARCONNEXIONS()   # ens caldrà tancar la connexió del socket i la del USB\n            sock.close()\n            \n            eventInici = Event()\n            procesSimular = Process(target=procesSimularExecucio, args=(FITXER_INSTRUCCIONS,FITXER_MP3,tempsReproduccioAcomulat,eventInici,cuaOrdresUSB))\n            procesSimular.start()\n            \n            #esperem que comenci a reproduir la música\n            eventInici.wait()\n        else:\n            pygame.mixer.music.set_volume(0.3)\n            pygame.mixer.music.play(start=tempsReproduccioAcomulat)  # start=POSICIO_MP3/1000\n\n        horaIniciReproducio=time.time()\n        \n        #entegem un timer que vagi seleccionant els elements que es van executant de la llista\n        UltimaOrdreExecutada=0\n        root.after(1000, seguirExecucioOrdres)\n\n        #esborrem l'últim temps executat del timing\n        txtTempsRepro.delete(0,tk.END)\n        txtTempsRepro.insert(tk.END ,\"\")\n\n        \n        \n\n                        \n                    \n                    \n                \n    \n    \ndef buttonBackPress():\n    global tempsReproduccioAcomulat\n    tempsReproduccioAcomulat-=5\n    if tempsReproduccioAcomulat < 0: tempsReproduccioAcomulat=0\n    \n    #pygame.mixer.music.set_pos(tempsReproduccioAcomulat)\n    \n    txtTempsRepro.delete(0,tk.END)\n    txtTempsRepro.insert(tk.END ,str(tempsReproduccioAcomulat)[:str(tempsReproduccioAcomulat).find('.')+4])\n    #txtTempsRepro.insert(tk.END ,tempsReproduccioAcomulat)\n       \n\ndef buttonAbansarPress():\n    global tempsReproduccioAcomulat\n    print(\"abasar\")\n    tempsReproduccioAcomulat+=5\n    \n    txtTempsRepro.delete(0,tk.END)\n    txtTempsRepro.insert(tk.END ,str(tempsReproduccioAcomulat)[:str(tempsReproduccioAcomulat).find('.')+4])\n    #txtTempsRepro.insert(tk.END ,tempsReproduccioAcomulat)\n    \n    \n\n\ndef buttonStopPress():\n    print(\"Stop\")\n    global PLAY_MP3\n    global tempsReproduccioAcomulat\n    global horaIniciReproducio\n\n    if pygame.mixer.get_init():\n        pygame.mixer.music.stop() \n    \n    MATAR_PROCES_SEGON_PLA()    \n        \n    if PLAY_MP3=='ON':\n        tempsReproduccioAcomulat += time.time() - horaIniciReproducio\n    #pygame.mixer.music.rewind()\n    \n    buttonPlay.config(text=\"4\")\n    \n    print(\"Ens hem quedat a la marca de temps: \",tempsReproduccioAcomulat)\n    txtTempsRepro.delete(0,tk.END)\n    txtTempsRepro.insert(tk.END ,str(tempsReproduccioAcomulat)[:str(tempsReproduccioAcomulat).find('.')+4])\n    #txtTempsRepro.insert(tk.END ,tempsReproduccioAcomulat)\n    tempsReproduccioAcomulat=0\n    horaIniciReproducio=0\n    \n    PLAY_MP3='OFF'    \n    \n    INICIALITZACONNEXIONS()\n\n    \n\n    \n\n    \n\n    \n    \n    \n#eventGestonarUSBBotons=None\ndef procesLlegirBotoneraUSB():\n    global sock\n    global connexioSerialUSB\n    global procesSimular\n    global configBotonera\n    \n    #la primera comanda la despreciem perquè pot arribar mitja comanda\n    #comanda= connexioSerialUSB.readline().decode(\"utf-8\") .rstrip(\"\\r\\n\")\n    if connexioSerialUSB is not None:\n        try:\n        #    while True:\n            if connexioSerialUSB.inWaiting():\n                comanda= connexioSerialUSB.readline().decode(\"utf-8\") .rstrip(\"\\r\\n\")\n                print(\"-\"+comanda+\"-\")\n                \n                #transformem el botó apretat a una comanda\n                try:\n                    comanda = configBotonera.get('BotoneraUSB','boto.'+comanda)\n                    \n                except NoOptionError:\n                    comanda=\"@Comanda desconeguda\" \n                print(\"-\"+comanda+\"-\")\n                    \n                \n                # mirem si és un comentari o cadena buida\n                if comanda[0:1] != \"@\" and comanda != \"\":\n                    if comanda[0:4] == \"gpio\": \n                        stdin, stdout, stderr = client.exec_command(comanda)\n                    else:\n                        EnviarComandaAServidor(comanda)\n\n\n            root.after(100, procesLlegirBotoneraUSB)\n        except:\n            print(sys.exc_info())  #[0]\n            print(\"no consultarem més el USB\")\n        #finally:\n        #    connexioSerialUSB.close()    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \nif __name__ == '__main__':\n        \n        \n    INICIALITZACONNEXIONS()\n    \n    \n    col = 0\n    fila = 0\n\n\n\n\n    #Pintem la finestra windows \n    root = tk.Tk()\n    root.title(\"Controladora de LEDS\")\n    root.geometry('1200x800')\n\n    #Configuració de les dreceres de teclat\n    root.bind('<F1>',PanicCelestialOff)\n    root.bind('<F2>',PanicCelestialOn)\n    \n    \n#    root.bind('q',ApagarGPIO)\n#    root.bind('w',EncendreGPIO10)\n#    root.bind('e',Encendre220vI2C15)\n#    \n#    root.bind('a',ApagarRGB)\n#    root.bind('s',EncendreRGB)\n#    root.bind('d',CreixerRGB)\n#    root.bind('f',DecreixerRGB)\n#    \n#    root.bind('z',netejarLedAdressable)\n#    root.bind('x',omplirLedAdressable)\n#    root.bind('c',creixerLedAdressable)\n#    root.bind('v',decreixerLedAdressable)\n#    root.bind('b',wipeLedAdressable)\n#    root.bind('n',incremental)\n#    root.bind('m',rainbowTempsTotal)\n#    root.bind(',',rainbowCycleTempsTotal)\n#    root.bind('.',theaterChaseTempsTotal)\n#    root.bind('-',theaterChaseRainbow)\n#    \n#    \n#    root.bind('p',buttonPlayPausePress)\n\n    \n    #<Tab>\n    #<Caps_Lock>\n    \n    \n    \n    \n    \n    #espai a la esquerra perquè no quedi tot enganxat\n    frame = tk.Frame(root)\n    frame.pack(fill=\"x\",side=tk.LEFT, padx=\"20\")\n\n\n\n    frameLed = tk.Frame(root)\n    frameLed.pack(side='left')\n\n    frameTotal = tk.Frame(frameLed)\n    frameTotal.pack(side='top')\n\n    frame1Led = tk.Frame(frameLed)\n    frameCanal = tk.Frame(frameLed)\n    frameLedAddr = tk.Frame(frameLed)\n    frame1Led.pack(side='top')\n    frameCanal.pack(side='top')\n    frameLedAddr.pack(side='top')\n\n    #frameLedAddr1 = tk.Frame(frameLedAddr)\n    #frameLedAddr1.pack(side='left')\n    #frameLedAddr2 = tk.Frame(frameLedAddr)\n    #frameLedAddr2.pack(side='left')\n\n\n\n    buttonPANIC_Celestial = tk.Button(frameTotal, bg=\"white\",text=\"Pànic Blanc\")\n    buttonPANIC_Celestial.bind('<ButtonPress>',PanicCelestialOn)\n    buttonPANIC_Celestial.bind('<ButtonRelease>',PanicCelestialOff)\n    buttonPANIC_Celestial.grid(column=col, row=fila)\n    buttonPANIC_Celestial['font'] = font.Font(size=20)\n    \n    \n\n    buttonPANIC_Infern = tk.Button(frameTotal, bg=\"white\",text=\"Pànic Negre\")\n    buttonPANIC_Infern.bind('<ButtonPress>',PanicCelestialOff)\n    buttonPANIC_Infern.grid(column=col+1, row=fila, padx=(50,0))\n    buttonPANIC_Infern['font'] = font.Font(size=20)\n\n\n\n\n\n    \n\n\n    #btn_textEncendreGPIO10 = tk.StringVar()\n    #buttonEncendreGPIO10 = tk.Button(frame1Led, \n    #                   textvariable=btn_textEncendreGPIO10,\n    #                   command=EncendreGPIO10,                   \n    #                   fg=\"black\")\n    #btn_textEncendreGPIO10.set(\"Encendre leds GPIO-10\")\n    #buttonEncendreGPIO10.grid(column=col, row=fila, pady=(40, 10))\n    #fila = fila + 1\n\n    btn_textEncendreI2C15 = tk.StringVar()\n    buttonEncendreI2C15 = tk.Button(frame1Led, \n                       textvariable=btn_textEncendreI2C15,\n                       command=Encendre220vI2C15,                   \n                       fg=\"black\")\n    btn_textEncendreI2C15.set(\"Encendre 220v I2C (canal 15)\")\n    buttonEncendreI2C15.grid(column=col, row=fila, pady=(40, 5))\n    col = col + 1\n\n    btn_textEncendreI2C14 = tk.StringVar()\n    buttonEncendreI2C14 = tk.Button(frame1Led, \n                       textvariable=btn_textEncendreI2C14,\n                       command=Encendre220vI2C14,                   \n                       fg=\"black\")\n    btn_textEncendreI2C14.set(\"Encendre 220v I2C (canal 14)\")\n    buttonEncendreI2C14.grid(column=col, row=fila, pady=(40, 5),padx=(20, 10))\n    col = 0 \n    fila = fila + 1\n    btn_textEncendreI2C12 = tk.StringVar()\n    buttonEncendreI2C12 = tk.Button(frame1Led, \n                       textvariable=btn_textEncendreI2C12,\n                       command=Encendre220vI2C12,                   \n                       fg=\"black\")\n    btn_textEncendreI2C12.set(\"Encendre 220v I2C (canal 12)\")\n    buttonEncendreI2C12.grid(column=col, row=fila, pady=(10, 10))\n    col = col + 1\n\n    btn_textEncendreI2C13 = tk.StringVar()\n    buttonEncendreI2C13 = tk.Button(frame1Led, \n                       textvariable=btn_textEncendreI2C13,\n                       command=Encendre220vI2C13,                   \n                       fg=\"black\")\n    btn_textEncendreI2C13.set(\"Encendre 220v I2C (canal 13)\")\n    buttonEncendreI2C13.grid(column=col, row=fila, pady=(10, 10),padx=(20, 10))\n    col = 0 \n    fila = fila + 1\n\n\n\n\n    sCanal = tk.StringVar()\n    R1 = tk.Radiobutton(frameCanal, text=\"Tots\", variable=sCanal, value='T')\n    R1.grid(column=0, row=0, pady=(10, 40))\n    R2 = tk.Radiobutton(frameCanal, text=\"Canal 1\", variable=sCanal, value='1')\n    R2.grid(column=1, row=0, pady=(10, 40))\n    R3 = tk.Radiobutton(frameCanal, text=\"Canal 2\", variable=sCanal, value='2')\n    R3.grid(column=2, row=0, pady=(10, 40))\n    R1.select()\n\n\n\n\n#    buttonPolsRGBCanal1 = tk.Button(frame1Led, \n#                       text=\"Pols RGB color Aleatori\", \n#                       fg=\"black\")\n#    buttonPolsRGBCanal1.bind('<ButtonPress>',RGBCanal1On)\n#    buttonPolsRGBCanal1.bind('<ButtonRelease>',RGBCanal1Off)\n#    buttonPolsRGBCanal1.grid(column=col, row=fila, pady=(40, 10))\n#    fila = fila + 1\n\n\n\n\n    buttonApagarRGB = tk.Button(frame1Led, \n                       text=\"Netejar RGB\", \n                       command=ApagarRGB,              \n                       fg=\"black\")\n    buttonApagarRGB.grid(column=col, row=fila, pady=(30, 10))\n    col = col + 1\n\n\n    buttonEncendreRGB = tk.Button(frame1Led, \n                       text=\"Encendre RGB (C)\", \n                       command=EncendreRGB,              \n                       fg=\"black\")\n    buttonEncendreRGB.grid(column=col, row=fila, pady=(30, 10),padx=(20, 10))\n    col = 0 \n    fila = fila + 1\n\n    buttonCreixerRGB = tk.Button(frame1Led, \n                       text=\"Creixer RGB (TT, C)\", \n                       command=CreixerRGB,              \n                       fg=\"black\")\n    buttonCreixerRGB.grid(column=col, row=fila, pady=(0, 10))\n    col = col + 1\n\n\n\n    buttonDecreixerRGB = tk.Button(frame1Led, \n                       text=\"Decreixer RGB (TT, C)\", \n                       command=DecreixerRGB,              \n                       fg=\"black\")\n    buttonDecreixerRGB.grid(column=col, row=fila, pady=(0, 10),padx=(20, 10))\n    fila = fila + 1\n\n\n\n\n    \n\n    button_netejarLedAdresable = tk.Button(frameLedAddr, \n                       text=\"Netejar\", \n                       fg=\"black\",\n                       command=netejarLedAdressable)\n    button_netejarLedAdresable.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n    \n    button_omplirLedAdresable = tk.Button(frameLedAddr, \n                       text=\"Omplir (C)\", \n                       fg=\"black\",\n                       command=omplirLedAdressable)\n    button_omplirLedAdresable.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n\n    button_creixerLedAdressable = tk.Button(frameLedAddr, \n                       text=\"Creixer (TT, C)\", \n                       fg=\"black\",\n                       command=creixerLedAdressable)\n    button_creixerLedAdressable.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n    button_decreixerLedAdressable = tk.Button(frameLedAddr, \n                       text=\"Decreixer (TT, C)\", \n                       fg=\"black\",\n                       command=decreixerLedAdressable)\n    button_decreixerLedAdressable.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n\n    button_WipeLedAdressable = tk.Button(frameLedAddr, \n                       text=\"Vano (TT, SUB, C)\", \n                       fg=\"black\",\n                       command=wipeLedAdressable)\n    button_WipeLedAdressable.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n    button_incremental = tk.Button(frameLedAddr,    # o rellotge de sorra\n                       text=\"Incremental (TI, SUB, C)\", \n                       fg=\"black\",\n                       command=incremental)\n    button_incremental.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n\n    buttonrainbow = tk.Button(frameLedAddr, \n                       text=\"rainbowTempsTotal (TT, TI, I)\", \n                       fg=\"black\",\n                       command=rainbowTempsTotal)\n    buttonrainbow.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n\n\n    button_rainbowCycleTempsTotal = tk.Button(frameLedAddr, \n                       text=\"rainbowCycleTempsTotal (TT, TI, I)\", \n                       fg=\"black\",\n                       command=rainbowCycleTempsTotal)\n    button_rainbowCycleTempsTotal.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n    button_theaterChaseTempsTotal = tk.Button(frameLedAddr, \n                       text=\"theaterChaseTempsTotal (TT, TI, SUB, C, I)\", \n                       fg=\"black\",\n                       command=theaterChaseTempsTotal)\n    button_theaterChaseTempsTotal.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n    button_theaterChaseRainbow = tk.Button(frameLedAddr, \n                       text=\"theaterChaseRainbow (TT, TI, I)\", \n                       fg=\"black\",\n                       command=theaterChaseRainbow)\n    button_theaterChaseRainbow.grid(column=col, row=fila, pady=(0, 10))\n    fila = fila + 1\n\n\n\n\n\n\n\n\n\n    frameConfig = tk.Frame(root)\n    frameConfig.pack(side='left')\n\n\n    frame0 = tk.Frame(frameConfig)\n    frame0.pack( )\n\n    frame1 = tk.Frame(frameConfig)\n    frame1.pack( )\n\n    frame2 = tk.Frame(frameConfig)\n    frame2.pack()\n\n    frame3 = tk.Frame(frameConfig)\n    frame3.pack()\n    \n    \n    framePantalla = tk.Frame(frameConfig)\n    framePantalla.pack()\n\n\n    fila = 0\n    col = 0\n    lbOm = tk.Label(frame0, text=\"Color predefinit:\")\n    lbOm.grid(column=col, row=fila, pady=(0, 5))\n    col = col + 1\n\n    choiceVar = tk.StringVar()\n    choices = ( \"blanc\", \"vermell\", \"taronja\", \"groc\", \"verd clar\", \"verd fosc\", \"verd-blau\", \"blau-cel\", \"blauEsmortait\", \"blau\", \"lila\", \"rosa\",  \"gris\")\n    valorsRGB=((255,255,255),(255,0,0),(255,128,0),(255,255,0),(128,255,0),(0,255,0),(0,255,128),(0,255,255),(0,128,255),(0,0,255),(127,0,255),(255,0,255),(128,128,128))\n\n\n    choiceVar.set(choices[0])\n    om = tk.OptionMenu(frame0, choiceVar, *choices, command=ColorPredefinitChange)\n    om.grid(column=col, row=fila,padx=(5, 0),pady=(0, 10))\n\n    col = col + 1\n    button_mostrar_color = tk.Button(frame0, text=\"          \")\n    button_mostrar_color.grid(column=col, row=fila, padx=(20, 0), pady=(0, 10))\n\n\n\n    fila = 0\n    col = 0\n    lblR = tk.Label(frame1, text=\"Red:\")\n    lblR.grid(column=col, row=fila,padx=(50, 0),)\n    col = col + 1\n    txtR = tk.Entry(frame1,width=5, justify=tk.RIGHT)\n    txtR.insert(tk.END ,\"255\")\n    txtR.grid(column=col, row=fila, padx=(5, 10), pady=(0, 0))\n    col = col + 1\n\n    lblG = tk.Label(frame1, text=\"Green:\")\n    lblG.grid(column=col, row=fila)\n    col = col + 1\n    txtG = tk.Entry(frame1,width=5, justify=tk.RIGHT)\n    txtG.insert(tk.END ,\"255\")\n    txtG.grid(column=col, row=fila, padx=(5, 10), pady=(0, 0))\n    col = col + 1\n\n    lblB = tk.Label(frame1, text=\"Blue:\")\n    lblB.grid(column=col, row=fila)\n    col = col + 1\n    txtB = tk.Entry(frame1,width=5, justify=tk.RIGHT)\n    txtB.insert(tk.END ,\"255\")\n    txtB.grid(column=col, row=fila, padx=(5, 10), pady=(0, 0))\n    col = 0\n    fila = fila + 1\n\n\n    chk_color_aleatori = tk.BooleanVar()\n    chk_color_aleatori.set(True) #set check state\n    chkAleatori = tk.Checkbutton(frame2, text='Color Aleatori', var=chk_color_aleatori, command=chekAleatoriChange)\n    chkAleatori.grid(column=col, row=fila)\n    fila = fila + 1\n    chekAleatoriChange()\n\n\n\n\n    fila =0\n    lblIntensitat = tk.Label(frame3, text=\"Intensitat:\")\n    lblIntensitat.grid(column=col, row=fila, pady=(40, 0))\n    col = col + 1\n    txtIntensitat = tk.Entry(frame3,width=5, justify=tk.RIGHT)\n    txtIntensitat.insert(tk.END ,\"255\")\n    txtIntensitat.grid(column=col, row=fila, padx=(5, 10), pady=(40, 0))\n\n\n\n    fila = fila + 1\n    col =0\n    lblTempsTotal = tk.Label(frame3, text=\"Temps Total:\")\n    lblTempsTotal.grid(column=col, row=fila, padx=(25, 10), pady=(40, 10))\n    col = col + 1\n    txtTempsTotal = tk.Entry(frame3,width=5, justify=tk.RIGHT)\n    txtTempsTotal.insert(tk.END ,\"5000\")\n    txtTempsTotal.grid(column=col, row=fila, padx=(5, 10), pady=(40, 10))\n\n\n    fila = fila + 1\n    col =0\n    lblTempsIteracio = tk.Label(frame3, text=\"Temps Iteració:\")\n    lblTempsIteracio.grid(column=col, row=fila, padx=(25, 10), pady=(0, 10))\n    col = col + 1\n    txtTempsIteracio = tk.Entry(frame3,width=5, justify=tk.RIGHT)\n    txtTempsIteracio.insert(tk.END ,\"50\")\n    txtTempsIteracio.grid(column=col, row=fila, padx=(5, 10), pady=(0, 10))\n    col = col + 1\n\n    fila = fila + 1\n    col =0\n    lblSubconjunt = tk.Label(frame3, text=\"Subconjunt:\")\n    lblSubconjunt.grid(column=col, row=fila, padx=(25, 10), pady=(0, 30))\n    col = col + 1\n    txtSubconjunt = tk.Entry(frame3,width=5, justify=tk.RIGHT)\n    txtSubconjunt.insert(tk.END ,\"3\")\n    txtSubconjunt.grid(column=col, row=fila, padx=(5, 10), pady=(0, 30))\n    col = col + 1\n\n    \n    \n    \n    #enviar text A PANTALLA\n    col =0\n    fila = 0\n    lblTextPantalla = tk.Label(framePantalla, text=\"Text:\")\n    lblTextPantalla.grid(column=col, row=fila,padx=(50, 0),)\n    col = col + 1\n    txtPantalla = tk.Entry(framePantalla,width=30, justify=tk.LEFT)\n    txtPantalla.insert(tk.END ,\"Feli# Sant Jordi\")\n    txtPantalla.grid(column=col, row=fila, padx=(5, 10), pady=(0, 0))\n    col = col + 1\n    \n    button_enviarPantalla = tk.Button(framePantalla, \n                       text=\"Send\", \n                       fg=\"black\",\n                       command=EnviarTextPantalla)\n    button_enviarPantalla.grid(column=col, row=fila, pady=(0, 0))\n\n    #enviar comanda al servidor\n    col =0\n    fila = 1\n    lblComanda = tk.Label(framePantalla, text=\"Comanda:\")\n    lblComanda.grid(column=col, row=fila,padx=(50, 0), pady=(10, 0),)\n    col = col + 1\n    txtComanda = tk.Entry(framePantalla,width=30, justify=tk.LEFT)\n    txtComanda.insert(tk.END ,\"|PANTALLA \")\n    txtComanda.grid(column=col, row=fila, padx=(5, 10), pady=(10, 0))\n    col = col + 1\n    \n    button_enviarComanda = tk.Button(framePantalla, \n                       text=\"Send\", \n                       command=EnviarTextComanda).grid(column=col, row=fila, pady=(10, 0))\n    \n    \n    framePantallaBotons = tk.Frame(frameConfig)\n    framePantallaBotons.pack()\n\n    tk.Button(framePantallaBotons,text=\"ct\", command=EnviarBanderaCat).grid(column=0, row=0, padx=(5, 0), pady=(10,0))\n    tk.Button(framePantallaBotons,text=\"gy\", command=EnviarBanderaGay).grid(column=1, row=0, padx=(5, 0), pady=(10,0))\n    tk.Button(framePantallaBotons,text=\"sm\", command=EnviarSmile     ).grid(column=2, row=0, padx=(5, 0), pady=(10,0))\n    tk.Button(framePantallaBotons,text=\"cd\", command=EnviarCountdown ).grid(column=3, row=0, padx=(5, 0), pady=(10,0))\n    tk.Button(framePantallaBotons,text=\"rs\", command=EnviarRosa      ).grid(column=4, row=0, padx=(5, 0), pady=(10,0))\n    tk.Button(framePantallaBotons,text=\"au\", command=EnviarAutomatic ).grid(column=5, row=0, padx=(5, 0), pady=(10,0))\n\n    \n    \n    \n    \n\n    frameSimulador = tk.Frame(root)\n    frameSimulador.pack(side=\"left\",padx=(25,0))\n    \n\n    \n    checkSimular = tk.IntVar()\n    checkButtonSimular = tk.Checkbutton(frameSimulador, text = \"Simular reproducció\", variable = checkSimular, \\\n                 onvalue = 1, offvalue = 0, height=5, \\\n                 width = 20)\n    checkButtonSimular.pack()\n    checkSimular.set(1)\n\n    #buttonOrdenar = tk.Button(frameSimulador, text=\"Ordenar Instruccions\", command=buttonOrdenarPress)\n    #buttonOrdenar.pack(pady=(0, 10))\n\n    \n    frameOrdres = tk.Frame(frameSimulador)\n    frameOrdres.pack()\n\n    lbOrdres = tk.Listbox(frameOrdres, selectmode='multiple',width=50, height=30)\n    lbOrdres.bind('<Delete>',lbOrdresEsborrarFila)\n    lbOrdres.bind(\"<<ListboxSelect>>\", lbSeleccionarTemps)\n    lbOrdres.pack(side=\"left\", fill=\"y\")\n    \n    scrollbarYOrdres = tk.Scrollbar(frameOrdres, orient=\"vertical\")\n    scrollbarYOrdres.config(command=lbOrdres.yview)\n    scrollbarYOrdres.pack(side=\"left\",fill=\"y\" ) \n    \n    lbOrdres.config(yscrollcommand=scrollbarYOrdres.set)\n    lbOrdres.activate(1)\n\n\n    \n\n\n    frameReproductor = tk.Frame(frameSimulador)\n    frameReproductor.pack()\n    \n    \n    fila=0\n    buttonRetrocedir = tk.Button(frameReproductor, text=\"7\", command=buttonBackPress)\n    buttonRetrocedir['font'] = font.Font(family='Webdings', size=20)\n    buttonRetrocedir.grid(column=0, row=fila,pady=(20, 20),padx=(10, 10))\n\n    buttonPlay = tk.Button(frameReproductor, text=\"4\", command=buttonPlayPausePress)\n    buttonPlay['font'] = font.Font(family='Webdings', size=20,)\n    buttonPlay.grid(column=1, row=fila,pady=(20, 20),padx=(10, 10))\n    col = col + 1\n    \n    #buttonPause = tk.Button(frameReproductor, text=\";\", command=buttonPausePress)\n    #buttonPause['font'] = font.Font(family='Webdings', size=20)\n    #buttonPause.grid(column=2, row=fila,pady=(20, 20),padx=(10, 10))\n\n    buttonAbansar = tk.Button(frameReproductor, text=\"8\", command=buttonAbansarPress)\n    buttonAbansar['font'] = font.Font(family='Webdings', size=20)\n    buttonAbansar.grid(column=2, row=fila,pady=(20, 20),padx=(10, 10))\n\n    buttonStop = tk.Button(frameReproductor, text=\"n\", command=buttonStopPress)\n    buttonStop['font'] = font.Font(family='Wingdings', size=20)\n    buttonStop.grid(column=3, row=fila,pady=(20, 20),padx=(10, 10))\n\n    \n    frameTempsIniciRepro = tk.Frame(frameSimulador)\n    frameTempsIniciRepro.pack()\n    \n    \n    lblTempsRepro = tk.Label(frameTempsIniciRepro, text=\"Temps actual:\")\n    lblTempsRepro.grid(column=0, row=0)\n    col = col + 1\n    txtTempsRepro = tk.Entry(frameTempsIniciRepro,width=10, justify=tk.RIGHT)\n    txtTempsRepro.insert(tk.END ,\"0\")\n    txtTempsRepro.grid(column=1, row=0, padx=(5, 10), pady=(0, 10))\n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    menubar = tk.Menu(root)\n    root.config(menu=menubar)\n\n    filemenu = tk.Menu(menubar, tearoff=0)\n    filemenu.add_command(label=\"ObrirMp3\",command=menuObrirMp3)\n    filemenu.add_command(label=\"Obrir Instruccions\",command=menuObrirInstruccions)\n    filemenu.add_command(label=\"Guardar Instruccions\",command=menuGuardarInstruccions)\n    #filemenu.add_separator()\n\n    menubar.add_cascade(label=\"Menú\", menu=filemenu)\n    \n  \n    \n    \n    \n    # A ESBORRAR !!! \n    file1 = open(FITXER_INSTRUCCIONS, 'r') \n    Lines = file1.readlines() \n    \n    lbOrdres.delete(0,lbOrdres.size())\n    for line in Lines: \n        lbOrdres.insert(tk.END,line )\n    file1.close() \n    #FINAL A ESBORRAR !!!\n    \n    \n    \n    #eventGestonarUSBBotons = Event()\n    #procesSimular = Process(target=procesLlegirBotoneraUSB, args=(eventGestonarUSBBotons,))\n    #procesSimular.start()\n\n    root.after(10000, procesLlegirBotoneraUSB)\n    \n    \n    try:\n        root.mainloop()\n\n    except:\n        TANCARCONNEXIONS()\n\n    #procesSimular.kill()\n    TANCARCONNEXIONS()\n","repo_name":"elmeuavi/RaspberryPiLed","sub_path":"Remot/ControladoraLedTCPIP.py","file_name":"ControladoraLedTCPIP.py","file_ext":"py","file_size_in_byte":60191,"program_lang":"python","lang":"ca","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10717612953","text":"\"\"\"Definition of Timer\nAdjusted from \nhttps://pypi.org/project/codetiming/\n\"\"\"\n\n# Standard library imports\nimport math\nimport time\nfrom contextlib import ContextDecorator\n\nfrom typing import Any, Callable, ClassVar, Optional, Union\n\ndef pretty_duration(secs):\n    hours, remaining_secs = divmod(secs, 3600.0)\n    minutes, remaining_secs = divmod(remaining_secs, 60.0)\n    seconds, remaining_secs = divmod(remaining_secs, 1.0)\n    msecs = remaining_secs*1e3\n    if int(hours) > 0:\n        return f\"{hours}h{minutes}m{round(seconds+remaining_secs)}s\"\n    if int(minutes) > 0:\n        return f\"{round(minutes)}m{round(seconds+remaining_secs)}s\"\n    if int(seconds) > 0:\n        return f\"{seconds+remaining_secs:.1f}s\"\n    return f\"{msecs:.1f}ms\"\n\nclass TimerError(Exception):\n    \"\"\"A custom exception used to report errors in use of Timer class\"\"\"\n\nclass Timer(ContextDecorator):\n    \"\"\"Time your code using a class, context manager, or decorator\"\"\"\n\n    def __init__(self, name):\n        self.name = name\n        self._start_time = None\n        self._last = None\n        self._checkpoints = []\n\n    def start(self) -> None:\n        \"\"\"Start a new timer\"\"\"\n        if self._start_time is not None:\n            raise TimerError(\"Timer is running. Use .stop() to stop it\")\n\n        self._start_time = time.perf_counter()\n        self._last = self._start_time\n\n    def elapsed(self, text) -> float:\n        \"\"\"Return how much time was spent so far, without stopping the timer\"\"\"\n        if self._start_time is None:\n            raise TimerError(\"Timer is not running. Use .start() to start it\")\n\n        # Calculate elapsed time\n        last = time.perf_counter()\n        elapsed_ms = (last - self._last)*1e3\n        self._last = last\n        self._checkpoints.append((text, elapsed_ms))\n        return elapsed_ms\n\n    def stop(self) -> float:\n        \"\"\"Stop the timer, and report the elapsed time\"\"\"\n        last = time.perf_counter()\n        total_secs = (last - self._start_time)\n        checkpoints = \" \".join(map(lambda text_ms: f\"[{text_ms[0]}={pretty_duration(text_ms[1])}]\", self._checkpoints))        \n\n        print (f\"[{self.name}] {pretty_duration(total_secs)} {checkpoints}\")\n        self._start_time = None\n        return last\n\n    def __enter__(self) -> \"Timer\":\n        \"\"\"Start a new timer as a context manager\"\"\"\n        self.start()\n        return self\n\n    def __exit__(self, *exc_info: Any) -> None:\n        \"\"\"Stop the context manager timer\"\"\"\n        self.stop()\n\nif __name__ == \"__main__\":\n    main ()\n","repo_name":"DaltonLens/DaltonLens-Research","sub_path":"charts/dlcharts/common/timer.py","file_name":"timer.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"13740882431","text":"################################################################################\n# This script runs an aerostructural optimization for the ScanEagle airplane,\n# a small drone used for recon missions. The geometry definition comes from\n# a variety of sources, including spec sheets and discussions with the\n# manufacturer, Insitu.\n#\n# Results using this model were presented in this paper:\n# https://arc.aiaa.org/doi/abs/10.2514/6.2018-1658\n# which was presented at AIAA SciTech 2018.\n#\n# This specific file is for John's fork and branch\n# https://github.com/johnjasa/OpenAeroStruct/tree/move_surface_vars\n# commit hash ee10ee86e0aec273d8e4db9cfe2871426d2e57a8\n################################################################################\n\nfrom __future__ import division, print_function\nimport numpy as np\n\nfrom openaerostruct.geometry.utils import generate_mesh\nfrom openaerostruct.integration.aerostruct_groups import AerostructGeometry, AerostructPoint\nfrom openmdao.api import IndepVarComp, Problem, SqliteRecorder, pyOptSparseDriver\n\nimport time\nnp.set_printoptions(precision=8)\nnp.set_printoptions(linewidth=150, suppress=True)\n\n# Initial default values\nmean_val_dict = {'mean_Ma' : 0.071,\n                 'mean_TSFC' : 9.80665 * 8.6e-6,\n                 'mean_W0' : 10.0,\n                 'mean_E' : 85.e9,\n                 'mean_G' : 25.e9,\n                 'mean_mrho' : 1600,\n                }\n\n\n# Total number of nodes to use in the spanwise (num_y) and\n# chordwise (num_x) directions. Vary these to change the level of fidelity.\nnum_y = 21 # 61 # 21\nnum_x = 3 # 7 # 3\n\n# Create a mesh dictionary to feed to generate_mesh to actually create\n# the mesh array.\nmesh_dict = {'num_y' : num_y,\n             'num_x' : num_x,\n             'wing_type' : 'rect',\n             'symmetry' : True,\n             'span_cos_spacing' : 0.5,\n             'span' : 3.11,\n             'root_chord' : 0.3,\n             }\n\nmesh = generate_mesh(mesh_dict)\n\n# Apply camber to the mesh\ncamber = 1 - np.linspace(-1, 1, num_x) ** 2\ncamber *= 0.3 * 0.05\n\nfor ind_x in range(num_x):\n    mesh[ind_x, :, 2] = camber[ind_x]\n\n# Introduce geometry manipulation variables to define the ScanEagle shape\nzshear_cp = np.zeros(10)\nzshear_cp[0] = .3\n\nxshear_cp = np.zeros(10)\nxshear_cp[0] = .15\n\nchord_cp = np.ones(10)\nchord_cp[0] = .5\nchord_cp[-1] = 1.5\nchord_cp[-2] = 1.3\n\nradius_cp = 0.01  * np.ones(10)\n\n# Define wing parameters\nsurface = {\n            # Wing definition\n            'name' : 'wing',        # name of the surface\n            'symmetry' : True,     # if true, model one half of wing\n                                    # reflected across the plane y = 0\n            'S_ref_type' : 'wetted', # how we compute the wing area,\n                                     # can be 'wetted' or 'projected'\n            'fem_model_type' : 'tube',\n\n            'taper' : 0.8,\n            'zshear_cp' : zshear_cp,\n            'xshear_cp' : xshear_cp,\n            'chord_cp' : chord_cp,\n            'sweep' : 20.,\n            'twist_cp' : np.array([2.5, 2.5, 5.]), #np.zeros((3)),\n            'thickness_cp' : np.ones((3))*.008,\n\n            # Give OAS the radius and mesh from before\n            'radius_cp' : radius_cp,\n            'mesh' : mesh,\n\n            # Aerodynamic performance of the lifting surface at\n            # an angle of attack of 0 (alpha=0).\n            # These CL0 and CD0 values are added to the CL and CD\n            # obtained from aerodynamic analysis of the surface to get\n            # the total CL and CD.\n            # These CL0 and CD0 values do not vary wrt alpha.\n            'CL0' : 0.0,            # CL of the surface at alpha=0\n            'CD0' : 0.015,            # CD of the surface at alpha=0\n\n            # Airfoil properties for viscous drag calculation\n            'k_lam' : 0.05,         # percentage of chord with laminar\n                                    # flow, used for viscous drag\n            't_over_c_cp' : np.array([0.12]),      # thickness over chord ratio\n            'c_max_t' : .303,       # chordwise location of maximum (NACA0015)\n                                    # thickness\n            'with_viscous' : True,\n            'with_wave' : False,     # if true, compute wave drag\n\n            # Material properties taken from http://www.performance-composites.com/carbonfibre/mechanicalproperties_2.asp\n            'yield' : 350.e6,\n\n            'fem_origin' : 0.35,    # normalized chordwise location of the spar\n            'wing_weight_ratio' : 1., # multiplicative factor on the computed structural weight\n            'struct_weight_relief' : True,    # True to add the weight of the structure to the loads on the structure\n            'distributed_fuel_weight' : False,\n            # Constraints\n            'exact_failure_constraint' : False, # if false, use KS function\n            }\n\n# Create the problem and assign the model group\nprob = Problem()\n\n# Add problem information as an independent variables component\nindep_var_comp = IndepVarComp()\nindep_var_comp.add_output('v', val=22.876, units='m/s')\nindep_var_comp.add_output('alpha', val=5., units='deg')\nindep_var_comp.add_output('re', val=1.e6, units='1/m')\nindep_var_comp.add_output('rho', val=0.770816, units='kg/m**3')\nindep_var_comp.add_output('R', val=1800e3, units='m')\nindep_var_comp.add_output('speed_of_sound', val=322.2, units='m/s')\nindep_var_comp.add_output('load_factor', val=1.)\nindep_var_comp.add_output('empty_cg', val=np.array([0.2, 0., 0.]), units='m')\n\nindep_var_comp.add_output('Mach_number', val=mean_val_dict['mean_Ma'])\nindep_var_comp.add_output('CT', val=mean_val_dict['mean_TSFC'], units='1/s')\nindep_var_comp.add_output('W0', val=mean_val_dict['mean_W0'],  units='kg')\nindep_var_comp.add_output('E', val=mean_val_dict['mean_E'], units='N/m**2')\nindep_var_comp.add_output('G', val=mean_val_dict['mean_G'], units='N/m**2')\nindep_var_comp.add_output('mrho', val=mean_val_dict['mean_mrho'], units='kg/m**3')\n\nprob.model.add_subsystem('prob_vars',\n     indep_var_comp,\n     promotes=['*'])\n\n# Add the AerostructGeometry group, which computes all the intermediary\n# parameters for the aero and structural analyses, like the structural\n# stiffness matrix and some aerodynamic geometry arrays\naerostruct_group = AerostructGeometry(surface=surface)\n\nname = 'wing'\n\n# Add the group to the problem\nprob.model.add_subsystem(name, aerostruct_group,\n    promotes_inputs=['load_factor'])\n\npoint_name = 'AS_point_0'\n\n# Create the aerostruct point group and add it to the model.\n# This contains all the actual aerostructural analyses.\nAS_point = AerostructPoint(surfaces=[surface])\n\nprob.model.add_subsystem(point_name, AS_point,\n    promotes_inputs=['v', 'alpha', 'Mach_number', 're', 'rho', 'CT', 'R',\n        'W0', 'speed_of_sound', 'empty_cg', 'load_factor'])\n\n# Issue quite a few connections within the model to make sure all of the\n# parameters are connected correctly.\ncom_name = point_name + '.' + name + '_perf'\nprob.model.connect(name + '.local_stiff_transformed', point_name + '.coupled.' + name + '.local_stiff_transformed')\nprob.model.connect(name + '.nodes', point_name + '.coupled.' + name + '.nodes')\n\n# Connect aerodyamic mesh to coupled group mesh\nprob.model.connect(name + '.mesh', point_name + '.coupled.' + name + '.mesh')\n\n# Connect performance calculation variables\nprob.model.connect(name + '.radius', com_name + '.radius')\nprob.model.connect(name + '.thickness', com_name + '.thickness')\nprob.model.connect(name + '.nodes', com_name + '.nodes')\nprob.model.connect(name + '.cg_location', point_name + '.' + 'total_perf.' + name + '_cg_location')\nprob.model.connect(name + '.structural_weight', point_name + '.' + 'total_perf.' + name + '_structural_weight')\nprob.model.connect(name + '.t_over_c', com_name + '.t_over_c')\n\nprob.model.connect('mrho', name + '.struct_setup.structural_weight.mrho')\nprob.model.connect('E', name + '.struct_setup.assembly.E')\nprob.model.connect('G', name + '.struct_setup.assembly.G')\nprob.model.connect('E', com_name + '.struct_funcs.vonmises.E')\nprob.model.connect('G', com_name + '.struct_funcs.vonmises.G')\n\n# Set the optimizer type\n# from openmdao.api import ScipyOptimizeDriver\nprob.driver = pyOptSparseDriver() # ScipyOptimizeDriver()\n# prob.driver.options['tol'] = 1e-7\nprob.driver.options['optimizer'] = 'SNOPT'\n# prob.driver.options['gradient method'] = 'pyopt_fd' # 'snopt_fd'\nprob.driver.options['print_results'] = True\nprob.driver.opt_settings['Major feasibility tolerance'] = 1e-9\n\n# Record data from this problem so we can visualize it using plot_wing\nrecorder = SqliteRecorder(\"aerostruct.db\")\nprob.driver.add_recorder(recorder)\nprob.driver.recording_options['record_derivatives'] = True\nprob.driver.recording_options['includes'] = ['*']\n\n# Setup problem and add design variables.\n# Here we're varying twist, thickness, sweep, and alpha.\nprob.model.add_design_var('wing.twist_cp', lower=-5., upper=10.)\nprob.model.add_design_var('wing.thickness_cp', lower=0.001, upper=0.01, scaler=1e3)\nprob.model.add_design_var('wing.sweep', lower=10., upper=30.)\nprob.model.add_design_var('alpha', lower=-10., upper=10.)\n\n# Make sure the spar doesn't fail, we meet the lift needs, and the aircraft\n# is trimmed through CM=0.\nprob.model.add_constraint('AS_point_0.wing_perf.failure', upper=0.)\nprob.model.add_constraint('AS_point_0.wing_perf.thickness_intersects', upper=0.)\nprob.model.add_constraint('AS_point_0.L_equals_W', equals=0.)\n\n# Instead of using an equality constraint here, we have to give it a little\n# wiggle room to make SLSQP work correctly.\nprob.model.add_constraint('AS_point_0.CM', lower=-0.001, upper=0.001)\nprob.model.add_constraint('wing.twist_cp', lower=np.array([-1e20, -1e20, 5.]), upper=np.array([1e20, 1e20, 5.]))\n\n# We're trying to minimize fuel burn\nprob.model.add_objective('AS_point_0.fuelburn', scaler=.1)\n\n# Set up the problem\nprob.setup()\n\n# # Use this if you just want to run analysis and not optimization\n# prob.run_model()\n# # print(\"fval = \", prob['AS_point_0.fuelburn'][0])\n# # print(\"twist = \", prob['wing.geometry.twist'])\n# # print(\"thickness =\", prob['wing.thickness'])\n# # print(\"sweep = \", prob['wing.sweep'])\n# # print(\"aoa = \", prob['alpha'])\n# # print(\"nodes = \", np.round(prob['AS_point_0.coupled.wing.struct_states.struct_weight_loads.nodes'][:,1], 5))\n\n#-----------------------VALUE PLUGIN TEST---------------------------------------\nprob['wing.twist_cp'] = 2.5*np.ones(3) # np.array([2.60830137, 10., 5.])\nprob['wing.thickness_cp'] = 1.e-3 * np.array([5.5, 5.5, 5.5]) # np.array([0.001, 0.001, 0.001])\nprob['wing.sweep'] = 20. # [18.89098985]\nprob['alpha'] = 5. # [2.19244059]\n\n# prob['Mach_number'] = 0.071\n# prob['CT'] = 9.80665 * 8.6e-6\n# prob['W0'] = 10.\n# prob['E'] = 85.e9\n# prob['G'] = 2.50000005e+10\n# prob['mrho'] = 1.60000012e+03\n\nprob.run_model()\nprob.run_model()\nprint(\"fuel burn = \", prob['AS_point_0.fuelburn'][0])\nprint('KS = ', prob['AS_point_0.wing_perf.failure'][0])\nprint('lift_con = ', prob['AS_point_0.L_equals_W'][0])\nprint('CM constraint = ', prob['AS_point_0.CM'][1])\nderiv_arr = np.zeros(6)\nderiv = prob.compute_totals(of=['AS_point_0.fuelburn'],\n                    wrt=['Mach_number', 'CT', 'W0', 'E', 'G', 'mrho', 'R', 'load_factor'])\nprint()\nprint(deriv['AS_point_0.fuelburn', 'Mach_number'][0])\nprint(deriv['AS_point_0.fuelburn', 'CT'][0])\nprint(deriv['AS_point_0.fuelburn', 'W0'][0])\nprint(deriv['AS_point_0.fuelburn', 'E'][0,0])\nprint(deriv['AS_point_0.fuelburn', 'G'][0,0])\nprint(deriv['AS_point_0.fuelburn', 'mrho'][0])\nprint(deriv['AS_point_0.fuelburn', 'R'][0])\nprint(deriv['AS_point_0.fuelburn', 'load_factor'][0])\n# deriv_arr[0] = deriv['AS_point_0.fuelburn', 'Mach_number']\n# deriv_arr[1] = deriv['AS_point_0.fuelburn', 'CT']\n# deriv_arr[2] = deriv['AS_point_0.fuelburn', 'W0']\n# deriv_arr[3] = deriv['AS_point_0.fuelburn', 'E']\n# deriv_arr[4] = deriv['AS_point_0.fuelburn', 'G']\n# deriv_arr[5] = deriv['AS_point_0.fuelburn', 'mrho']\n# print('deriv_arr = ', deriv_arr)\n\n#------------------------SETUP BUG REPRODUCTION---------------------------------\n# new_Ma = 0.071 + 0.005\n# prob['Mach_number'] = new_Ma\n# prob.run_model()\n\n# prob.setup()\n# prob.run_model()\n# print(\"fval = \", prob['AS_point_0.fuelburn'][0])\n\n# print(\"Mach_number = \", prob['Mach_number'])\n# prob.run_model()\n# print(\"fval = \", prob['AS_point_0.fuelburn'][0])\n\n#--------------------FINITE DIFFERENCE -----------------------------------------\n# fval1 = prob['AS_point_0.fuelburn'][0]\n# # Check against finite difference\n# pert = 1.e-6\n# surface['mrho'] += pert\n# prob.run_model()\n# fval2 = prob['AS_point_0.fuelburn'][0]\n# dfval = (fval2 - fval1) / pert\n# print(\"dfval = \", dfval)\n\n#----------------------------OPTIMIZATION--------------------------------------\n# Actually run the optimization problem\n# start_time = time.time()\n# prob.run_driver()\n# elapsed_time = time.time() - start_time\n# print(\"fval = \", prob['AS_point_0.fuelburn'][0])\n# print(\"twist_cp = \", prob['wing.twist_cp'])\n# print(\"thickness_cp = \", prob['wing.thickness_cp'])\n# print(\"twist = \", prob['wing.geometry.twist'])\n# print(\"thickness =\", prob['wing.thickness'])\n# print(\"sweep = \", prob['wing.sweep'])\n# print('alpha = ', prob['alpha'])\n# print('elapsed_time = ', elapsed_time)\n","repo_name":"OptimalDesignLab/pyStatReduce","sub_path":"pystatreduce/openmdao/run_scaneagle_john.py","file_name":"run_scaneagle_john.py","file_ext":"py","file_size_in_byte":13178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70300584890","text":"\"\"\"empty message\n\nRevision ID: 33bc683404bd\nRevises: 1312d9703e28\nCreate Date: 2020-04-16 08:39:11.784676\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '33bc683404bd'\ndown_revision = '1312d9703e28'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('cars', sa.Column('rst_company', sa.String(length=30), nullable=True))\n    op.add_column('cars', sa.Column('rst_date', sa.Date(), nullable=True))\n    op.add_column('cars', sa.Column('status', sa.String(length=10), nullable=True))\n    op.add_column('cars', sa.Column('updated', sa.DateTime(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('cars', 'updated')\n    op.drop_column('cars', 'status')\n    op.drop_column('cars', 'rst_date')\n    op.drop_column('cars', 'rst_company')\n    # ### end Alembic commands ###\n","repo_name":"rsentra/glad-web","sub_path":"migrations/versions/33bc683404bd_.py","file_name":"33bc683404bd_.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73593980087","text":"'''\nPlot location of the cell towers in Bulgaria.\n\nAuthor: Axel.Tidemann@telenor.com\n\n'''\n\nimport argparse\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap\nfrom pykml.factory import KML_ElementMaker as KML\nfrom lxml import etree\n\nparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('h5_file', help='HDF5 file')\nparser.add_argument('--frame_table',\n                    help='Frame table to read from HDF5 file',\n                    default='site_info')\nparser.add_argument('--output_file',\n                    help='KML filename',\n                    default='sofia.kml')\nparser.add_argument('--region',\n                    help='What region to select',\n                    default='SOFIA_CITY')\nargs = parser.parse_args()\n\nsite_info = pd.read_hdf(args.h5_file, args.frame_table)\nregion = site_info.query(\"Region == '{}'\".format(args.region))\n\nkml = KML.Document()\nkml.append(KML.Folder())\n\nfor lon,lat,site_id in zip(region.Longitude.values, region.Latitude.values, region.index):\n    kml.Folder.append(KML.Placemark(KML.name(site_id), KML.Point(KML.coordinates('{},{}'.format(lon, lat)))))\n\nwith open(args.output_file, 'w') as f:\n    f.write(etree.tostring(kml))\n","repo_name":"axeltidemann/propeller","sub_path":"globul/globul_plot.py","file_name":"globul_plot.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"11790186006","text":"#!/usr/bin/env python \nimport sys, os\nimport util\nfrom path import path\n\nassert len(sys.argv) > 1\n\nwork_dir = path(sys.argv[1])\nlog_dir = work_dir.parent / \"workers\"\n\nif not log_dir.exists():\n    log_dir.mkdir()\nhost = os.uname()[1].split('.')[0]\nlog_file = log_dir / host\n\nf = log_file.open('w')\nf.write(\"manager on %s starting with PID %d\\n\" % (host, os.getpid()))\nf.flush()\nwhile 1:\n    args = util.get_next_job(work_dir)\n    if args:\n        f.write('starting job \"%s\"\\n' % args)\n        f.flush()\n        retcode = os.system('deal/scripts/exec.sh deal/scripts/run.sh ' + args)\n        if retcode == 0:\n            f.write('finished.')\n        else:\n            f.write('ERROR: job finished with exit code %d\\n' % retcode)\n        f.flush()\n    else:\n        f.write(\"No more work - shuting down\\n\")\n        f.close()\n        sys.exit(0)\n\n\n\n","repo_name":"bloodearnest/deal","sub_path":"scripts/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"43771337719","text":"from django.urls import path\r\nfrom . import views \r\nfrom rest_framework import routers\r\nfrom .views import product_list ,ProductViewSet , CategorySerializer\r\n\r\n\r\n\r\nrouter = routers.DefaultRouter()\r\nrouter.register(r'',ProductViewSet,CategorySerializer)\r\n\r\napp_name = 'myshop'\r\n\r\nurlpatterns = [\r\n    path('',product_list, name='product_list'),\r\n    path('product/',ProductViewSet.as_view({'get':'list'}),name='products'),\r\n    path('order/',CategorySerializer.as_view({'get':'list'}),name='orders'),\r\n    path('<slug:category_slug>/', views.product_list,\r\n         name='product_list_by_category'\r\n         ),\r\n    path('<int:id>/<slug:slug>', views.product_detail,\r\n         name='product_detail')\r\n]","repo_name":"Baizak04/shop","sub_path":"globus/shop/myshop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20429311993","text":"from collections import defaultdict\nfrom collections import defaultdict\ndef solution(record):\n    answer = []\n    house = defaultdict(str)\n    print(house)\n    for i in record:\n        user = list(map(str, i.split()))\n        if user[0] == \"Enter\":\n            house[user[1]] = user[2]\n            answer.append((user[1],0))\n        elif user[0] == \"Leave\":\n            answer.append((user[1],1))\n        elif user[0] == \"Change\":\n            house[user[1]] = user[2]\n    bank = []\n    for user, action in answer:\n        if action == 1:\n            bank.append(f\"{house[user]}님이 들어왔습니다.\")\n        else:\n            bank.append(f\"{house[user]}님이 나갔습니다.\")\n    return bank\n\n\nsolution([\"Enter uid1234 Muzi\", \"Enter uid4567 Prodo\",\"Leave uid1234\",\"Enter uid1234 Prodo\",\"Change uid4567 Ryan\"])","repo_name":"Young312886/TIL","sub_path":"Algorithm/Python/Programmers/오픈채팅방.py","file_name":"오픈채팅방.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27336589083","text":"class graph:\n    def __init__(self):\n        self.store = []\n\n    def addVertex(self, n):\n        if not isinstance(n, int):\n            return -1\n        if n < 1:\n            return -1\n        for i in range(0,n,1):\n            self.store += [[]]\n        return len(self.store)\n\n    def addEdge(self, from_idx, to_idx, directed, weight):\n        if not isinstance(directed, bool):\n            return False\n        if not (isinstance(from_idx, int) and isinstance(to_idx, int) and isinstance(weight, int)):\n            return False\n        if (from_idx < 0) or (to_idx < 0) or (weight == 0):\n            return False\n        length = len(self.store)\n        if (from_idx >= length) or (to_idx >= length):\n            return False\n        for i in self.store[from_idx]:\n            if i[0] == to_idx:\n                return False\n        if directed is False:\n            for i in self.store[to_idx]:\n                if i[0] == from_idx:\n                    return False\n            self.store[to_idx] += [[from_idx, weight]]\n        self.store[from_idx] += [[to_idx, weight]]\n        return True\n\n    def traverse(self, start, typeBreadth):\n        r_list = []\n        if not ((isinstance(start, int) or start is None) and isinstance(typeBreadth, bool)):\n            return r_list\n        if not (start in range(0, len(self.store), 1) or start is None):\n            return r_list\n        found_list = [False for i in range(0, len(self.store), 1)]\n        processed_list = [False for i in range(0, len(self.store), 1)]\n        line = []\n        temp_list = []\n        if start is None:\n            for i in range(0, len(self.store), 1):\n                if found_list[i] is False:\n                    line += [i]\n                    found_list[i] = True\n                while len(line) != 0:\n                    if typeBreadth is True:\n                    # Breadth First, Use Queue\n                        node = line[0]\n                        line = line[1:]\n                    else:\n                    # Depth First, Use Stack\n                        node = line[-1]\n                        line = line[:-1]\n                    if processed_list[node] is False:\n                        temp_list += [node]\n                        processed_list[node] = True\n                        child_unprocessed = False\n                        for i in self.store[node]:\n                            child = i[0]\n                            if found_list[child] is False:\n                                line += [child]\n                                found_list[child] = True\n                            if processed_list[child] is False:\n                                child_unprocessed = True\n                        if child_unprocessed == False:\n                            r_list += [temp_list]\n                            temp_list = []\n        if isinstance(start, int):\n            line += [start]\n            while len(line) != 0:\n                if typeBreadth is True:\n                # Breadth First, Use Queue\n                    node = line[0]\n                    line = line[1:]\n                else:\n                # Depth First, Use Stack\n                    node = line[-1]\n                    line = line[:-1]\n                if processed_list[node] is False:\n                    r_list += [node]\n                    processed_list[node] = True\n                    for i in self.store[node]:\n                        child = i[0]\n                        if found_list[child] is False:\n                            line += [child]\n                            found_list[child] = True\n        return r_list\n\n    def connectivity(self, vx, vy):\n        element = [False, False]\n        if not (isinstance(vx, int) and isinstance(vy, int)):\n            return element\n        if not (vx in range(0, len(self.store), 1) and vy in range(0, len(self.store), 1)):\n            return element\n        vx_connected = self.traverse(vx, True)\n        vy_connected = self.traverse(vy, True)\n        if vy in vx_connected:\n            element[0] = True\n        if vx in vy_connected:\n            element[1] = True\n        return element\n\n    def path(self,vx,vy):\n        if not (isinstance(vx, int) and isinstance(vy, int)):\n            return [[],[]]\n        if not (vx in range(0, len(self.store), 1) and vy in range(0, len(self.store), 1)):\n            return [[],[]]\n        path_to = []\n        path_back = []\n        visited_list = [False] * len(self.store)\n        path_to = self.path_helper(vx, vy, path_to, visited_list)\n        visited_list = [False] * len(self.store)\n        path_back = self.path_helper(vy, vx, path_back, visited_list)\n        element = [path_to, path_back]\n        return element\n\n    def path_helper(self, from_idx, to_idx, path, visited_list):\n        visited_list[from_idx] = True\n        path.append(from_idx)\n        if from_idx == to_idx:\n            return path\n        else:\n            for i in self.store[from_idx]:\n                successor_node = i[0]\n                if visited_list[successor_node] is False:\n                    path = self.path_helper(successor_node, to_idx, path, visited_list)\n                    if to_idx in path:\n                        return path\n        path = path[:-1]\n        visited_list[from_idx] = False\n        return path\n","repo_name":"EdwardJTL/CSC190","sub_path":"Labs/Lab 5/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":5298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20450078686","text":"from rss_feed.domain.add_feed_items_repo import AddFeedItemsRepo\nfrom rss_feed.models import FeedItem\n\n\nclass AddFeedItemsRepoDBImpl(AddFeedItemsRepo):\n    def get_last_item(self, channel_id):\n        return FeedItem.objects.only('published_datetime').order_by('published_datetime').last()\n\n    def create_items(self, channel_id, items):\n        feed_item_objects = []\n        for item in items:\n            feed_item_objects.append(\n                FeedItem(channel_id=channel_id, link=item['link'], title=item['title'], summery=item['summary'],\n                         published_datetime=item['published_parsed'])\n            )\n\n        FeedItem.objects.bulk_create(feed_item_objects)\n","repo_name":"saber-solooki/sample-feed","sub_path":"feedy/rss_feed/data/add_feed_items_repo.py","file_name":"add_feed_items_repo.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7323666967","text":"import torch\nimport torch.nn as nn\nfrom .common import conv1x1, conv3x3\nimport math\n\n# ---------------------------Small Data Sets Like CIFAR-10 or CIFAR-100----------------------------\n# both-preact | half-preact | no-preact\nclass PreResidualBlock(nn.Module):\n    \"\"\"\n    base module for PreResNet on small data sets\n    \"\"\"\n    def __init__(self, in_plane, out_plane, stride=1, down_sample=None, block_type=\"both-preact\"):\n        \"\"\"\n        init module and weights\n        :param in_plane: size of input plane\n        :param out_plane: size of output plane\n        :param stride: stride of convolutional layers, default 1\n        :param down_sample: down sample type for expand dimension of input feature maps, default None\n        :param block_type: type of blocks, decide position of short cut, both-preact: short cut start from beginning\n        of the first segment, half-preact: short cut start from the position between the first segment and the second\n        one. no-preact: short cut start from beginning of second segment and remove first segment. default: both-preact\n        \"\"\"\n        super(PreResidualBlock, self).__init__()\n        self.block_type = block_type\n        self.down_sample = down_sample\n\n        self.first_segment = nn.Sequential(\n            nn.BatchNorm2d(in_plane),\n            nn.ReLU(inplace=True)\n        )\n        self.second_segment = nn.Sequential(\n            conv3x3(in_plane, out_plane, stride),\n            nn.BatchNorm2d(out_plane),\n            nn.ReLU(inplace=True),\n            conv3x3(out_plane, out_plane)\n        )\n\n    def forward(self, x):\n        \"\"\"\n        forward procedure of residual module\n        :param x: input feature maps\n        :return: output feature maps\n        \"\"\"\n        if self.block_type == \"half-preact\":\n            out = self.first_segment(x)\n            residual = out\n            out = self.second_segment(out)\n        elif self.block_type == \"no-preact\":\n            residual = x\n            out = self.second_segment(x)\n        else:\n            residual = x\n            out = self.first_segment(x)\n            out = self.second_segment(out)\n\n        if self.down_sample:\n            residual = self.down_sample(residual)\n        out = out + residual\n        return out\n\n\nclass PreResNet(nn.Module):\n    \"\"\"\n    define PreResNet on small data sets\n    \"\"\"\n    def __init__(self, depth, wide_factor=1, num_classes=10):\n        \"\"\"\n        init model and weights\n        :param depth: depth of network\n        :param wide_factor: wide factor for deciding width of network, default is 1\n        :param num_classes: number of classes, related to labels. default 10\n        \"\"\"\n        super(PreResNet, self).__init__()\n\n        block = PreResidualBlock\n        self.in_plane = 16 * wide_factor\n        self.depth = depth\n        n = (depth - 2) / 6\n        self.conv = conv3x3(3, 16 * wide_factor)\n        self.module_1 = self.make_layer(block, 16 * wide_factor, n)\n        self.module_2 = self.make_layer(block, 32 * wide_factor, n, 2)\n        self.module_3 = self.make_layer(block, 64 * wide_factor, n, 2)\n        self.bn = nn.BatchNorm2d(64 * wide_factor)\n        self.relu = nn.ReLU(inplace=True)\n        self.avg_pool = nn.AvgPool2d(7)\n        self.fc = nn.Linear(64 * wide_factor, num_classes)\n        \n        self._init_weight()\n\n    def _init_weight(self):\n        # init layer parameters\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\n    def make_layer(self, block, out_plane, n_blocks, stride=1):\n        \"\"\"\n        make residual blocks, including short cut and residual function\n        :param block: type of basic block to build network\n        :param out_plane: size of output plane\n        :param n_blocks: number of blocks on every segment\n        :param stride: stride of convolutional neural network, default 1\n        :return: residual blocks\n        \"\"\"\n        down_sample = None\n        if (stride != 1) or (self.in_plane != out_plane):\n            down_sample = nn.Sequential(\n                conv1x1(self.in_plane, out_plane, stride=stride))\n\n        layers = []\n        layers.append(block(self.in_plane, out_plane, stride, down_sample, block_type=\"half-preact\"))\n        self.in_plane = out_plane\n        for i in range(1, n_blocks):\n            layers.append(block(out_plane, out_plane))\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        \"\"\"\n        forward procedure of model\n        :param x: input feature maps\n        :return: output feature maps\n        \"\"\"\n        out = self.conv(x)\n        out = self.module_1(out)\n        out = self.module_2(out)\n        out = self.module_3(out)\n        out = self.bn(out)\n        out = self.relu(out)\n        out = self.avg_pool(out)\n        out = out.view(out.size(0), -1)\n        out = self.fc(out)\n        return out\n\n\n# ---------------------------ImageNet----------------------------------------------------------\n# both-preact | half-preact | no-preact\nclass PreBottleNeck(nn.Module):\n    \"\"\"\n    basic module for ImageNet PreResNet\n    \"\"\"\n    def __init__(self, in_plane, out_plane, stride=1, down_sample=None, block_type=\"both-preact\"):\n        \"\"\"\n        init module and weights\n        :param in_plane: size of input plane\n        :param out_plane: size of output plane\n        :param stride: stride of convolutional layers, default 1\n        :param down_sample: down sample type for expand dimension of input feature maps, default None\n        :param block_type: type of blocks, decide position of short cut, both-preact: short cut start from beginning\n        of the first segment, half-preact: short cut start from the position between the first segment and the second\n        one. no-preact: short cut start from beginning of second segment and remove first segment. default: both-preact\n        \"\"\"\n        super(PreBottleNeck, self).__init__()\n        self.block_type = block_type\n        self.down_sample = down_sample\n\n        inner_outplane = out_plane//4\n        if block_type != \"no-preact\":\n            self.first_segment = nn.Sequential(\n                nn.BatchNorm2d(in_plane),\n                nn.ReLU(inplace=True)\n            )\n        self.second_segment = nn.Sequential(\n            conv1x1(in_plane, inner_outplane, stride),\n            nn.BatchNorm2d(inner_outplane),\n            nn.ReLU(inplace=True),\n            conv3x3(inner_outplane, inner_outplane),\n            nn.BatchNorm2d(inner_outplane),\n            nn.ReLU(inplace=True),\n            conv1x1(inner_outplane, out_plane)\n        )\n\n    def forward(self, x):\n        \"\"\"\n        forward procedure of module\n        :param x: input feature maps\n        :return: output feature maps\n        \"\"\"\n        if self.block_type == \"half-preact\":\n            out = self.first_segment(x)\n            residual = out\n            out = self.second_segment(out)\n        elif self.block_type == \"no-preact\":\n            residual = x\n            out = self.second_segment(x)\n        else:\n            residual = x\n            out = self.first_segment(x)\n            out = self.second_segment(out)\n        if self.down_sample:\n            residual = self.down_sample(residual)\n        out = out + residual\n        return out\n\n","repo_name":"bohanzhuang/Towards-Effective-Low-bitwidth-Convolutional-Neural-Networks","sub_path":"ipytorch/models/custom/PreResNet.py","file_name":"PreResNet.py","file_ext":"py","file_size_in_byte":7460,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"3431368729","text":"import sys\nimport os\nimport math\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nclass Vertex:\n    def __init__(self, identifier: int, coordinates: list[float], orientation: list[float]) -> None:\n        self.id = identifier\n        self.coordinates = coordinates\n        self.orientation = orientation\n\n    def __str__(self) -> str:\n        return f\"ID: {self.id}\\nCoord: {self.coordinates}\\nOrient: {self.orientation}\"\n\n\nclass Pline:\n    def __init__(self, label: int, vertex_count: int, vertices: list[Vertex]) -> None:\n        self.label = label\n        self.vertex_count = vertex_count\n        self.vertices = vertices\n\n    def __str__(self) -> str:\n        return f\"Polyline {self.label} with {self.vertex_count} vertices\"\n\n\ndef read_pline(path: str) -> list[Pline]:\n    \"\"\"Read a polyline from a `.pline` file.\n\n    Args:\n        path (str): Path to the file.\n\n    Returns:\n        list[Pline]: List of polylines in the file.\n    \"\"\"\n    print(f\"Parsing file {path}\")\n    with open(path) as f:\n        lines = f.readlines()\n\n    pline_strings = []\n    for line in lines:\n        if not line.startswith('#'):\n            pline_strings.append(line[:-1])\n\n    plines = []\n    for pline in pline_strings:\n        pline_string = str.split(pline, \" \")\n        pline_string.reverse()\n\n        pline_label = int(pline_string.pop())\n        pline_nvert = int(pline_string.pop())\n        pline_verts = []\n        while len(pline_string) % 7 == 0 and len(pline_string) > 0: \n            new_id = int(pline_string.pop())\n            \n            new_coord = [float(x) for x in pline_string[-3:]]\n            new_coord.reverse()\n            del pline_string[-3:]\n            \n            new_orient = [float(x) for x in pline_string[-3:]]\n            new_orient.reverse()\n            del pline_string[-3:]\n            \n            new_vertex = Vertex(new_id, new_coord, new_orient)\n            pline_verts.append(new_vertex)\n        \n        new_pline = Pline(pline_label, pline_nvert, pline_verts)\n        plines.append(new_pline)\n        print(f\"{new_pline} eingelesen\")\n    return plines\n\n\ndef last_in_circle(pline: Pline, start_index: int, radius: int, direction=\"right\") -> int:\n    \"\"\"From a circle around a vertex of a polyline, find last following vertex that is still inside the circle.\n\n    Args:\n        pline (Pline): A polyline.\n        start_index (int): The index of the vertex in the center of the circle.\n        radius (int): The radius of the circle.\n        direction (str, optional): Choose if a vertex right or left from the original vertex is looked for.\n            Defaults to \"right\".\n\n    Returns:\n        int: The index of the last vertex following the original vertex that is still inside the circle.\n    \"\"\"\n    last_vertex_index = start_index\n    while True:\n        if direction == \"right\":\n            next_vertex_index = (last_vertex_index + 1) % len(pline.vertices)\n            if next_vertex_index == start_index:\n                return\n        else:\n            next_vertex_index = (last_vertex_index - 1) % len(pline.vertices)\n        distance = math.dist(pline.vertices[start_index].coordinates,\n                             pline.vertices[next_vertex_index].coordinates)\n        if distance > radius:\n            break\n        last_vertex_index = next_vertex_index\n    return last_vertex_index\n\n\ndef calc_beta(center: Vertex, last_in: Vertex, first_out: Vertex) -> float:\n    \"\"\"Calculate the angle beta\n\n    Args:\n        center (Vertex): Middle point of the circle.\n        last_in (Vertex): Last vertex still inside the circle.\n        first_out (Vertex): First vertex outside of the circle.\n\n    Returns:\n        float: beta in DEG.\n    \"\"\"\n    center_vector = list(map(lambda a, b: a - b, center.coordinates, last_in.coordinates))\n    out_vector = list(map(lambda a, b: a - b, first_out.coordinates, last_in.coordinates))\n\n    beta = np.radians(180) - np.arccos(np.dot(center_vector, out_vector) /\n                                       (np.linalg.norm(center_vector) * np.linalg.norm(out_vector)))\n    return beta\n\n\ndef filter_response(i: Vertex, c: Vertex, beta: float, r: float) -> float:\n    d = math.dist(c.coordinates, i.coordinates)\n    p = 2 * d * math.cos(beta)\n    q = d + 0.5 * (-p + math.sqrt(p ** 2 - 4 * (d ** 2 - r ** 2)))\n    return q\n\n\ndef run_length(qp: float, qm: float, r: float) -> float:\n    q = 0.5 * (qp / r + qm / r)\n    return q\n\n\ndef calc_alpha(q: float, beta: float, r: float) -> float:  # bekommt das q von filter response, nicht run length\n    alpha = np.arccos(q * math.cos(beta) / r)\n    return alpha\n\n\ndef transform_pline(pline: Pline):\n    xs = []\n    ys = []\n    zs = []\n    for vertex in pline.vertices:\n        xs.append(vertex.coordinates[0])\n        ys.append(vertex.coordinates[1])\n        zs.append(vertex.coordinates[2])\n    coordinates = [xs, ys, zs]\n    return coordinates\n\n    \n\ndef visualize_pline(pline: Pline, filter_responses: list[float]=None):\n    points = transform_pline(pline)\n    fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n    sc = ax.scatter(points[0], points[1], points[2], c=filter_responses)\n\n    cbar = fig.colorbar(sc)\n    cbar.set_label(r\"$\\alpha$\")\n    \n    #plt.show()\n\n    \ndef main():\n    plines = read_pline(sys.argv[1])\n    \"\"\"\n    TODO mby plines visualisieren\n    \"\"\"\n\n    radiuses = []\n    sharp_corners_qs = []\n    sharp_corners_alphas = []\n    for pline in plines:\n        distances = []\n        d = 0\n        for j in range(len(pline.vertices)-1):\n            d = math.dist(pline.vertices[j].coordinates, pline.vertices[j+1].coordinates)\n            distances.append(d)\n        max_dist = max(distances)\n\n        radius = max_dist\n        radiuses.append(radius)\n\n        print(f\"Iterating pline {pline.label}\")\n        qs = []\n        alphas = []\n        for vertex in pline.vertices:\n            try:\n                last_right = last_in_circle(pline, pline.vertices.index(vertex), radius)\n                beta_right = calc_beta(vertex, pline.vertices[last_right], pline.vertices[last_right+1])\n                response_right = filter_response(vertex, pline.vertices[last_right], beta_right, radius)\n\n                last_left = last_in_circle(pline, pline.vertices.index(vertex), radius, direction=\"left\")\n                beta_left = calc_beta(vertex, pline.vertices[last_left], pline.vertices[last_left-1])\n                response_left = filter_response(vertex, pline.vertices[last_left], beta_left, radius)\n            except TypeError:\n                print(f\"{TypeError} in vertex {vertex.id}\")\n                qs.append(None)\n                alphas.append(None)\n                continue\n            except IndexError:\n                beta_right = calc_beta(vertex, pline.vertices[last_right], pline.vertices[0])\n                response_right = filter_response(vertex, pline.vertices[last_right], beta_right, radius)\n\n                q = run_length(response_right, response_left, radius)\n                alpha = calc_alpha(response_right, beta_right, radius)\n\n                qs.append(q)\n                alphas.append(alpha)\n                continue\n\n            q = run_length(response_right, response_left, radius)\n            alpha = calc_alpha(response_right, beta_right, radius)\n\n            qs.append(q)\n            alphas.append(alpha)\n\n        sharp_corners_qs.append(qs)\n        sharp_corners_alphas.append(alphas)\n\n    #os.mkdir(f\"Ergebnisse/plines/r{int(radius)}/\")\n    for i in range(len(plines)):\n        distances = [0.0]\n        d = 0\n        for j in range(len(plines[i].vertices)-1):\n            d += math.dist(plines[i].vertices[j].coordinates, plines[i].vertices[j+1].coordinates)\n            distances.append(d)\n   \n        fig, ax = plt.subplots(2, 1, constrained_layout=True)\n        # fig.tight_layout(pad=3.0)\n        ax[0].plot(distances, sharp_corners_qs[i],\n                   linestyle=\"solid\",\n                   color=\"#1c6294\",\n                   marker=\".\",\n                   markerfacecolor='#eda55c',\n                   markeredgecolor=\"#eda55c\")\n        ax[0].set_title(f\"Runlength integral invariant\")\n        ax[0].set(xlabel=\"Lauflänge in [mm]\", ylabel=r\"Runlength Integral Invariant $\\hat{Q}$\")\n        ax[1].plot(distances, sharp_corners_alphas[i],\n                   linestyle=\"solid\",\n                   color=\"#1c6294\",\n                   marker=\".\",\n                   markerfacecolor='#eda55c',\n                   markeredgecolor=\"#eda55c\")\n        ax[1].set_title(f\"Angle integral invariant\")\n        ax[1].set(xlabel=\"Lauflänge in [mm]\", ylabel=r\"Angle Integral Invariant $\\alpha_K$\")\n        fig.suptitle(f\"Polyline {pline.label}, $r=${radiuses[i]}\")\n\n        visualize_pline(plines[i], sharp_corners_alphas[i])\n\n        #plt.savefig(f\"Ergebnisse/plines/r{int(radius)}/pline{i}.png\")\n        plt.show()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"skranz0/mustererkennung","sub_path":"scd.py","file_name":"scd.py","file_ext":"py","file_size_in_byte":8840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73400185528","text":"from project_lib.models.discriminative_models import *\nfrom project_lib.models.pipe import *\nfrom project_lib.graphics import compute_std_minCDF_plot_for_model\nfrom project_lib.preprocessing import *\n\n\ndef evaluation(DTR, LTR, DTE, LTE):\n\n    print(\"Testing discriminative models...\")\n    l_val = np.logspace(-5, 5, num=51)\n    print(\"================== Start LR Testing ===================\")\n    for x in l_val:\n        for priors in [[0.5, 0.5], [0.9, 0.1], [0.1, 0.9]]:\n            print(\"========================================================\")\n            print(\"Testing \" + str(LR([], None)).replace(\"_\", \"\") +\n                  \"(πT=\" + str(priors[0]) + \"; λ=\" + str(x) + \") with ZScore features...\")\n            pipeLR = Pipe(DTR, LTR, DTE, LTE, [Zscore()], LR(priors, x))\n            pipeLR.make_evaluation()\n        print(\"========================================================\")\n    print(\"=================== End LR Testing ====================\")\n\n    print(\"================== Start QLR Testing ==================\")\n    for x in l_val:\n        for priors in [[0.5, 0.5], [0.9, 0.1], [0.1, 0.9]]:\n            print(\"Testing \" + str(QLR([], None)).replace(\"_\", \"\") +\n                  \"(πT=\" + str(priors[0]) + \"; λ=\" + str(x) + \") with RAW features...\")\n            pipeQLR = Pipe(DTR, LTR, DTE, LTE, [], QLR(priors, x))\n            pipeQLR.make_evaluation()\n    print(\"================== End QLR Testing ====================\")\n\n    print(\"End Testing discriminative models...\")\n\n    compute_std_minCDF_plot_for_model(Model.LR, [\"ZScore\"], {\"prior_t\": 0.9}, is_train=False)\n\n\n","repo_name":"victor3099/Gender-Identification","sub_path":"testing/test_discriminative.py","file_name":"test_discriminative.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34447017554","text":"_author__ = 'zachary'\nimport unittest\nimport os\nfrom bson.objectid import ObjectId\n\nfrom matchminer.database import get_db\nfrom matchminer.miner import _count_matches_by_filter, _count_matches\n\nTEST_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../', 'data'))\n\nfrom tests.test_matchminer import TestMinimal\n\nclass TestStatus(TestMinimal):\n\n    match = {\n        \"_id\": ObjectId(),\n        \"MATCH_STATUS\": 0,\n        \"VARIANT_CATEGORY\": \"MUTATION\",\n        \"CLINICAL_ID\": ObjectId(),\n        \"TRUE_HUGO_SYMBOL\": \"BRAF\",\n        \"TEAM_ID\": ObjectId(),\n        \"FILTER_STATUS\": 1,\n        \"FILTER_NAME\": \"MEK Inhibitor\",\n        \"VARIANTS\": [\n            ObjectId()\n        ],\n        \"ONCOTREE_PRIMARY_DIAGNOSIS_NAME\": \"Diffuse Glioma\",\n        \"USER_ID\": ObjectId(),\n        \"MMID\": \"D47CE1\",\n        \"FILTER_ID\": ObjectId(),\n        \"PATIENT_MRN\": \"XXXXXX\",\n        \"EMAIL_SUBJECT\": \"\",\n        \"EMAIL_ADDRESS\": \"test@test.test\",\n        \"EMAIL_BODY\": \"\",\n    }\n\n    status = {\n        \"_id\": ObjectId(),\n        \"updated_genomic\": 0,\n        \"new_genomic\": 0,\n        \"silent\": False,\n        \"new_clinical\": 0,\n        \"updated_clinical\": 0,\n    }\n\n    def setUp(self):\n        super(TestStatus, self).setUp(settings_file=None, url_converters=None)\n        self.db = get_db()\n        self.db['status'].drop()\n        self.db['match'].drop()\n        self.db['match'].insert_one(self.match)\n\n    def tearDown(self):\n        self.db['match'].drop()\n\n    def test_count_matches(self):\n\n        user = {'_id': ObjectId(), 'teams': [self.match['TEAM_ID']]}\n\n        # one new matches\n        counts = _count_matches(list(self.db['match'].find({'_id': ObjectId(self.match['_id'])})), self.db['match'])\n        assert counts['new_matches'] == 1\n        assert self._new_match() is True\n\n        # should be updated\n        counts = _count_matches(list(self.db['match'].find({'_id': ObjectId(self.match['_id'])})), self.db['match'])\n        assert counts['new_matches'] == 0\n        assert self._new_match() is True\n\n        # check ther other condition that will evaluate to a new match\n        self.db['match'].update({'_id': self.match['_id']}, {'$set': {'_new_match': False}})\n\n        counts = _count_matches(list(self.db['match'].find({'_id': ObjectId(self.match['_id'])})), self.db['match'])\n        assert counts['new_matches'] == 1\n        assert self._new_match() is True\n\n        counts = _count_matches(list(self.db['match'].find({'_id': ObjectId(self.match['_id'])})), self.db['match'])\n        assert counts['new_matches'] == 0\n        assert self._new_match() is True\n\n    def test_count_matches_by_filter(self):\n\n        # add two filters and matches in each bin for both filters\n        team_id = ObjectId()\n        filter1 = ObjectId()\n        filter2 = ObjectId()\n        self.db.filter.insert_many([\n            {'_id': filter1, 'TEAM_ID': team_id, 'status': 1, 'temporary': False},\n            {'_id': filter2, 'TEAM_ID': team_id, 'status': 1, 'temporary': False}\n        ])\n        matches = [{'MATCH_STATUS': i, 'FILTER_STATUS': 1, 'TEAM_ID': team_id, 'FILTER_ID': filter1}\n                   for i in [0, 1, 2, 3, 4, 5, 6, 7]]\n        matches += [{'MATCH_STATUS': i, 'FILTER_STATUS': 1, 'TEAM_ID': team_id, 'FILTER_ID': filter1}\n                    for i in [0, 1, 2, 3, 4, 5, 6, 7]]\n        matches += [{'MATCH_STATUS': i, 'FILTER_STATUS': 1, 'TEAM_ID': team_id, 'FILTER_ID': filter2}\n                    for i in [0, 1, 2, 3, 4, 5, 6, 7]]\n        self.db.match.insert_many(matches)\n\n        # count number of matches in each bin for both filters\n        counts = _count_matches_by_filter(\n            list(self.db['match'].find({'TEAM_ID': team_id})),\n            list(self.db['filter'].find({'TEAM_ID': team_id, 'status': 1, 'temporary': False}))\n        )\n\n        # check the number of matches for each filter\n        assert sorted(counts.keys()) == sorted([str(filter1), str(filter2)]), '%s\\n%s\\b%s' % (\n            list(counts.keys()), filter1, filter)\n        assert counts[str(filter1)] == {\n            \"new\": 2,\n            \"pending\": 2,\n            \"flagged\": 2,\n            'not_eligible': 2,\n            'enrolled': 2,\n            'contacted': 2,\n            'eligible': 2,\n            'deferred': 2\n        }, counts\n        assert counts[str(filter2)] == {\n            \"new\": 1,\n            \"pending\": 1,\n            \"flagged\": 1,\n            'not_eligible': 1,\n            'enrolled': 1,\n            'contacted': 1,\n            'eligible': 1,\n            'deferred': 1\n        }, counts\n\n    def _new_match(self):\n        return self.db['match'].find_one({'_id': self.match['_id']})['_new_match']\n","repo_name":"dfci/matchminer-api","sub_path":"tests/unit/test_api/test_status.py","file_name":"test_status.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"32792336793","text":"from PyQt6.QtWidgets import QDialog\nfrom PyQt6.uic import loadUi\n\nfrom core.lib.handlers.crypt.transposition.simple_transposition import SimpleTransposition\n\n\nclass TransposePage(QDialog):\n    def __init__(self, widget) -> None:\n        super(TransposePage, self).__init__()\n        loadUi(\"core/lib/uis/transposition/Transposition.ui\", self)\n        self.backButton.clicked.connect(self._go_back)\n\n        self.encodeButton.clicked.connect(self._encode)\n        self.decodeButton.clicked.connect(self._decode)\n\n        self.widget = widget\n\n    def _go_back(self) -> None:\n        self.widget.setCurrentIndex(0)\n\n    def _encode(self) -> None:\n        simp_trans = SimpleTransposition()\n        simp_trans.trans_type = 'custom'\n        simp_trans.cols_count = self.colsEdit.toPlainText()\n        self.resultEdit.setText(simp_trans.encrypt(self.textEdit.toPlainText()))\n\n    def _decode(self) -> None:\n        simp_trans = SimpleTransposition()\n        simp_trans.trans_type = 'custom'\n        simp_trans.cols_count = self.colsEdit.toPlainText()\n        self.resultEdit.setText(simp_trans.decrypt(self.textEdit.toPlainText()))\n    ","repo_name":"exorevan/Smart-Computer","sub_path":"core/lib/pages/transposition_page.py","file_name":"transposition_page.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44361897565","text":"class Persona:\n    def __init__(self, param_nombre, param_email):\n        self.nombre = param_nombre\n        self.email = param_email\n        self.asistencia = 0\n\n    def cambiar_nombre(self, nuevo_nombre):\n        self.nombre = nuevo_nombre\n\n    def asistencia_clase(self):\n        self.asistencia += 1\n\nclass Materia:\n    def __init__(self, param_nombre, param_aula, param_horario):\n        self.nombre = param_nombre\n        self.aula = param_aula\n        self.horario = param_horario\n\nclass Profesor(Persona):\n    def __init__(self, param_nombre, param_email, legajo_empleado):\n        self.legajo_empleado = legajo_empleado\n        super().__init__(param_nombre, param_email)\n\nclass Alumno(Persona):\n    def __init__(self, param_nombre, param_email, numero_alumno):\n        self.numero_alumno = numero_alumno\n        self.materias_cursando = []\n        super().__init__(param_nombre, param_email)\n\n    def cursando(self, Materia):\n        self.materias_cursando.append(Materia.nombre)\n\nclass Materia:\n    def __init__(self, param_nombre, param_aula, param_horario):\n        self.nombre = param_nombre\n        self.aula = param_aula\n        self.horario = param_horario\n\n\n\nmate = Materia(\"Matematica\", \"7\", \"3 pm\")\n\nalumna_maria = Alumno(\"Maria\", \"m.perez@gmail.com\", 665544)\nprint(id(alumna_maria))\nprint(\"El nombre es: \", alumna_maria.nombre)\nprint(\"El email es: \", alumna_maria.email)\nprint(\"El legajo es: \", alumna_maria.numero_alumno)\nalumna_maria.asistencia_clase()\nalumna_maria.cursando(mate)\nprint(\"La alumna fue a clase, su asistencia es: \", alumna_maria.asistencia)\nprint(\"La alumna cursa: \", alumna_maria.materias_cursando)\nprint(id(mate))\n\nprofesor_pepe = Profesor(\"Pepe\", \"pepe@gmail.com\", 225588)\nprint(id(profesor_pepe))\nprint(\"El nombre es: \", profesor_pepe.nombre)\nprint(\"El email es: \", profesor_pepe.email)\nprint(\"El legajo es: \", profesor_pepe.legajo_empleado)\nprint(\"Su asistencia es: \", profesor_pepe.asistencia)\nprofesor_pepe.asistencia_clase()\nprint(\"El profesor fue a clase, su asistencia es: \", profesor_pepe.asistencia)\n","repo_name":"um-computacion-tm/estudiantes-MaguiMaluff","sub_path":"estudiantes.py","file_name":"estudiantes.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15380972805","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jun  6 19:39:19 2018\r\n\r\n@author: jiangsh16\r\n\r\nfunction: universe input card\r\n\"\"\"\r\n\r\nimport rm\r\nimport mr\r\nimport cell\r\n\r\nclass universe:\r\n    def __init__(self):\r\n        self.name = ''\r\n        self.id = 0\r\n        self.move = [0, 0, 0]\r\n        self.rotate = [0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n        self.lat = 0\r\n        self.pitch = [0, 0, 0]\r\n        self.scope = [0, 0, 0]\r\n        self.sita = 60\r\n        self.fill = []\r\n        self.surf_bool = [1, 2, 3, 4, 5, 6]\r\n        self.cells = []\r\n\r\n    def readRMC(self, s_block):\r\n        l_line = s_block.split('\\n')\r\n        if len(l_line) == 1:\r\n            l_line = l_line[0].split(' ')\r\n            l_line = rm.format_line(l_line)\r\n            l_line.pop(0)\r\n            self.id = l_line.pop(0)\r\n            i = l_line.count('MOVE')\r\n            if i != 0:\r\n                j = l_line.index('MOVE')\r\n                l_move = l_line[j + 1:j + 4]\r\n                self.move = [float(s) for s in l_move]\r\n                del l_line[j:j + 4]\r\n\r\n            i = l_line.count('ROTATE')\r\n            if i != 0:\r\n                j = l_line.index('ROTATE')\r\n                l_rotate = l_line[j + 1:j + 10]\r\n                self.rotate = [float(s) for s in l_rotate]\r\n                del l_line[j:j + 10]\r\n            i = l_line.count('FILL')\r\n            if i != 0:\r\n                j = l_line.index('FILL')\r\n                l_fill = l_line[j + 1:]\r\n                self.fill = [int(s) for s in l_fill]\r\n                del l_line[j:]\r\n            i = l_line.count('LAT')\r\n            if i != 0:\r\n                j = l_line.index('LAT')\r\n                self.lat = l_line[j + 1]\r\n                j = l_line.index('PITCH')\r\n                l_pitch = l_line[j + 1:j + 4]\r\n                self.pitch = [float(s) for s in l_pitch]\r\n                if self.lat == '1':\r\n                    j = l_line.index('SCOPE')\r\n                    l_scope = l_line[j + 1:j + 4]\r\n                    self.scope = [int(s) for s in l_scope]\r\n                elif self.lat == '2':\r\n                    j = l_line.index('SCOPE')\r\n                    self.scope = l_line[j + 1:j + 3]\r\n                    l_scope = l_line[j + 1:j + 4]\r\n                    self.scope = [int(s) for s in l_scope]\r\n                    j = l_line.index('SITA')\r\n                    self.sita = float(l_line[j + 1])\r\n        else:\r\n            l_line0 = l_line[0].split(' ')\r\n            l_line0.pop()\r\n            self.id = l_line0.pop(0)\r\n            i = l_line.count('MOVE')\r\n            if i != 0:\r\n                j = l_line.index('MOVE')\r\n                l_move = l_line[j + 1:j + 4]\r\n                self.move = [float(s) for s in l_move]\r\n                del l_line[j:j + 4]\r\n            for i in range(1, len(l_line)):\r\n                c_cell =cell. cell()\r\n                c_cell.readRMC(l_line[i])\r\n                self.cells.append(c_cell)\r\n\r\n\r\n    def writeRMC(self):\r\n        s = 'UNIVERSE ' + str(self.id)\r\n        if self.move != [0, 0, 0] and self.move != []:\r\n            l_move = [str(i) for i in self.move]\r\n            s = s + ' MOVE=' + ' '.join(l_move)\r\n        if self.cells == []:\r\n            s = s + ' LAT=' + str(self.lat)\r\n            l_pitch = [str(i) for i in self.pitch]\r\n            s = s + ' PITCH=' + ' '.join(l_pitch)\r\n            l_scope = [str(i) for i in self.scope]\r\n            s = s + ' SCOPE=' + ' '.join(l_scope)\r\n            s = s + ' FILL =\\n '\r\n            j_row = self.scope[0]\r\n            for i in range(0, j_row):\r\n                if i == 0:\r\n                    s = s + ' ' + ' '.join(self.fill[i * j_row:i * j_row + j_row]) + '\\n'\r\n                else:\r\n                    s = s + '  ' + ' '.join(self.fill[i * j_row:i * j_row + j_row]) + '\\n'\r\n\r\n        else:\r\n            s = s + '\\n'\r\n            for c_cell in self.cells:\r\n                s = s + c_cell.writeRMC()\r\n        return s\r\n\r\n\r\n    def readMCNP(self,u,l_line,l_blocklist):\r\n        self.id = u\r\n        i = l_line.count('LAT')     #默认不含LAT的UNIVERSE都是包含CELL子单元的\r\n        if i == 0:\r\n            c_cell = cell.cell()\r\n            c_cell.readMCNP(u,l_line)\r\n            self.cells.append(c_cell)\r\n        else:\r\n            l_line = rm.format_line(l_line)\r\n            j = l_line.index('LAT')\r\n            self.lat = l_line[j+1]\r\n            if l_line.count('IMP:N') != 0:\r\n                j = l_line.index('IMP:N')\r\n                self.void = str(1 - int(l_line[j+1]))\r\n            if l_line[1] == '0':\r\n                i = 2\r\n            else:\r\n                i = 3\r\n            j = i\r\n            while l_line[j] != 'U':\r\n                 j = j + 1\r\n            self.surf_bool = l_line[i:j]\r\n            j = l_line.index('FILL')\r\n            self.scope[0] = mr.getscope(l_line[j+1:j+4])\r\n            self.scope[1] = mr.getscope(l_line[j+4:j+7])\r\n            self.scope[2] = mr.getscope(l_line[j+7:j+10])\r\n            self.fill = l_line[j+10:]\r\n            self.pitch = mr.getpitch(self.surf_bool,l_blocklist)\r\n\r\n        self.move = mr.getmove(u,l_blocklist,self)\r\n\r\n\r\n    def readMCNP_con(self,u,l_line):\r\n        c_cell = cell.cell()\r\n        c_cell.readMCNP(u,l_line)\r\n        self.cells.append(c_cell)\r\n\r\n    def writeMCNP(self, l_key, l_blocklist):\r\n        s = ''\r\n        if l_key[1] == 0:   #如果是universe0\r\n            for c in self.cells:\r\n                s = s + c.writeMCNP1(l_key[1], l_blocklist)\r\n\r\n        else:\r\n            if self.cells == []:    #如果是填充阵列\r\n                s = s + str(self.id) + ' 1 -1.0 '\r\n                l_surf_bool = rm.find_surface(self.id,self.pitch,l_blocklist)\r\n                s = s + ' '.join(l_surf_bool)\r\n                s = s + ' u=' + str(self.id)\r\n                s = s + ' lat=' + str(self.lat)\r\n                s = s + ' imp:n=1'\r\n                s = s + ' fill='\r\n                x1 = -(self.scope[0] - 1) / 2\r\n                x2 = (self.scope[0] - 1) / 2\r\n                y1 = -(self.scope[1] - 1) / 2\r\n                y2 = (self.scope[1] - 1) / 2\r\n                z1 = -(self.scope[2] - 1) / 2\r\n                z2 = (self.scope[2] - 1) / 2\r\n                s = s + str(x1) + ':' + str(x2) + ' '\r\n                s = s + str(y1) + ':' + str(y2) + ' '\r\n                s = s + str(z1) + ':' + str(z2) + '&\\n'\r\n                j_row = self.scope[0]\r\n                l_fill = [str(i) for i in self.fill]\r\n                for i in range(0, j_row):\r\n                    if i < j_row - 1:\r\n                        s = s + '  ' + ' '.join(l_fill[i * j_row:i * j_row + j_row]) + ' &\\n'\r\n                    else:\r\n                        s = s + '  ' + ' '.join(l_fill[i * j_row:i * j_row + j_row]) + '\\n'\r\n\r\n            else:\r\n                for c in self.cells:\r\n                    s = s + c.writeMCNP(l_key[1], l_blocklist)\r\n        return s\r\n","repo_name":"jiangshihang/rmc_mcnp","sub_path":"universe.py","file_name":"universe.py","file_ext":"py","file_size_in_byte":6803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12918288502","text":"import os\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.rst')).read()\n\nrequires = [\n    'pyramid',\n    'pyramid_zodbconn',\n    'pyramid_tm',\n    'ZODB3',\n    'repoze.folder',\n    'repoze.retry',\n    'repoze.tm2',\n    'repoze.zodbconn',\n    'repoze.whoplugins.zodb',\n    'deform',\n    'nose',\n    ]\n\nsetup(name='votingmachine',\n      version='2.0.7',\n      description='votingmachine',\n      long_description=README + '\\n\\n' +  CHANGES,\n      classifiers=[\n        \"Programming Language :: Python\",\n        \"Framework :: Pylons\",\n        \"Topic :: Internet :: WWW/HTTP\",\n        \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n        ],\n      author='',\n      author_email='',\n      url='',\n      keywords='web pylons pyramid',\n      packages=find_packages(),\n      include_package_data=True,\n      zip_safe=False,\n      install_requires = requires,\n      tests_require= requires,\n      test_suite=\"votingmachine\",\n      entry_points = \"\"\"\\\n      [paste.app_factory]\n      main = votingmachine:main\n      \"\"\",\n      paster_plugins=['pyramid'],\n      )\n","repo_name":"sixfeetup/votingmachine","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40365348425","text":"import gspread\n\nfrom pantapalabras.config import settings\nfrom pantapalabras.controller.spreadsheet_controller import SpreadsheetController, service_account_credentials\n\n\ndef test_add_vocabulary_pair():\n    # Act\n    SpreadsheetController().add_vocabulary_pair(\"hello\", \"world\")\n\n    # Assert\n    client = gspread.authorize(service_account_credentials)\n    sheet = client.open(settings.SPREADSHEET).sheet1\n    all_records = sheet.get_all_records()\n\n    assert all_records[-1] == {\"lang_a\": \"hello\", \"lang_b\": \"world\"}\n\n    # Tear Down\n    sheet.delete_row(len(all_records) + 1)\n\n\ndef test_get_last_row_index():\n    last_row_index = SpreadsheetController().get_last_row_index()\n\n    assert isinstance(last_row_index, int)\n\n\ndef test_get_whole_spreadsheet():\n    spreadsheet = SpreadsheetController().get_whole_spreadsheet()\n\n    assert isinstance(spreadsheet, list)\n    assert list(spreadsheet[0].keys()) == [\"lang_a\", \"lang_b\"]\n","repo_name":"plysytsya/pantapalabras","sub_path":"tests/test_controller/test_spreadsheet_controller.py","file_name":"test_spreadsheet_controller.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"46619862156","text":"'''\n执行结果：\n通过\n显示详情\n执行用时：24 ms, 在所有 Python 提交中击败了98.01% 的用户\n内存消耗：13.5 MB, 在所有 Python 提交中击败了75.34% 的用户\n'''\n\n\nclass Solution(object):\n    def reconstructQueue(self, people):\n        \"\"\"\n        :type people: List[List[int]]\n        :rtype: List[List[int]]\n        \"\"\"\n        people_dict = {}\n        for p in people:\n            h, k = p[0], p[1]\n            # set default value if not exist else do nothing\n            people_dict.setdefault(h, [])\n            people_dict[h].append(k)\n\n        result = []\n\n        for h in sorted(people_dict.keys(), reverse=True):\n            people_dict[h].sort()\n            for k in people_dict[h]:\n                result.insert(k, [h, k])\n\n        return result","repo_name":"dongbo910220/leetcode_","sub_path":"刷题leetcode/406. 根据身高重建队列.py","file_name":"406. 根据身高重建队列.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4575024848","text":"#!/usr/bin/env python\n\nimport argparse\nimport os\nimport pathlib\nimport re\n\ndef main():\n    parser = argparse.ArgumentParser(allow_abbrev=False, description=\"Convert file into includable C header\")\n    parser.add_argument(\"--in\", \"-i\", type=pathlib.Path, metavar=\"INPUT\", dest=\"input\", required=True, help=\"Input file\")\n    parser.add_argument(\"--out\", \"-o\", type=pathlib.Path, metavar=\"OUTPUT\", dest=\"output\", required=True, help=\"Output header\")\n    parser.add_argument(\"--columns\", type=int, default=12, help=\"Column count\")\n    args = parser.parse_args()\n\n    t = pathlib.Path()\n    varname, _ = re.subn(\"[^a-zA-Z0-9]\", \"_\", str(args.input.name))\n\n    binary_data = args.input.open(\"rb\").read()\n\n    with args.output.open(\"w\", newline=\"\\n\") as fout:\n        fout.write(\"unsigned char {}[] = {{\\n\".format(varname))\n        bytes_written = 0\n        while bytes_written < len(binary_data):\n            col = bytes_written % args.columns\n            if col == 0:\n                fout.write(\"  \")\n            column_data = binary_data[bytes_written:bytes_written+args.columns]\n            fout.write(\", \".join(\"0x{:02x}\".format(d) for d in column_data))\n            bytes_written += len(column_data)\n            if bytes_written < len(binary_data):\n                fout.write(\",\\n\")\n            else:\n                fout.write(\"\\n\")\n        fout.write(\"}};\\nunsigned int {}_len = {:d};\\n\".format(varname, len(binary_data)))\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n","repo_name":"libsdl-org/SDL","sub_path":"cmake/xxd.py","file_name":"xxd.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":7118,"dataset":"github-code","pt":"77"}
+{"seq_id":"21839391121","text":"T = int(input())\nfor tc in range(1, T + 1):\n    N, M, L = map(int, input().split())\n    tree = [0 for _ in range(N + 1)]\n    for _ in range(M):\n        leaf, value = map(int, input().split())\n        tree[leaf] = value\n\n    # 노드 갯수가 0이라면 오른쪽 자식노드가 없으므로 0을 넣는다\n    if not (N % 2):\n        tree.append(0)\n\n    # 노드 갯수가 4개 혹은 5개라면 [4+5, 2+3] 두 번의 덧셈이 일어난다\n    # 6개 혹은 7개일 때: [6+7, 4+5, 2+3] 세 번의 덧셈이 일어난다\n    # 따라서 왼쪽 자식 노드의 번호를 기준으로 인덱스를 잡아주었다.\n    for i in range((N // 2) * 2, 1, -2):\n        tree[i // 2] = tree[i] + tree[i + 1]\n\n    print(f'#{tc} {tree[L]}')","repo_name":"niinp28/TIL","sub_path":"PS/SWEA/Tree/sum_of_node.py","file_name":"sum_of_node.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23145493766","text":"\"\"\"portal URL Configuration\"\"\"\n\nfrom django.conf.urls import url\nfrom . import views\n\napp_name = 'portal'\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^register/$', views.register, name='register'),\n    url(r'^login_user/$', views.login_user, name='login_user'),\n    url(r'^logout_user/$', views.logout_user, name='logout_user'),\n    url(r'^(?P<lit_type_id>[0-9]+)/$', views.detail, name='detail'),\n    url(r'^(?P<lit_type_id>[0-9]+)/contribute/$', views.contribute, name='contribute'),\n    url(r'^contribute/$', views.contribute, name='contribute'),\n    url(r'^contribute_b/$', views.contribute_b, name='contribute_b'),\n    url(r'^literature/(?P<filter_by>[a-zA_Z]+)/$', views.literatures, name='literature'),\n    url(r'^terms/$', views.termsandcondition, name='terms'),\n]","repo_name":"jsalarza/atrax","sub_path":"portal/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6400090435","text":"import numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.cluster import MiniBatchKMeans\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\nfrom globals import *\n\n# Define Global vectorized data\ndf = allStatements\ndf.rename( columns={0 :'text'}, inplace=True )\ndf['label'] = df.index\nvec = TfidfVectorizer(stop_words=\"english\")\nvec.fit(df.text.values)\nfeatures = vec.transform(df.text.values)\nsorted = allStatements[['text']]\n\ndef clusterData(df, features, minClusters, maxClusters, returnData, rateMax):\n    \"\"\"\n    @param df is the list of statements of type df\n    @param maxClusters is the max number of clusters (categories) the data is to be sorted into.\n    @param minClusters is the min number of clusters (categories) the data is to be sorted into.\n    @param features is the vectorized text to cluster of type df.\n    @param returnData is the df to write resulting data to.\n    @param rateMax is the maximum rating a statement can get. This function assumes\n        there are only two variables to rank on.\n    \"\"\"\n    for p in range(1, participants +1):\n        start = df\n        # Change cluster number/state for each demographic group\n        # if p == 1 or p%participants == 0:\n        n_clusters = random.randint(minClusters,maxClusters)\n        random_state = random.randint(0,10)\n\n        cls = MiniBatchKMeans(n_clusters=n_clusters, random_state=random_state)\n        cls.fit(features)\n        cls.predict(features)\n\n        start['participant' + str(p)] = pd.Series(cls.labels_, index=start.index)\n        start = start[start.columns.drop(list(start.filter(regex='label')))]\n        returnData = start.merge(returnData, left_index = True, on = 'text')\n\n        rateMin = 1\n        if p%2 == 0:\n            rateMin == 2\n        elif p%3 == 0:\n            rateMin == 3\n\n        rating1 = np.random.uniform(rateMin,rateMax, size = len(returnData))\n        rating1 = [round(num) for num in rating1]\n        rating1 = pd.DataFrame(rating1, columns=['rating1/'+ str(p)])\n        returnData = returnData.merge(rating1, left_index = True, right_index = True)\n\n        rating2 = np.random.uniform(rateMin,rateMax, size = len(returnData))\n        rating2 = [round(num) for num in rating2]\n        rating2 = pd.DataFrame(rating2, columns=['rating2/'+ str(p)])\n        returnData = returnData.merge(rating2, left_index = True, right_index = True)\n    return returnData\n\n\ndef buildAll(df, features, n, sorted, rateMax):\n    \"\"\"\n    From KMeans clusters, build the sorted and rated data set.\n    \"\"\"\n    x = clusterData(df, features, minClusters, maxClusters, sorted, rateMax)\n    x.columns  = [i.strip('_x').strip('_y') for i in x.columns]\n    x = x.loc[:,~x.sort_index(1).columns.duplicated()]\n    x['ID'] = x.index\n    data = x\n    x = x.rename(columns={'text': 'Statement',})\n    x['StatementID'] = x.index + 1\n    x['Statement'] = x['Statement'].str.strip()\n    statementsWrite = x[['StatementID', 'Statement']]\n\n    x[\"UserID\"] = x['ID'] + 1\n    x = pd.wide_to_long(x, [\"participant\", \"rating2/\", \"rating1/\"], i=\"ID\", j=\"num\")\n    x = x.drop_duplicates()\n    x = x.sort_values(by=['num', 'ID'])\n    x = x.rename(columns={\"rating1/\": rating1, \"rating2/\": rating2})\n    x[\"StatementID\"] = x.groupby('num').ngroup(ascending=True)\n    x[\"StatementID\"] += 1\n    col_names = [\"UserID\", \"StatementID\", rating1, rating2]\n    ratingsWrite = x.reindex(columns=col_names)\n\n    y = data\n    y[\"UserID\"] = y['ID']\n    del y['ID']\n    keep = [col for col in y.columns if 'participant' in col]\n    keep = keep + ['UserID']\n    y = y[keep]\n    y = pd.wide_to_long(y, [\"participant\"], i=\"UserID\", j=\"num\")\n    y = y.rename(columns={\"participant\": \"cluster\"})\n    y[\"UserID1\"] = y.groupby('num').ngroup(ascending=True)\n    y[\"StatementID1\"] = y.groupby('UserID').ngroup(ascending=True)\n    y = y.sort_values(by=[ \"UserID1\", \"cluster\"])\n    y[\"StatementID1\"] += 1\n    y = y.reset_index(drop=True)\n    y = y.rename(columns={\"StatementID1\": \"StatementID\", \"UserID1\" : \"UserID\"})\n    y = y.drop_duplicates()\n    # y = y.replace(0, np.nan)\n    nStatements = y.groupby(['UserID', 'cluster']).count().max() + 1\n    nStatements = nStatements['StatementID']\n    accum = ['UserID', 'Cluster']\n    for st in range(nStatements):\n        accum.append('Statement' + str(st))\n\n    z = pd.DataFrame(columns = accum)\n    for i in range(len(y)):\n        end = False\n        clusterCurrent = y.loc[y.index[i], \"cluster\"]\n        userCurrent = y.loc[y.index[i], \"UserID\"]\n        try:\n            clusterNext = y.loc[y.index[i+1], \"cluster\"]\n            userNext = y.loc[y.index[i+1], \"UserID\"]\n        except:\n            end = True\n\n        if i == 0:\n            statements = [userCurrent, clusterCurrent]\n        if (clusterNext == clusterCurrent) and not end:\n            statement = y.loc[y.index[i], \"StatementID\"]\n            statements.append(statement)\n        else:\n            statement = y.loc[y.index[i], \"StatementID\"]\n            statements.append(statement)\n            diff = nStatements + 2 - len(statements)\n            for k in range(diff):\n                statements.append(np.nan)\n            series = pd.Series(statements, index = z.columns)\n            z = z.append(series, ignore_index=True)\n            statements = [userNext, clusterNext]\n\n    z = z.sort_values(by=[ \"UserID\", \"Cluster\"])\n    z[\"UserID\"] += 1\n    z[\"Cluster\"] += 1\n    z[\"Cluster\"] = z[\"Cluster\"].astype(str)\n    sortedCardsWrite = z.reset_index(drop=True)\n\n    accum = [\"Variable\", rating1, rating2]\n    r = pd.DataFrame(columns = accum)\n    series1 = pd.Series(['Min', 1, 1], index = r.columns)\n    series2 = pd.Series(['Max', rateMax, rateMax], index = r.columns)\n    r = r.append(series1, ignore_index=True)\n    r = r.append(series2, ignore_index=True)\n    ratingsScaleWrite = r\n\n    with pd.ExcelWriter('sortData.xlsx') as writer:\n        statementsWrite.to_excel(writer, sheet_name='Statements', index = False)\n        sortedCardsWrite.to_excel(writer, sheet_name='SortedCards', index = False, header = False)\n        demographics.to_excel(writer, sheet_name='Demographics', index = False, header = True)\n        ratingsWrite.to_excel(writer, sheet_name='Ratings', index = False)\n        ratingsScaleWrite.to_excel(writer, sheet_name='RatingsScale', index = False)\n\n\nbuildAll(df, features, maxClusters, sorted, rateMax)\n\n\n# Graph the results\n# pca = PCA(n_components=2, random_state=random_state)\n# reduced_features = pca.fit_transform(features.toarray())\n# reduced_cluster_centers = pca.transform(cls.cluster_centers_)\n# plt.scatter(reduced_features[:,0], reduced_features[:,1], c=cls.predict(features))\n# plt.scatter(reduced_cluster_centers[:, 0], reduced_cluster_centers[:,1], marker='x', s=150, c='b')\n# plt.show()\n","repo_name":"henrymanley/cMapKMeans","sub_path":"autoSort.py","file_name":"autoSort.py","file_ext":"py","file_size_in_byte":6747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20256743071","text":"lst = [1,2,3,'a',4,'b',5] #sample list\n\nvalue = input('''list = [1,2,3,a,4,b,5]\nEnter a value from the list : ''')\n\nfor i in range (len(lst)) :\n       \n    if str(lst[i]) == value :\n        \n        print('Index of selected value is ',i)\n","repo_name":"LidaRP/Simulate-the-Insert-function-Python","sub_path":"Simulate the Insert function.py","file_name":"Simulate the Insert function.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75211885367","text":"import math\nimport numpy as np\nimport time\n\nfrom xplane_autoland.xplane_connect.xpc3 import XPlaneConnect\n\n# Default values set for Rwy 04 of Grant Co. Airport\n# TODOs relate to generalizing for arbitrary airports\n\nclass XPlaneDriver:\n    def __init__(self, home_heading=53.7, local_start=(-35285.421875, 40957.0234375),\n                 start_ground_range=12464, start_elev = 1029.45,\n                 t=(-25159.26953, 33689.8125)):\n        self._client = XPlaneConnect()\n        self._home_heading = home_heading\n        self._local_start = local_start\n        # TODO: get from glideslope points\n        self._start_ground_range = start_ground_range # (m)\n        self._start_elev = start_elev # (m) beginning elevation for a 3 degree glideslope\n        self._t = np.array(t).reshape((2, 1))\n\n        # TODO: determine this automatically\n        #       relates to home heading and local coordinate frame at given airport\n        self._rotrad = -0.6224851011617226\n        self._R = np.array([[ np.cos(self._rotrad), -np.sin(self._rotrad) ],\n                            [ np.sin(self._rotrad),  np.cos(self._rotrad)]])\n\n    def reset(self,\n              init_u=60., init_v=0, init_w=0., init_p=0, init_q=0, init_r=0,\n              init_phi=0, init_theta=0, init_psi=0, init_x=12464, init_y=0, init_h=None,\n              noBrake=True):\n        \"\"\"\n            Resets the aircraft and resets forces, fuel, etc.\n\n            Args (all state variables):\n                init_u        - longitudinal velocity (m/s)\n                init_v        - lateral velocity (m/s)\n                init_w        - vertical velocity (m/s)\n                init_p        - roll rate (deg/s)\n                init_q        - pitch rate (deg/s)\n                init_r        - yaw rate (deg/s)\n                init_phi      - roll angle (deg)\n                init_theta    - pitch angle (deg)\n                init_psi      - initial heading (deg)\n                init_x        - horizontal distance (m)\n                init_y        - lateral deviation (m)\n                init_h        - aircraft altitude (m)\n            Note: if choose not to pass one, will be set to a nominal default value\n        \"\"\"\n\n        self._client.pauseSim(True)\n\n        if init_h is None:\n            init_h = self._start_elev\n\n        # Turn off joystick \"+\" mark from screen\n        self._client.sendDREF(\"sim/operation/override/override_joystick\", 1)\n\n        # Zero out control inputs\n        self._client.sendCTRL([0,0,0,0])\n\n        # Set parking brake\n        self._client.sendDREF(\"sim/flightmodel/controls/parkbrake\", int(noBrake))\n\n        # Zero out moments and forces\n        initRef = \"sim/flightmodel/position/\"\n        drefs = []\n        refs = ['theta','phi', 'local_vx','local_vy','local_vz','local_ax','local_ay','local_az',\n        'Prad','Qrad','Rrad','q','groundspeed',\n        'indicated_airspeed','indicated_airspeed2','true_airspeed','M','N','L','P','Q','R','P_dot',\n        'Q_dot','R_dot','Prad','Qrad','Rrad']\n        for ref in refs:\n            drefs += [initRef+ref]\n        values = [0]*len(refs)\n        self._client.sendDREFs(drefs,values)\n\n        # Set position and orientation\n        # Set known good start values\n        # Note: setting position with lat/lon gets you within 0.3m. Setting local_x, local_z is more accurate)\n        self.set_orient_pos(init_phi, init_theta, init_psi, init_x, init_y, init_h)\n        self.set_orientrate_vel(init_u, init_v, init_w, init_p, init_q, init_r)\n\n        # Fix the plane if you \"crashed\" or broke something\n        self._client.sendDREFs([\"sim/operation/fix_all_systems\"], [1])\n\n        # Set fuel mixture for engine\n        self._client.sendDREF(\"sim/flightmodel/engine/ENGN_mixt\", 0.61)\n\n        # Reset fuel levels\n        self._client.sendDREFs([\"sim/flightmodel/weight/m_fuel1\",\"sim/flightmodel/weight/m_fuel2\"],[232,232])\n\n        # Give time to settle\n        time.sleep(1.)\n\n    def pause(self, yes=True):\n        \"\"\"\n        Pause or unpause the simulation\n\n        Args:\n            yes: whether to pause or unpause the sim [default: True (i.e., pause)]\n        \"\"\"\n        self._client.pauseSim(yes)\n\n    def send_ctrl(self, elev, aileron, rudder, throttle):\n        \"\"\"\n        Sets control surface information (on the main aircraft)\n\n            Args:\n                elev: [-1, 1]\n                aileron: [-1, 1]\n                rudder: [-1, 1]\n                throttle: [-1, 1]\n        \"\"\"\n        self._client.sendCTRL([elev, aileron, rudder, throttle])\n\n    def send_brake(self, brake):\n        \"\"\"\n        Set the parking brake to on or off\n\n            Args:\n                brake: 0 - off; 1 - on\n        \"\"\"\n        self._client.sendDREF(\"sim/flightmodel/controls/parkbrake\", brake)\n\n    ###########################################################################\n    # State estimation\n    # by default these are passthroughs -- can be overloaded in a subclass\n    ###########################################################################\n    def est_statevec(self):\n        vel  = self.est_vel_state()\n        ovel = self.est_orient_vel_state()\n        o    = self.est_orient_state()\n        pos  = self.est_pos_state()\n\n        return np.stack((vel, ovel, o, pos)).flatten()\n\n    def est_vel_state(self):\n        return self.get_vel_state()\n\n    def est_orient_vel_state(self):\n        return self.get_orient_vel_state()\n\n    def est_orient_state(self):\n        return self.get_orient_state()\n\n    def est_pos_state(self):\n        return self.get_pos_state()\n\n    ###########################################################################\n    # True state getters\n    ###########################################################################\n    def get_statevec(self):\n        \"\"\"\n        Returns the state vector used in the autoland scenario\n        Based on https://arc.aiaa.org/doi/10.2514/6.2021-0998\n            u      - longitudinal velocity (m/s)\n            v      - lateral velocity (m/s)\n            w      - vertical velocity (m/s)\n            p      - roll velocity (deg/s)\n            q      - pitch velocity (deg/s)\n            r      - yaw velocity (deg/s)\n            phi    - roll angle (deg)\n            theta  - pitch angle (deg)\n            psi    - yaw angle (deg)\n            x      - horizontal distance (m)\n            y      - lateral deviation (m)\n            h      - aircraft altitude (m)\n        \"\"\"\n\n        vel  = self.get_vel_state()\n        ovel = self.get_orient_vel_state()\n        o    = self.get_orient_state()\n        pos  = self.get_pos_state()\n\n        return np.stack((vel, ovel, o, pos)).flatten()\n\n    def get_vel_state(self):\n        return self._body_frame_velocity()\n\n    def get_orient_vel_state(self):\n        P = self._client.getDREF('sim/flightmodel/position/P')[0]\n        Q = self._client.getDREF('sim/flightmodel/position/Q')[0]\n        R = self._client.getDREF('sim/flightmodel/position/R')[0]\n        return np.array([P, Q, R])\n\n    def get_orient_state(self):\n        phi = self._client.getDREF('sim/flightmodel/position/phi')[0]\n        theta = self._client.getDREF('sim/flightmodel/position/theta')[0]\n        psi = self._get_home_heading()\n        return np.array([phi, theta, psi])\n\n    def get_pos_state(self):\n        x, y = self._get_home_xy()\n        h = self._client.getDREF('sim/flightmodel/position/elevation')[0]\n        return np.array([x, y, h])\n\n    def set_orient_pos(self, phi, theta, psi, x, y, h):\n        '''\n        Set the orientation and position of the plane.\n        Args:\n            phi    - roll angle (deg)\n            theta  - pitch angle (deg)\n            psi    - yaw angle (deg)\n            x      - horizontal distance (m)\n            y      - lateral deviation (m)\n            h      - aircraft altitude (m)\n        '''\n        # zero out orientation at first\n        self._client.sendDREF('sim/flightmodel/position/phi', 0)\n        self._client.sendDREF('sim/flightmodel/position/theta', 0)\n        self._client.sendDREF('sim/flightmodel/position/psi', self._to_local_heading(0))\n\n        self._send_xy(x, y)\n        # set elevation by getting offset between local y (the axis for elevation)\n        # and current elevation\n        # then use that to shift the coordinate to align with the desired elevation\n        curr_elev = self._client.getDREF(\"sim/flightmodel/position/elevation\")[0]\n        curr_localy = self._client.getDREF(\"sim/flightmodel/position/local_y\")[0]\n        offset = curr_elev - curr_localy\n        self._client.sendDREF(\"sim/flightmodel/position/local_y\", h - offset)\n\n        self._client.sendDREF('sim/flightmodel/position/phi', phi)\n        self._client.sendDREF('sim/flightmodel/position/theta', theta)\n        self._client.sendDREF('sim/flightmodel/position/psi', self._to_local_heading(psi))\n\n    def set_orientrate_vel(self, u, v, w, p, q, r):\n        # 2d rotation and flip\n        uv = np.array([u, v]).reshape((2, 1))\n        hr = math.radians(self._home_heading)\n        R = np.array([[np.cos(hr), -np.sin(hr)],[np.sin(hr), np.cos(hr)]])\n        rot_uv = R@uv\n        # flip direction of longitudinal velocity to put in OpenGL coordinates\n        self._client.sendDREF('sim/flightmodel/position/local_vz', -rot_uv[0])\n        self._client.sendDREF('sim/flightmodel/position/local_vx', rot_uv[1])\n        self._client.sendDREF('sim/flightmodel/position/local_vy', w)\n        self._client.sendDREF('sim/flightmodel/position/P', p)\n        self._client.sendDREF('sim/flightmodel/position/Q', q)\n        self._client.sendDREF('sim/flightmodel/position/R', r)\n    \n    ###########################################################################\n    # Helper functions\n    ###########################################################################\n    def _body_frame_velocity(self):\n        cos = math.cos\n        sin = math.sin\n\n        psi = self._client.getDREF('sim/flightmodel/position/psi')[0]\n        theta = self._client.getDREF('sim/flightmodel/position/theta')[0]\n        phi = self._client.getDREF('sim/flightmodel/position/phi')[0]\n\n        h = math.radians(psi)\n        Rh = np.array([[ cos(h), sin(h), 0],\n                    [-sin(h), cos(h), 0],\n                    [      0,      0,  1]])\n        el = math.radians(theta)\n        Re = np.array([[cos(el), 0, -sin(el)],\n                    [      0, 1,        0],\n                    [sin(el),  0,  cos(el)]])\n        roll = math.radians(phi)\n        Rr = np.array([[1,          0,         0],\n                    [0,  cos(roll), sin(roll)],\n                    [0, -sin(roll), cos(roll)]])\n        R = np.matmul(Rr, np.matmul(Re, Rh))\n\n        vx = self._client.getDREF('sim/flightmodel/position/local_vx')[0]\n        vy = self._client.getDREF('sim/flightmodel/position/local_vy')[0]\n        vz = self._client.getDREF('sim/flightmodel/position/local_vz')[0]\n        # local frame is East-Up-South and we convert to North-East-Down\n        vel_vec = np.array([-vz, vx, -vy]).T\n\n        return np.matmul(R, vel_vec)\n\n    def _get_home_heading(self):\n        \"\"\"\n        Get the value of the aircraft's heading in degrees from the runway\n        \"\"\"\n        true_heading = self._client.getDREF(\"sim/flightmodel/position/psi\")[0]\n        return true_heading - self._home_heading\n\n    def _get_local_heading(self):\n        \"\"\"\n        Get the value of the aircraft's heading in degrees from the Z axis\n        \"\"\"\n        return self._client.getDREF(\"sim/flightmodel/position/psi\")[0]\n\n    def _get_home_xy(self):\n        \"\"\"\n        Get the aircraft's current x and y position and heading in the\n        home frame. The x-value represents crosstrack error,the y-value represents\n        downtrack position, and theta is the heading error.\n        \"\"\"\n\n        psi = self._client.getDREF(\"sim/flightmodel/position/psi\")[0]\n        x   = self._client.getDREF(\"sim/flightmodel/position/local_x\")[0]\n        y   = self._client.getDREF(\"sim/flightmodel/position/local_z\")[0]\n\n        # Get the positions in home coordinates\n        rotx, roty = self._local_to_home(x, y)\n\n        x = self._start_ground_range - roty\n        y = -rotx\n        return x, y\n\n    # TODO: replace with single-step coordinate transform\n    def rotateToLocal(self, x, y):\n        \"\"\"Rotate to the local coordinate frame.\n\n            Args:\n                x: x-value in home coordinate frame\n                y: y-value in home coordinate frame\n        \"\"\"\n        rotx = 0.583055934597441 * x + -0.8124320138514389 * y\n        roty = 0.8124320138514389 * x + 0.583055934597441 * y\n        return rotx, roty\n\n    # TODO: replace with single-step coordinate transformation\n    def _local_to_home(self, x, y):\n        \"\"\"Get the home coordinates of the aircraft from the local coordinates.\n\n            Args:\n                x: x-value in the local coordinate frame\n                y: y-value in the local coordinate frame\n        \"\"\"\n\n        # Translate to make start x and y the origin\n        startX, startY = self._local_start\n        transx = startX - x\n        transy = startY - y\n\n        # Rotate to align runway with y axis\n        rotx, roty = self.rotate_to_home(transx, transy)\n        return rotx, roty\n\n    def rotate_to_home(self, x, y):\n        \"\"\"\n            Rotate to the home coordinate frame.\n\n            Home coordinate frame starts at (0,0) at the start of the runway\n            and ends at (0, 2982 at the end of the runway). Thus, the x-value\n            in the home coordinate frame corresponds to crosstrack error and\n            the y-value corresponds to downtrack position.\n\n            Args:\n                x: x-value in local coordinate frame\n                y: y-value in local coordinate frame\n        \"\"\"\n        rotx = 0.583055934597441 * x + 0.8124320138514389 * y\n        roty = -0.8124320138514389 * x + 0.583055934597441 * y\n        return rotx, roty\n\n    def _to_local_heading(self, psi):\n        \"\"\"\n        Convert home heading to local frame heading\n        \"\"\"\n        return psi + self._home_heading\n\n    def _send_xy(self, x, y):\n        local_x, local_z = self._xy_to_local_xz(x, y)\n        self._client.sendDREF(\"sim/flightmodel/position/local_x\", local_x)\n        self._client.sendDREF(\"sim/flightmodel/position/local_z\", local_z)\n\n    def _xy_to_local_xz(self, x, y):\n        \"\"\"\n        Converts autoland statevec's x, y elements to local x, z coordinates.\n        Note: in local frame, y is elevation (up) so we care about x and **z** for this rotation\n        \"\"\"\n        # rotation to align to runway\n        # flip a sign because of x, y orientation\n        # in autoland frame, x is pointing frame the runway to the starting point\n        # and y is pointing to the right from the plane's point of view\n        F = np.array([[-1.,  0.],\n                    [ 0., 1.]])\n        r = (self._R@F)@np.array([[x], [y]]).reshape((2, 1))\n        local_x, local_z = r + self._t\n        return local_x.flatten(), local_z.flatten()\n\n    def _local_xz_to_xy(self, local_x, local_z):\n        R = self._R\n        F = np.array([[-1.,  0.],\n                    [ 0., 1.]])\n        RF = R@F\n        l = np.array([[local_x], [local_z]]).reshape((2, 1))\n        r = l - self._t\n        x, y = np.linalg.inv(RF)@r\n        return x, y\n\n","repo_name":"makaimann/XPlaneAutolandScenario","sub_path":"src/xplane_autoland/xplane_connect/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":15203,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"15543109917","text":"#1 Define a function named is_two. \n# It should accept one input and return \n# True if the passed input is either the number or the string 2, False otherwise.\ndef is_two(one_input):\n    if one_input in [2, '2'] :\n        return True\n    else: \n        return False\n\n#2\n#Define a function named is_vowel. \n# It should return True if the passed string is a vowel, False otherwise\ndef is_vowel(vowel):\n    if vowel.upper() in ['A', 'E', 'I', 'O', 'U']:\n        return True\n    else:\n        return False\n\n#3 Define a function named is_consonant.\n#  It should return True if the passed string is a consonant,\n#  False otherwise. Use your is_vowel function to accomplish this.\ndef is_consonant(consonant):\n    if consonant.upper() not in ['A', 'E', 'I', 'O', 'U']:\n        return True\n    else:\n        return False\n\n#4 Define a function that accepts a string that is a word. \n# The function should capitalize the first letter of the word if the word starts with a consonant.\ndef cap_if_const(word):\n   if word[0].upper() not in ['A', 'E', 'I', 'O', 'U']:\n       return word.title()\n   else:\n       return word\n\n#5 Define a function named calculate_tip. \n# It should accept a tip percentage\n#  (a number between 0 and 1) and the bill total, and return the amount to tip.\ndef calculate_tip(percent, total):\n    if percent >=0 and percent <=1:\n        return round((percent * total) + total, 2)\n\n#6 Define a function named apply_discount. \n# It should accept a original price, and a discount percentage,\n#  and return the price after the discount is applied.\ndef apply_discount(total, percentage_off):\n    if percentage_off >= 0 or percentage_off <= 1:\n         return round(total - (total * percentage_off), 2)\n\n\n# 7 Define a function named handle_commas.\n#  It should accept a string that is a number that \n# contains commas in it as input, and return a number as output.\ndef handle_commas(phrase):\n   return int(phrase.replace(',',''))\n\n#8 Define a function named get_letter_grade. \n# It should accept a number and return the letter grade \n# associated with that number (A-F).\ndef get_letter_grade(grade):\n    if grade <= 100 and grade >= 90:\n        return 'A'\n    elif grade < 90 and grade >= 80:\n        return 'B'\n    elif grade < 80 and grade >= 70:\n        return 'C'\n    elif grade <= 70 and grade >= 60:\n        return 'D'\n    else:\n        return 'F'\n\n#9 define a function named remove_vowels that \n# accepts a string and returns a string with all the vowels removed.\ndef remove_vowels(phrase):\n    vowels = ('a', 'e', 'i','o','u')\n    for char in phrase.lower():\n        if char in vowels:\n            phrase = phrase.replace(char, '')\n    return phrase\n\n#10 Define a function named normalize_name. It should accept a string and return a valid python identifier, that is:\n# anything that is not a valid python identifier should be removed\n# leading and trailing whitespace should be removed\n# everything should be lowercase\n# spaces should be replaced with underscores\n# for example:\n# Name will become name First Name will become first_name % Completed will become completed\ndef normalize_name(string_name):\n    for char in string_name:\n        if not char.isalpha:\n            string_name = string_name.replace(char,'')\n        if char == \" \":\n            string_name = string_name.replace(char, '_')\n    if string_name[0]  == '_':\n            string_name = string_name.replace(char, )\n    return(string_name.lower())\n\n\n\n#11 Write a function named cumulative_sum \n# that accepts a list of numbers and returns\n#  a list that is the cumulative sum of the numbers in the list.\ndef cumulative_sum(num_list):\n    newlist = []\n    temp = 0\n    for i in range(0, len(num_list)):\n        temp+=num_list[i]\n        newlist.append(temp)\n    return newlist\n\nx = cumulative_sum([10,20,30])\nprint(x)\n\n\n#bonus \n# #1 Create a function named twelveto24. \n# It should accept a string in the format 10:45am or 4:30pm \n# and return a string that is the representation of the time in a 24-hour format. \n# Bonus write a function that does the opposite.\ndef twelveto24(time):\n    if 'am' in time:\n        time = time.replace('am','')\n    elif 'pm' in time:\n        time = time.replace('pm','')\n        first_digits = time.split(':')\n        first_plus_12 = int(first_digits[0]) + 12\n        time = time.replace(str(first_digits[0]), str(first_plus_12))\n    return(time)\n\n        \n    \n      \nx = twelveto24('2:30pm')\nprint(x)\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\n\n\n\n","repo_name":"carterrxavier/python-exercises","sub_path":"function_exercises.py","file_name":"function_exercises.py","file_ext":"py","file_size_in_byte":4463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29381473289","text":"\n\ndef checkout(lista_productos:list, carrito:dict):\n\n    descuento = 0\n    subtotal = 0\n\n    productos = carrito.keys()\n    \n    if 1 in productos and 2 in productos and 3 in productos:\n        descuento = descuento + 0.3\n    \n    if 4 in productos and 5 in productos:\n        descuento = descuento + 0.15\n\n    for item in lista_productos:\n        if item[0] in productos:\n            precio = float(item[2])\n            subtotal_producto = carrito[item[0]] * precio\n\n            subtotal = subtotal + subtotal_producto\n            print(item[1] + ' $' + str(float(round(subtotal_producto, 1))))    \n\n    total = subtotal - (subtotal * descuento)\n\n    return round(total, 1)\n\np1=[1,\"Pokemon X\",33.77]\np2=[2,\"Nintendo XL\",203]\np3=[3,\"Mario Kart 7\",27.58]\np4=[4,\"PlayStation 4\",348.00]\np5=[5,\"FIFA 16\",51.19]\n\nproductos = [p1, p2, p3, p5, p5]\n\ncarrito = {}\nsubtotal = 0\n\nwhile True:\n\n    opcion = input(\"Ingrese su opcion (ver / checkout / producto): \")\n\n    if opcion.lower() == 'ver':\n        for item in productos:\n            print(item)\n        \n    elif opcion.lower() == 'checkout':\n        break\n\n    # opcion de compra\n    else:\n        prod = int(opcion.split(',')[0])\n        cantidad = int(opcion.split(',')[1])\n\n        carrito[prod] = cantidad\n\n\nprint('TOTAL: $' + str(checkout(productos, carrito)))","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej4/hito2_ej4_4683aa4700338fba4fb4a66b005f5e5d.py","file_name":"hito2_ej4_4683aa4700338fba4fb4a66b005f5e5d.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38895839150","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 21 20:19:58 2020\r\n\r\n@author: lujingyi\r\n\"\"\"\r\n\r\nimport os\r\nfrom os.path import join\r\nimport cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pylab as pl\r\nimport extract_information_flow_field_copy as OF\r\n\r\nfolder_path = \"20200318-181952/\"\r\n\r\n#Find out how many figures are in the sequence\r\npath, dirs, files = next(os.walk( folder_path ))\r\n\r\n#Inizialize empty array for images\r\nimages = np.empty(len( files ), dtype=object)\r\n\r\n#Load all images in the correct sequence and convert colors\r\ni= 0\r\nfilenames = os.listdir(folder_path)\r\nfilenames.sort(key=lambda x:int(x[:-4]))\r\nfor image in filenames:\r\n    images[i] = cv2.imread( folder_path+image )\r\n    images[i] = cv2.cvtColor(images[i], cv2.COLOR_RGB2BGR)\r\n    i= i+1\r\n    \r\n#img = images[543]\r\n#plt.imshow(img),plt.show()\r\n\r\nerrtab=[]\r\ntoctab=[]\r\nfor i in range(543,len(images)-1): #only testing images with trees\r\n    index1 = i\r\n    index2 = i+1\r\n    points_old, points_new, flow_vectors = OF.show_flow(index1, index2,images)\r\n    pu, pv, err=OF.estimate_linear_flow_field(points_old, flow_vectors, RANSAC=True, n_iterations=100, error_threshold=10.0)\r\n    errtab.append(err)\r\n    toc = OF.time_to_contact(pu,pv)\r\n    toctab.append(toc)\r\n    \r\n    \r\n#time_start = time.clock()    \r\n#index1 = 544\r\n#index2 = 545\r\n#\r\n#points_old, points_new, flow_vectors = OF.show_flow(index1, index2,images);\r\n#pu, pv, err=OF.estimate_linear_flow_field(points_old, flow_vectors, RANSAC=False, n_iterations=100, error_threshold=10.0)\r\n#toc = OF.time_to_contact(pu,pv)\r\n#print(time.clock() - time_start)\r\n#===============================================================================\r\n#n_points = points_old.shape[0];\r\n#\r\n## make a matrix A with elements [x,1] \r\n#A_hor = np.concatenate((points_old[:,0].reshape(n_points,1), np.ones([points_old.shape[0], 1])), axis=1);\r\n## Moore-Penrose pseudo-inverse:\r\n## https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse\r\n#pseudo_inverse_A_hor = np.linalg.pinv(A_hor);\r\n## target = horizontal flow:\r\n#u_vector = flow_vectors[:,0];\r\n## solve the linear system:\r\n#pu = np.dot(pseudo_inverse_A_hor, u_vector);\r\n## calculate how good the fit is:\r\n#errs_u = np.abs(np.dot(A_hor, pu) - u_vector);\r\n#\r\n## Plot the fit:\r\n#plt.figure();\r\n#plt.plot(points_old[:,0], flow_vectors[:,0], 'x');\r\n#min_x = np.min(points_old[:,0]);\r\n#u_min_x = np.dot(np.asarray([min_x, 1]).reshape(1,2), pu);\r\n#max_x = np.max(points_old[:,0]);\r\n#u_max_x = np.dot(np.asarray([max_x, 1]).reshape(1,2), pu);\r\n#plt.plot([min_x, max_x], [u_min_x, u_max_x], 'r');\r\n#plt.xlabel('x [px]');\r\n#plt.ylabel('u [px]');\r\n#plt.title('Horizontal flow fit')\r\n#\r\n## make a matrix A with elements [y,1]\r\n#A_ver = np.concatenate((points_old[:,1].reshape(n_points,1), np.ones([points_old.shape[0], 1])), axis=1);\r\n## Moore-Penrose pseudo-inverse:\r\n## https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse\r\n#pseudo_inverse_A_ver = np.linalg.pinv(A_ver);\r\n## target = vertical flow:\r\n#v_vector = flow_vectors[:,1];\r\n#pv = np.dot(pseudo_inverse_A_ver, v_vector);\r\n#errs_v = np.abs(np.dot(A_ver, pv) - v_vector);\r\n#err = (np.mean(errs_u) + np.mean(errs_v)) / 2.0;\r\n#\r\n## Plot the fit:\r\n#plt.figure();\r\n#plt.plot(points_old[:,1], flow_vectors[:,1], 'x');\r\n#min_y = np.min(points_old[:,1]);\r\n#v_min_y = np.dot(np.asarray([min_y, 1]).reshape(1,2), pv);\r\n#max_y = np.max(points_old[:,1]);\r\n#v_max_y = np.dot(np.asarray([max_y, 1]).reshape(1,2), pv);\r\n#plt.plot([min_y, max_y], [v_min_y, v_max_y], 'r');\r\n#plt.xlabel('y [px]');\r\n#plt.ylabel('v [px]');\r\n#plt.title('Vertical flow fit');\r\n#\r\n#\r\n#print('pu = {} (W/Z), {} (-U/Z)'.format(pu[0], pu[1]))\r\n#print('Error u = {}, error v = {}'.format(np.mean(errs_u), np.mean(errs_v)))\r\n#===============================================================================\r\n#n_points = points_old.shape[0];\r\n#\r\n##RANSAC parameters - change to do the exercise:\r\n#n_iterations=50;\r\n#error_threshold=10.0;\r\n#sample_size = 3; # 3 is the minimum for this fit. You can set it higher, though.\r\n#\r\n# # This is a RANSAC method to better deal with outliers\r\n## matrices and vectors for the big system:\r\n#A = np.concatenate((points_old, np.ones([points_old.shape[0], 1])), axis=1);\r\n#u_vector = flow_vectors[:,0];\r\n#v_vector = flow_vectors[:,1];\r\n#\r\n## solve many small systems, calculating the errors:\r\n#errors = np.zeros([n_iterations, 2]);\r\n#pu = np.zeros([n_iterations, 3])\r\n#pv = np.zeros([n_iterations, 3])\r\n#for it in range(n_iterations):\r\n#    inds = np.random.choice(range(n_points), size=sample_size, replace=False);\r\n#    AA = np.concatenate((points_old[inds,:], np.ones([sample_size, 1])), axis=1);\r\n#    pseudo_inverse_AA = np.linalg.pinv(AA);\r\n#    # horizontal flow:\r\n#    u_vector_small = flow_vectors[inds, 0];\r\n#    # pu[it, :] = np.linalg.solve(AA, UU);\r\n#    pu[it,:] = np.dot(pseudo_inverse_AA, u_vector_small);\r\n#    errs = np.abs(np.dot(A, pu[it,:]) - u_vector);\r\n#    errs[errs > error_threshold] = error_threshold;\r\n#    errors[it, 0] = np.mean(errs);\r\n#    # vertical flow:\r\n#    v_vector_small = flow_vectors[inds, 0];\r\n#    # pv[it, :] = np.linalg.solve(AA, VV);\r\n#    pv[it, :] = np.dot(pseudo_inverse_AA, v_vector_small);\r\n#    errs = np.abs(np.dot(A, pv[it,:]) - v_vector);\r\n#    errs[errs > error_threshold] = error_threshold;\r\n#    errors[it, 1] = np.mean(errs);\r\n#\r\n## take the minimal error\r\n#errors = np.mean(errors, axis=1);\r\n#ind = np.argmin(errors);\r\n#err = errors[ind];\r\n#pu = pu[ind, :];\r\n#pv = pv[ind, :];\r\n#\r\n## Plot the fit:\r\n#pu = np.asarray([pu[0], pu[2]]).reshape(2,1);\r\n#plt.figure();\r\n#plt.plot(points_old[:,0], flow_vectors[:,0], 'x');\r\n#min_x = np.min(points_old[:,0]);\r\n#u_min_x = np.dot(np.asarray([min_x, 1]).reshape(1,2), pu);\r\n#u_min_x = u_min_x[0][0];\r\n#max_x = np.max(points_old[:,0]);\r\n#u_max_x = np.dot(np.asarray([max_x, 1]).reshape(1,2), pu);\r\n#u_max_x = u_max_x[0][0];\r\n#plt.plot([min_x, max_x], [u_min_x, u_max_x], 'r');\r\n#plt.xlabel('x [px]');\r\n#plt.ylabel('u [px]');\r\n#plt.title('Horizontal flow fit')\r\n#\r\n## Plot the fit:\r\n#pv = np.asarray([pv[0], pv[2]]).reshape(2,1);\r\n#plt.figure();\r\n#plt.plot(points_old[:,1], flow_vectors[:,1], 'x');\r\n#min_y = np.min(points_old[:,1]);\r\n#v_min_y = np.dot(np.asarray([min_y, 1]).reshape(1,2), pv);\r\n#v_min_y = v_min_y[0][0];\r\n#max_y = np.max(points_old[:,1]);\r\n#v_max_y = np.dot(np.asarray([max_y, 1]).reshape(1,2), pv);\r\n#v_max_y = v_max_y[0][0];\r\n#plt.plot([min_y, max_y], [v_min_y, v_max_y], 'r');\r\n#plt.xlabel('y [px]');\r\n#plt.ylabel('v [px]');\r\n#plt.title('Vertical flow fit');","repo_name":"giulialeto/MAV-Prototyping","sub_path":"toc.py","file_name":"toc.py","file_ext":"py","file_size_in_byte":6502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30862374779","text":"def personal_info(name, age, address):\n    print('이름 : ', name)\n    print('나이 : ', age)\n    print('주소 : ', address)\n\npersonal_info('홍길동', 25, '대구')\n\n# 딕셔너리 언팩킹\npersonal_info(**{'name' : '홍길동', 'address' : '대구', 'age' : 25})\n\ndef personal_info1(**info):\n    # print(info)\n    for k, v in info.items():\n        print(k,v)\n\npersonal_info1(name='홍길동', address='대구', age=25, tel='111-2222')","repo_name":"jjeoni/pythonproject","sub_path":"day04/func_test03.py","file_name":"func_test03.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70984865530","text":"#coding:utf-8\r\n\r\nfrom flask import Flask, render_template, url_for, request, redirect\r\nfrom flask_bootstrap import Bootstrap\r\nimport model\r\nimport json\r\nfrom crontab import CronTab\r\nimport config\r\n\r\napp = Flask(__name__)\r\nBootstrap(app)\r\n\r\n@app.route('/')\r\ndef list():\r\n\tjobs = model.getJobs()\r\n\treturn render_template(\"list.html\", jobs=jobs, obName=config.obName)\r\n\r\n@app.route('/edit/<id>', methods=['GET', 'POST'])\r\ndef edit(id):\r\n\tif request.method == 'GET':\r\n            job = model.getJobById(id)\r\n            return render_template(\"edit.html\", job=job)\r\n\telif request.method == \"POST\":\r\n\t\tupdateData = {\r\n\t\t\t\"name\":request.form['name'],\r\n\t\t\t\"demander\":request.form['demander'],\r\n\t\t\t\"desc\":request.form['desc'],\r\n\t\t\t\"cycle\":request.form['cycle'],\r\n            \"crontabTime\":request.form['crontabTime'],\r\n\t\t\t\"jobTime\":request.form['jobTime'],\r\n\t\t\t\"shPath\":request.form['shPath'],\r\n\t\t\t\"receivers\":request.form['receivers']\r\n\t\t}\r\n\r\n\t\t#检查crontab是否需要修改\r\n\t\tresData = model.getCrontabTimeById(id)\r\n\t\t\r\n\t\tif resData[1] == request.form['crontabTime'] or int(resData[2]) == 1:\r\n\t\t\tpass\r\n\t\telse:\r\n\t\t\tjobName = resData[0]\r\n\t\t\tcronUser = CronTab(user = True)\r\n\t\t\teditJob = cronUser.find_comment(jobName)\r\n\t\t\tcronUser.remove(editJob)\r\n\t\t\taddJob = cronUser.new(command = config.cronPath+'/'+request.form['shPath'])\r\n\t\t\taddJob.setall(request.form['crontabTime'])\r\n\t\t\taddJob.set_comment(request.form['name'])\r\n\t\t\tcronUser.write()\r\n\r\n\t\tres = model.updateJob(id, updateData)\r\n\t\tif res == True:\r\n\t\t\treturn redirect('/')\r\n\t\telse:\r\n\t\t\treturn \"修改出错\"\r\n\r\n@app.route(\"/add\", methods=[\"GET\", \"POST\"])\r\ndef add():\r\n\tif request.method == \"GET\":\r\n\t\treturn render_template(\"add.html\")\r\n\telif request.method == \"POST\":\r\n\t\tcronType = request.form['cronType']\r\n\t\tif cronType == '每天':\r\n\t\t\tcronH = request.form['cronHTime']\r\n\t\t\tcronM = request.form['cronMTime']\r\n\t\t\tcrontabTime = '%d %d * * *'%(int(cronM), int(cronH))\r\n\t\telif cronType == '每周':\r\n\t\t\tcronH = request.form['cronHTime']\r\n\t\t\tcronM = request.form['cronMTime']\r\n\t\t\tweekNum = request.form['cronDay']\r\n\t\t\tcrontabTime = '%d %d * * %d'%(int(cronM), int(cronH), int(weekNum))\r\n\t\telif cronType == '每月':\r\n\t\t\tcronH = request.form['cronHTime']\r\n\t\t\tcronM = request.form['cronMTime']\r\n\t\t\tmonthNum = request.form['cronDay']\r\n\t\t\tcrontabTime = '%d %d %d * *'%(int(cronM), int(cronH), int(monthNum))\r\n\t\telif cronType == '自定义':\r\n\t\t\tcrontabTime = request.form['crontabTime']\r\n\r\n\t\tcrontabTime = crontabTime.strip()\r\n\t\tinsertData = {\r\n\t\t\t\"name\":request.form['name'],\r\n\t\t\t\"demander\":request.form['demander'],\r\n\t\t\t\"desc\":request.form['desc'],\r\n\t\t\t\"cycle\":request.form['cycle'],\r\n\t\t\t\"jobTime\":request.form['jobTime'],\r\n            \"crontabTime\": crontabTime,\r\n\t\t\t\"shPath\":request.form['shPath'],\r\n\t\t\t\"receivers\":request.form['receivers']\r\n\t\t}\r\n\r\n\t\tres = model.addJob(insertData)\r\n\t\t#添加crontab任务\r\n\t\tcronUser = CronTab(user=True)\r\n\t\tcod = config.cronPath + '/'+request.form['shPath']\r\n\t\tjob = cronUser.new(command=cod)\r\n\t\tjob.setall(crontabTime)\r\n\t\tjob.set_comment(request.form['name'])\r\n\t\tcronUser.write()\r\n\r\n\t\tif res == True:\r\n\t\t\treturn redirect('/')\r\n\t\telse:\r\n\t\t\treturn \"添加出错\"\r\n\r\n@app.route(\"/stop/<id>\", methods=[\"GET\", \"POST\"])\r\ndef stop(id):\r\n\t#将delete_flag 设置为1\r\n\tmodel.change(id, 'stop')\r\n\r\n\t#将crontab失效\r\n\tresData = model.getCrontabTimeById(id)\r\n\tjobName = resData[0]\r\n\tcronUser = CronTab(user = True)\r\n\teditJob = cronUser.find_comment(jobName)\r\n\tcronUser.remove(editJob)\r\n\tcronUser.write()\r\n\treturn redirect('/')\r\n\r\n@app.route(\"/restart/<id>\", methods=[\"GET\", \"POST\"])\r\ndef restart(id):\r\n\t#将delete_flag 设置为1\r\n\tmodel.change(id, 'restart')\r\n\r\n\t#新增crontab\r\n\tresData = model.getCrontabTimeById(id)\r\n\tjobName = resData[0]\r\n\t\r\n\tcronUser = CronTab(user = True)\r\n\taddJob = cronUser.new(command = config.cronPath+'/'+resData[3])\r\n\taddJob.setall(resData[1])\r\n\taddJob.set_comment(jobName)\r\n\tcronUser.write()\r\n\treturn redirect('/')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tapp.run(host=\"0.0.0.0\", port=5000, debug=True)\r\n","repo_name":"Onestarko/jobRecsMag","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29377744949","text":"def validar_secuencia(secuencia):\n    secuencia = secuencia.upper()\n    nucleotidos_validos = set(['A', 'C', 'T', 'G'])\n    \n    for nucleotido in secuencia:\n        if nucleotido not in nucleotidos_validos:\n            return \"La secuencia es incorrecta\"\n    \n    return \"La secuencia es correcta\"\n\n\ndef contar_palabras(frase):\n    palabras = frase.split()\n    return len(palabras)\n\n\nif __name__ == \"__main__\":\n    secuencia_adn = input(\"Ingrese una secuencia de ADN: \")\n    resultado_secuencia = validar_secuencia(secuencia_adn)\n    print(resultado_secuencia)\n    \n    frase = input(\"Ingrese una frase: \")\n    cantidad_palabras = contar_palabras(frase)\n    print(\"La frase tiene\", cantidad_palabras, \"palabras.\")\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej2/hito2_ej2_7a1fa6091f78d02626a3a4df04bc0972.py","file_name":"hito2_ej2_7a1fa6091f78d02626a3a4df04bc0972.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43968288802","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport cv2 as cv\nimport numpy as np\n#import cscore as cs\n#from cscore import CameraServer\nimport threading\nimport logging\nimport pupil_apriltags\nfrom imutils.video import VideoStream\nimport argparse\nimport datetime\nimport time\n\n\nfrom networktables import NetworkTables\nfrom networktables.util import ChooserControl\n\nfrom AprilTagCapture import NetworkTablesVisionHelper\nfrom AprilTagCapture import ATagCapture\n\n\ntablename = \"SmartDashboard\"\nip_address = \"10.42.53.2\"\naprilTag_size = 0.1651\ncameramtx_filename = \"./images/cheapcam/cameramtx.npz\"\n\n\nvisionHelper = NetworkTablesVisionHelper(ip_address)\n\nNetworkTables.initialize(ip_address)\nsd = NetworkTables.getTable(\"SmartDashboard\")\n#logging.basicConfig(level=logging.DEBUG)\n#import networktables\n#nt = networktables.NetworkTablesInstance()\n#nt.initialize(server='10.42.53.2')\n\n\nFRAME_WIDTH = 640\nFRAME_HEIGHT = 480\n\n#cs = CameraServer.getInstance()\n#cs.enableLogging()\n\n#camera = cs.startAutomaticCapture()\n\n#camera.setResolution(FRAME_WIDTH, FRAME_HEIGHT)\n\n#camera = cs.CvSource(\"cvsource\", cs.VideoMode.PixelFormat.kMJPEG, 640, 480, 30)\n\n# Get a CvSink. This will capture images from the camera\n#cvSink = cs.getVideo()\n\n# (optional) Setup a CvSource. This will send images back to the Dashboard\n# outputStream = cs.putVideo(\"Rectangle\", FRAME_WIDTH, FRAME_HEIGHT)\nprint(\"[INFO] detecting AprilTags...\")\n# Allocating new images is very expensive, always try to preallocate\n\n#windowName = \"Live video Feed\"\n#cv2.namedWindow(windowName)\nlocal_clock = 0\nremote_clock = 0\n\ndef sync_networktables_time(table, key, value, isNew):\n    global local_clock\n    global remote_clock\n    if key == \"robottime\":\n        local_clock = time.perf_counter()\n        remote_clock = value\n        sd.putNumber(\"RPitime\", value)\n    \n\n\n\ndef gen_frames(visionHelper):\n    while(True):\n        visionHelper.processVideos(drawAxes=True,drawMask=True)\n        visionHelper.outputVideo()\n\n\n        #output = frame.tobytes()\n        #yield (b'--frame\\r\\n'\n        #           b'Content-Type: image/jpeg\\r\\n\\r\\n' + output + b'\\r\\n')  # concat frame one by one and show result\n        #cv2.imshow(\"Trackin  g... \", frame)\n        #cv2.imshow(\"Gray\", gray)\n        #cv2.imshow(windowName, frame)\n        key = cv.waitKey(1) & 0xFF\n        # if the `q` key was pressed, break from the loop\n        if key == ord(\"q\"):\n            break\n\n\n\n\nif __name__ == '__main__':\n    num_cameras = len(visionHelper.getSinks())\n    for (camera_num, cv_sink) in zip(range(num_cameras),visionHelper.getSinks()):\n        print(\"Adding apriltag capture \" + str(camera_num))\n        visionHelper.addAprilTagCapturing(ATagCapture(aprilTagSize=aprilTag_size, \n                                                      cameramtx_filename=cameramtx_filename, cvSink=cv_sink))\n    sd.addEntryListener(sync_networktables_time,key=\"robottime\",immediateNotify=True)\n    visionHelper.syncTimes()\n    gen_frames(visionHelper=visionHelper)\n    #app.run(debug=True,host=\"0.0.0.0\")\n\n#th = threading.Thread(target=video_thread, daemon=True)\n#th.start()\n\n#mjpegServer = cs.MjpegServer(\"httpserver\", 8081)\n#mjpegServer.setSource(camera)\n\n#print(\"mjpg server listening at http://0.0.0.0:8081\")\ninput(\"Press enter to exit...\")\n\ncv.destroyAllWindows()\n","repo_name":"TASRobotics/RaidZero-FRC-Vision","sub_path":"Working/AprilTags/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"45043613616","text":"import numpy as np\nimport math\nimport random\n\nclass TS_Gibbs:\n\t\n\tdef __init__(self, alpha):\n\t\tself.alpha = alpha\n\n\t\tself.articles_clicks = dict()\n\t\t\n\tdef add_new_article(self, article_id):\n\t\tif article_id not in self.articles_clicks:\n\t\t\tself.articles_clicks[article_id] = list()\n\n\tdef update(self, user, selected_article, click):\n\t\tself.articles_clicks[selected_article].append(click)\t\t\n\n\tdef warmup(self, fo):\n\t\tpass\n\n\tdef select(self, user, pre_selected_article, lines, total_impressions, click):\n\t\tbest_value_articles = list()\n\t\tbest_value = -1\n\t\tselected_article = -1\n\n\t\tfor line in lines:\n\t\t\tarticle_id = int(line.split(\" \")[0])\n\t\t\tself.add_new_article(article_id)\t\t\t\n\t\t\t\n\t\t\tif len(self.articles_clicks[article_id]) == 0:\n\t\t\t\tcur_value = 0.5\n\t\t\telse:\n\t\t\t\tindex = random.randint(0, len(self.articles_clicks[article_id])-1)\t\n\t\t\t\tcur_value = self.articles_clicks[article_id][index]\n\n\t\t\tif best_value < cur_value:\n\t\t\t\tbest_value = cur_value\n\t\t\t\tbest_value_articles = list([article_id])\n\t\t\telif best_value == cur_value:\n\t\t\t\tbest_value_articles.append(article_id)\n\t\t\t\n\t\tindex = random.randint(0, len(best_value_articles)-1)\t\n\t\tselected_article = best_value_articles[index]\n\n\t\treturn selected_article, False\n\n","repo_name":"anikiforova/thesis","sub_path":"Yahoo/TS_Gibbs.py","file_name":"TS_Gibbs.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69966063610","text":"from django.shortcuts import render, redirect\n\nfrom .models import *\nfrom apps.products.models import Category, Product\nfrom apps.telegram.views import get_text\n\n\n# Create your views here.\ndef index(request):\n    settings = Settings.objects.latest('id')\n    about = About.objects.latest('id')\n    categories = Category.objects.all()\n    products = Product.objects.all().order_by('?')\n    best_product = Product.objects.all().order_by('?')[:1]\n    return render(request, \"base/index.html\",locals())\n\ndef about(request):\n    settings = Settings.objects.latest('id')\n    about = About.objects.latest('id')\n    best_products = Product.objects.all().order_by('?')\n    return render(request, \"base/about.html\",locals())\n\ndef contact(request):\n    settings = Settings.objects.latest('id')\n    about = About.objects.latest('id')\n    if request.method ==\"POST\":\n        name = request.POST.get('name')\n        phone = request.POST.get('phone')\n        message = request.POST.get('message')\n        cause = request.POST.get('cause')\n        Contact.objects.create(name=name, phone=phone, message=message, cause=cause)\n\n        get_text(f\"\"\" Оставлен отзыв \nИмя пользователя: {name}\nНомер телефона: {phone}\nПричина: {cause}\nСообщение: {message}\n\"\"\")\n        return redirect('index')\n\n    return render(request, \"base/contact.html\", locals())","repo_name":"Simon070805/Barmak.store","sub_path":"apps/settings/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75115793209","text":"# Bonus Work #5: Printing Operations (Multiplication and Division) - 19/9/22\n\n# Print the multiplication of two numbers\n\nprint(\"\\n-----Printing the multiplication of two numbers-----\\n\")\nnum1 = int(input(\"Enter the first number: \"))\nnum2 = int(input(\"Enter the second number: \"))\nresult = float\nresult = num1 * num2\nprint(\"The product of the two numbers is\", result)\n\n# Divide the number 5 by 2 and print the answer\n\nprint(\"\\n-----Dividing the number 5 by 2-----\\n\")\nnum1 = 5\nnum2 = 2\nresult = float\nresult = num1 / num2\nprint(num1, \"/\", num2, \"=\", result)\nprint()","repo_name":"Code-Lab-1/programming-skills-portfolio-psjasmin","sub_path":"Chapter 1- Getting Started with Python/Exercises/Bonus Work_5.py","file_name":"Bonus Work_5.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38707030671","text":"\"\"\"\n5\n1 2 3 1 2\n\"\"\"\n# 투 포인터로 구현 가능\n\nfrom sys import stdin\nfrom collections import defaultdict\n\nN = int(stdin.readline())\nls = list(map(int, stdin.readline().strip().split()))\ndic = defaultdict(int)\n\nanswer = 0\nmx = 0\nfor i in range(N):\n    key = ls[i]\n    mx = max(dic[key], mx)\n    answer += (i - mx + 1)\n    dic[key] = i+1\nprint(answer)","repo_name":"Ju-yeop/Algorithm_BaekJoon","sub_path":"Impl_13144.py","file_name":"Impl_13144.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13584114609","text":"import pandas as pd\nimport numpy as np\nimport h5py\nimport os\nfrom emlutilities.core import resize, result_metrics, compare\n\ndef test_resizer():\n\n    eq_catalog = pd.read_csv(\"ModelsAndSampleData/100samples.csv\")\n    hdf5_data = h5py.File(\"ModelsAndSampleData/100samples.hdf5\", 'r')\n\n    output_name = \"resized\"\n    size=50\n\n    sampled_hdf5, sampled_eq_catalog = resize(hdf5_data, eq_catalog, size, output_name)\n\n    os.remove(\"{}_{}.hdf5\".format(output_name, size))\n\n    assert len(sampled_eq_catalog) == 50\n\ndef test_results_metrics():\n\n    eqt = pd.read_csv(\"ModelsAndSampleData/X_test_results_EQT.csv\")\n    test_dict = {'EQT':eqt}\n    test_columns = np.array([\"model_name\",\"det_recall\",\"det_precision\",\"d_tp\",\"d_fp\",\"d_tn\",\"d_fn\",\"p_recall\",\"p_precision\",\"p_mae\",\"p_rmse\",\"p_tp\",\"p_fp\",\"p_tn\",\"p_fn\",\"s_recall\",\"s_precision\",\"s_mae\",\"s_rmse\",\"s_tp\",\"s_fp\",\"s_tn\",\"s_fn\",\"#events\",\"#noise\"])\n    result_metrics(test_dict)\n    \n    csv = pd.read_csv(\"test_result_metrics.csv\")\n    \n    comparison = csv.columns == test_columns\n    equal_arrays = comparison.all()\n\n    assert equal_arrays\n\n    os.remove('test_result_metrics.csv')\n\n    \ndef test_compare():\n    dataframe = pd.read_csv(\"ModelsAndSampleData/test_results.csv\", index_col=0)\n    a = compare(dataframe)\n    assert a.loc[\"test\"]['det_recall'] == 'Better'\n\n\n\n","repo_name":"mrp3anut/emlutilities","sub_path":"test/test_emlutilities.py","file_name":"test_emlutilities.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36569971000","text":"import os\nimport pendulum\nimport pandas as pd\nfrom airflow import DAG\nfrom pathlib import Path\nfrom airflow.macros import ds_add\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.python import PythonOperator\n\n\nwith DAG(\n    'dados_climaticos',\n    start_date=pendulum.datetime(2022, 8, 22, tz=\"UTC\"),\n    schedule_interval='0 0 * * 1'\n) as dag:\n\n    tarefa_1 = BashOperator(\n        task_id='cria_pasta',\n        bash_command='mkdir '\n        f\"\"\"-p {os.path.abspath(\n            \"dados/semana_{{data_interval_end.strftime('%Y-%m-%d')}}\")}\"\"\"\n    )\n\n    def extrai_dados(data_interval_end):\n        city = 'Boston'\n        key = 'S2288F34GLCJJ42JH4WAYS4DS'\n\n        URL = 'https://weather.visualcrossing.com/'\\\n            'VisualCrossingWebServices/rest/' \\\n            f'services/timeline/{city}/{data_interval_end}/'\\\n            f'{ds_add(data_interval_end, 7)}?' \\\n            f'unitGroup=metric&include=days&key={key}&contentType=csv'\n\n        dados = pd.read_csv(URL)\n\n        # Criação da pasta da salvar os dados buscados pela API\n        dir_data_path = os.path.abspath(f\"dados/semana_{data_interval_end}\")\n        dir_path = Path(dir_data_path)\n\n        # salvando dados\n        dados.to_csv(dir_path / 'dados_brutos.csv', index=False)\n        dados[['datetime', 'tempmin', 'temp', 'tempmax']].\\\n            to_csv(dir_path / 'temperaturas.csv', index=False)\n        dados[['datetime', 'description', 'icon']].\\\n            to_csv(dir_path / 'condicoes.csv', index=False)\n\n    tarefa_2 = PythonOperator(\n        task_id='extrai_dados',\n        python_callable=extrai_dados,\n        op_kwargs={\n            'data_interval_end': \"{{data_interval_end.strftime('%Y-%m-%d')}}\"}\n    )\n\n    tarefa_1 >> tarefa_2    # type: ignore\n","repo_name":"Lyarkh/Estudo_Airflow","sub_path":"01_Primeira_Pipeline/Airflow_Dags/dags/dados_climaticos.py","file_name":"dados_climaticos.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"26117811952","text":"import numpy as np\nfrom subprocess import check_output\n\nART_FILE = \"../data/valid.rsa.art.txt\"\nTGT_FILE = \"../data/valid.rsa.tgt.txt\"\n\n# s0 output\nLIT_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1023_173511_acc26.59_rglf42.62.txt\"#10\n#\"/home/hansonlu/links/data/2031val/2031val_1023_172927_acc34.81_rglf42.62.txt\"#5\n#\"/home/hansonlu/links/data/2031val/2031val_1023_172437_acc55.74_rglf42.62.txt\"#2\n\n# alpha = 0.5\nLOW_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1013_140725_acc55.24_rglf42.58.txt\"#2\n#\"/home/hansonlu/links/data/2031val/2031val_1014_212030_acc35.11_rglf42.3.txt\"#5\n#\"/home/hansonlu/links/data/2031val/2031val_1018_005633_acc26.29_rglf42.02.txt\"#10\n\n# alpha = 1.5\nMID_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1015_002551_acc36.93_rglf40.91.txt\"#5\n#\"/home/hansonlu/links/data/2031val/2031val_1018_034120_acc30.28_rglf38.66.txt\"#10\n#\"/home/hansonlu/links/data/2031val/2031val_1013_153233_acc56.43_rglf42.02.txt\"#2\n\n# alpha = 3\nHI_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1023_192339_acc58.84_rglf40.79.txt\"\n#\"/home/hansonlu/links/data/2031val/2031val_1023_222423_acc43.13_rglf34.75.txt\"#5\n#\"/home/hansonlu/links/data/2031val/2031val_1030_010438_acc37.81_rglf27.11.txt\"#10\n\nS0_B20_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1106_150510_acc35.3_rglf42.27.txt\"\n\n# alpha = 1.5, beam=20\nS1_B20_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1110_133158_acc39.19_rglf40.57.txt\"#5\n#\"/home/hansonlu/links/data/2031val/2031val_1106_005815_acc33.04_rglf38.44.txt\"#10\n\nGROW_P1_B10_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1106_214353_acc35.99_rglf41.63.txt\"#5\n\nGROW_P1_B20_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1106_222500_acc35.45_rglf41.39.txt\"#5\n\nMEMOIZED_L1_B10_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1109_235527_acc36.24_rglf41.89.txt\"#5\n\nMEMOIZED_L1_B20_FILE = \\\n\"/home/hansonlu/links/data/2031val/2031val_1110_010214_acc42.49_rglf34.77.txt\"#5\n\nreadable = True\nsample = 100\nOUT_FILE = \"memo_l1/memol1_qual_sample_Nov10_4distr.txt\"\n\nexamples = []\n\n# files = [open(ART_FILE, \"r\"),\n#          open(TGT_FILE, \"r\"),\n#          open(LIT_FILE, \"r\"),\n#          open(LOW_FILE, \"r\"),\n#          open(MID_FILE, \"r\"),\n#          open(HI_FILE, \"r\")]\n# columns = ['source','gold','s0','alpha0.5','alpha1.5','alpha3']\n# files = [open(ART_FILE, \"r\"),\n#          open(TGT_FILE, \"r\"),\n#          open(LIT_FILE, \"r\"),\n#          open(S0_B20_FILE, \"r\"),\n#          open(MID_FILE, \"r\"),\n#          open(S1_B20_FILE, \"r\")]\n# columns = ['source','gold','s0_beam10','s0_beam20','s1_beam10','s1_beam20']\nfiles = [open(ART_FILE, \"r\"),\n         open(TGT_FILE, \"r\"),\n         open(LIT_FILE, \"r\"),\n         open(MID_FILE, \"r\"),\n         open(S1_B20_FILE, \"r\"),\n         open(MEMOIZED_L1_B10_FILE, \"r\"),\n         open(MEMOIZED_L1_B20_FILE, \"r\")]\ncolumns = ['      source', '        gold','      s0_b10','s1_basic_b10','s1_basic_b20',' s1_memo_b10',' s1_memo_b20']\n\nassert len(files) == len(columns)\n\nif sample:\n    num_lines = int(check_output(['wc', '-l', TGT_FILE]).split()[0])\n    idxs = np.random.choice(num_lines, sample, replace=False).tolist()\n    idxs.sort(reverse=True)\n\nwith open(OUT_FILE, \"w\") as f:\n    if readable:\n        f.write('\\n'.join(columns) + '\\n\\n')\n    else:\n        f.write('\\t'.join(columns) + '\\n')\n    for i, ss in enumerate(zip(*files)):\n        if sample:\n            if len(idxs) > 0 and i == idxs[-1]: idxs.pop()\n            else: continue\n        ss = [s.rstrip() for s in ss]\n        if readable:\n            f.write('\\n'.join([tag + ': ' + s for tag, s in\n                               zip(columns, ss) ]) + '\\n\\n')\n        else:\n            f.write('\\t'.join(ss) + '\\n')\n    # for src, gold, s0, low, mid, hi in zip(art_f, tgt_f, lit_f, low_f, mid_f, hi_f):\n    #     if CSV:\n    #         f.write(\"%s,\\n%s,\\n%s,\\n%s,\\n%s,\\n%s\\n\\n\"\n    #                 % (src.rstrip(), gold.rstrip(), s0.rstrip(), low.rstrip(), mid.rstrip(), hi.rstrip()))\n    #     else:\n    #         f.write(\"%s\\t%s\\t%s\\t%s\\t%s\\t%s\\n\"\n    #                 % (src.rstrip(), gold.rstrip(), s0.rstrip(), low.rstrip(), mid.rstrip(), hi.rstrip()))\n\nfor f in files:\n    f.close()\n","repo_name":"hansonhl/bbrsa","sub_path":"analysis/qual_analysis.py","file_name":"qual_analysis.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40549747919","text":"import pandas\n\ndef read_Csv(filename):\n    '''\n    读取csv文件\n    :param filename:文件路径\n    :return:\n    '''\n    try:\n        DataFrame = pandas.read_csv(filename)\n    except Exception as e:\n        print('Error occured when opening excel file: {}'.format(e))\n        return pandas.DataFrame(columns=['Empty'])\n    return DataFrame\n\ndef read_Excel(filename, sheetname_list):\n    '''\n    标准pandas读取Excel函数, 从filename路径读取sheetname_list中的任意一个sheet, 只要发现任意一个就返回\n    打开文件失败或者没有sheetname,则返回空的DataFrame\n    :param filename:  文件路径\n    :param sheetname_list:  [可能的sheetname list]\n    :return: DataFrame(打开文件失败或者没有sheetname,则返回空的DataFrame)\n    '''\n    try:\n        DataFrame = pandas.ExcelFile(filename)\n    except Exception as e:\n        print('Error occured when opening excel file: {}'.format(e))\n        return pandas.DataFrame(columns=['Empty'])\n\n    DataFrame_sheetnames = DataFrame.sheet_names\n    for each in sheetname_list:\n        if each in DataFrame_sheetnames:\n            return DataFrame.parse(each)\n    print('No one sheet in {} Detected in file {}'.format(str(sheetname_list), filename))\n    return pandas.DataFrame(columns=['Empty'])\n\n\ndef validate_DataFrameCol(DataFrame, colname_list):\n    '''\n    检验DataFrame中是否包含所需的所有column(按照colname来索引)\n    :param DataFrame: 需要检验的DataFrame\n    :param colname_list: [col_name...]格式的列表\n    :return: 检查结果,布尔型\n    '''\n    col_names = DataFrame.columns\n    for each in colname_list:\n        if each not in col_names:\n            print('Column {} Not Detected in DataFrame'.format(each))\n            return False\n    return True\n\n\ndef validate_DataFrameColType(DataFrame, coltype_dict):\n    '''\n    检验DataFrame中是否包含所需的所有column(按照colname来索引)\n    :param DataFrame: 需要检验的DataFrame\n    :param coltype_dict: {col_name:dtype}格式的字典\n    :return: 检查结果的String\n    '''\n    for each_col, each_type in coltype_dict.items():\n        try:\n            DataFrame[each_col] = DataFrame[each_col].astype(each_type)\n        except ValueError:\n            'There are ValueError in \"{}\" Column. Data Type Should Be \"{}\". Please check.'.format(each_col, each_type)\n            return 'There are ValueError in \"{}\" Column. Data Type Should Be \"{}\". Please check.'.format(each_col,\n                                                                                                         each_type)\n        except KeyError:\n            return 'Column {} not detected.'.format(each_col)\n    return 'Data Validated, All columns type correct.'\n\n\n","repo_name":"RSF45355382/GSM_AutoRelationer","sub_path":"CommonFunction/ExcelReading.py","file_name":"ExcelReading.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6622609877","text":"from tensorflow.python.keras.layers import Layer, Dense\n\nclass PointWiseFeedForwardNetwork(Layer):\n    def __init__(self, d_model, dff):\n        super(PointWiseFeedForwardNetwork, self).__init__()\n\n        self.d1 = Dense(dff, activation='relu')\n        self.d2 = Dense(d_model)\n\n    def call(self, inputs):\n        d1_output = self.d1(inputs)\n        d2_output = self.d2(d1_output)\n        return d2_output","repo_name":"mpafla/question-generation","sub_path":"models/layers/point_wise_feed_forward_network.py","file_name":"point_wise_feed_forward_network.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7900410986","text":"\nfrom scenario import Scenario\nfrom pedestrian import Pedestrian\n\n\nif __name__ == '__main__':\n\n    method = \"SIR\"                          # Set model\n    primary_scenario = \"SIR-1\" # Read scenario from here\n    edited_scenario = \"SIR-4\"  # Save scenario here\n\n    scenario = Scenario(primary_scenario, edited_scenario, method)\n    pedestrian = Pedestrian()\n\n    while True:\n        scenario.choices()\n        choice = input(\"\\nEnter Key: \")\n        if choice == \"i\":\n            pedestrian.print_info(scenario)\n        elif choice == \"a\":\n            pedestrian.add(scenario)\n        elif choice == \"d\":\n            pedestrian.delete(scenario)\n        elif choice == \"e\":\n            pedestrian.edit(scenario)\n        elif choice == \"r\":    \n            scenario.run_scenario(pedestrian)\n        else:\n            print(f\"Exited: No option with key: {choice}\")\n            exit()","repo_name":"Erfan-Mashayekh/MLCMS","sub_path":"Exersice2/scenario-editor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5462302590","text":"from mlagents_envs import base_env\nimport numpy as np\nimport copy\nimport gym\nfrom gym_unity.envs import ActionFlattener\nfrom ray.rllib.env.apis.task_settable_env import TaskSettableEnv\nfrom ray.rllib.models.preprocessors import OneHotPreprocessor\nimport uuid\nfrom soccer_twos.wrappers import MultiAgentUnityWrapper\nimport random\nfrom ray.rllib.utils.annotations import override\n\n\nclass SingleObsWrapper(gym.core.Wrapper):\n    \"\"\"\n    A wrapper to unstack the agent observation, returning only the last information\n    \"\"\"\n\n    def __init__(self, env):\n        super(SingleObsWrapper, self).__init__(env)\n        self.env = env\n\n        #original obs format:\n        # size = 336\n        #[fowardt_-2(88), fowardt_-1(88), fowardt_0(88), backwardt_-2(24), backwardt_-1(24), backwardt_0(24)]\n\n        #single obs format:\n        # size = 112\n        # [fowardt_0(88), backwardt_0(24)]\n        \n        self.observation_space = gym.spaces.Box(\n            0, 1, dtype=np.float32, shape=(112,)\n        )\n\n    def step(self, action):\n        obs, rewards, done, info = self.env.step(action)\n\n        return (\n            self._preprocess_obs(obs),\n            rewards,\n            done,\n            info,\n        )\n    \n    def reset(self):\n        return self._preprocess_obs(self.env.reset())\n\n    def _preprocess_obs(self, obs):\n        return SingleObsWrapper.preprocess_obs(obs)\n\n    @staticmethod\n    def preprocess_obs(obs, **kwargs):\n        new_obs = {}\n        for agent_id, agent_obs in obs.items():\n            new_agent_obs = np.zeros((112,))\n            new_agent_obs[:88] = agent_obs[176:264]\n            new_agent_obs[88:] = agent_obs[312:]\n            new_obs[agent_id] = new_agent_obs\n        return new_obs\n\nclass MultiagentTeamObsWrapper(gym.core.Wrapper):\n    \"\"\"\n    A wrapper for multiagent a environment.\n    Join the observation of each team for each agent.\n    \"\"\"\n\n    def __init__(self, env):\n        super(MultiagentTeamObsWrapper, self).__init__(env)\n        self.env = env\n\n        # duplicate obs space (concatenate team players)\n        self.observation_space = gym.spaces.Box(\n            0, 1, dtype=np.float32, shape=(env.observation_space.shape[0] * 2,)\n        )\n\n    def step(self, action):\n        obs, rewards, done, info = self.env.step(action)\n\n        return (\n            self._preprocess_obs(obs),\n            rewards,\n            done,\n            info,\n        )\n    \n    def reset(self):\n        return self._preprocess_obs(self.env.reset())\n\n    def _preprocess_obs(self, obs):\n        return {\n            0: np.concatenate((obs[1], obs[0])),\n            1: np.concatenate((obs[0], obs[1])),\n            2: np.concatenate((obs[3], obs[2])),\n            3: np.concatenate((obs[2], obs[3])),\n        }\n\n    @staticmethod\n    def preprocess_obs(obs, **kwargs):\n        return {\n            0: np.concatenate((obs[1], obs[0])),\n            1: np.concatenate((obs[0], obs[1])),\n        }\n\nclass AgentIdInfoWrapper(gym.core.Wrapper):\n    \"\"\"\n    A wrapper for multiagent a environment.\n    Adds agent id information to the observation.\n    \"\"\"\n\n    def __init__(self, env):\n        super(AgentIdInfoWrapper, self).__init__(env)\n        self.env = env\n\n        self.observation_space = gym.spaces.Box(\n            0, 1, dtype=np.float32, shape=(env.observation_space.shape[0]+2,)\n        )\n\n    def step(self, action):\n        obs, rewards, done, info = self.env.step(action)\n\n        return (\n            self._preprocess_obs(obs),\n            rewards,\n            done,\n            info,\n        )\n    \n    def reset(self):\n        return self._preprocess_obs(self.env.reset())\n\n    def _preprocess_obs(self, obs):\n        return {\n            0: np.concatenate(([0, 1], obs[0])),\n            1: np.concatenate(([1, 0], obs[1])),\n            2: np.concatenate(([0, 1], obs[2])),\n            3: np.concatenate(([1, 0], obs[3])),\n        }\n\n    @staticmethod\n    def preprocess_obs(obs, **kwargs):\n        return {\n            0: np.concatenate(([0, 1], obs[0])),\n            1: np.concatenate(([1, 0], obs[1])),\n        }\n\nclass PreviousActionWrapper(gym.core.Wrapper):\n    \"\"\"\n    A wrapper that adds agent previous action information to the observation.\n    \"\"\"\n\n    def __init__(self, env):\n        super(PreviousActionWrapper, self).__init__(env)\n        self.env = env\n\n        self.preprocessor = OneHotPreprocessor(env.action_space)\n\n        # duplicate obs space (concatenate team players)\n        self.observation_space = gym.spaces.Box(\n            0, 1, dtype=np.float32, shape=(env.observation_space.shape[0]+self.preprocessor.shape[0],)\n        )\n\n        if isinstance(env.action_space, gym.spaces.Discrete):\n            self.zero_action = self.preprocessor.transform(0)\n        else:\n            self.zero_action = self.preprocessor.transform(np.zeros(env.action_space.shape).astype(int))\n\n        self.last_action = {\n            0: self.zero_action,\n            1: self.zero_action,\n            2: self.zero_action,\n            3: self.zero_action,\n        }\n\n    def step(self, action):\n        for agent_id, a in action.items():\n            self.last_action[agent_id] = self.preprocessor.transform(a)\n\n        obs, rewards, done, info = self.env.step(action)\n\n        return (\n            self._preprocess_obs(obs),\n            rewards,\n            done,\n            info,\n        )\n    \n    def reset(self):\n        self.last_action = {\n            0: self.zero_action,\n            1: self.zero_action,\n            2: self.zero_action,\n            3: self.zero_action,\n        }\n        return self._preprocess_obs(self.env.reset())\n\n    def _preprocess_obs(self, obs):\n        return {\n            0: np.concatenate((self.last_action[0], obs[0])),\n            1: np.concatenate((self.last_action[1], obs[1])),\n            2: np.concatenate((self.last_action[2], obs[2])),\n            3: np.concatenate((self.last_action[3], obs[3])),\n        }\n\n    @staticmethod\n    def preprocess_obs(obs, **kwargs):\n         return {\n            0: np.concatenate((kwargs['last_action'][0], obs[0])),\n            1: np.concatenate((kwargs['last_action'][1], obs[1])),\n        }\n\nclass RandomEnvWrapper(gym.core.Wrapper):\n    \"\"\"\n    A wrapper randomizes the envoriment. \n    (By default Does only randomize Ball position and Player rotation)\n    \"\"\"\n\n    def __init__(self, env, randomize = {'ball': ['position'], 'player': ['rotation_y']}, min_divergence = 0.3, k=1):\n        super(RandomEnvWrapper, self).__init__(env)\n        self.env = env\n        self.randomize = randomize\n        self.base_env = self.env.unwrapped\n        while not isinstance(self.base_env, MultiAgentUnityWrapper):\n            self.base_env = self.base_env.unwrapped\n        self.env_channel = self.base_env._env._side_channel_manager._side_channels_dict[uuid.UUID('3f07928c-2b0e-494a-810b-5f0bbb7aaeca')]\n        \n        self.k = 1\n        self.min_divergence = min_divergence\n\n        self.default_watch_position = {\n            'ball_info': {\n                'position': [0., 0.],\n                'velocity': [0., 0.]\n            },\n            'player_info': {\n                0: {\n                    'position': [-8.190001, -1.2],\n                    'rotation_y': 90.0,\n                    'velocity': [0., 0.],\n                },\n                1: {\n                    'position': [-8.190001,  1.2],\n                    'rotation_y': 90.0,\n                    'velocity': [0., 0.],\n                },\n                2: {\n                    'position': [8.190001, 1.2],\n                    'rotation_y': 270.0,\n                    'velocity': [0., 0.],\n                },\n                3: {\n                    'position': [8.190001, -1.2],\n                    'rotation_y': 270.0,\n                    'velocity': [0., 0.],\n                }\n            }\n        }\n\n        self.default_train_position = {\n            'ball_info': {\n                'position': [1.0909986, 1.8254881],\n                'velocity': [0., 0.]\n            },\n            'player_info': {\n                0: {\n                    'position': [-9.031397, -1.2],\n                    'rotation_y': 87.729774,\n                    'velocity': [0., 0.],\n                },\n                1: {\n                    'position': [-6.2403193,  1.2],\n                    'rotation_y': 85.95333,\n                    'velocity': [0., 0.],\n                },\n                2: {\n                    'position': [6.4539313, 1.2],\n                    'rotation_y': 277.19568,\n                    'velocity': [0., 0.],\n                },\n                3: {\n                    'position': [6.6643953, -1.2],\n                    'rotation_y': 270.04953,\n                    'velocity': [0., 0.],\n                }\n            }\n        }\n\n\n        VELOCITY_RANGE = 5\n        self.limits = {\n            'ball_info': {\n                'position': ([-10, 10], [-5, 5]),\n                'velocity': ([-VELOCITY_RANGE, VELOCITY_RANGE], [-VELOCITY_RANGE, VELOCITY_RANGE])\n            },\n            'player_info': {\n                0: {\n                    'position': ([-5, 18], [-5, 5]),\n                    'rotation_y': ([-80,270],),\n                    'velocity': ([-VELOCITY_RANGE, VELOCITY_RANGE], [-VELOCITY_RANGE, VELOCITY_RANGE]),\n                },\n                1: {\n                    'position': ([-5, 18], [-5, 5]),\n                    'rotation_y': ([-80,270],),\n                    'velocity': ([-VELOCITY_RANGE, VELOCITY_RANGE], [-VELOCITY_RANGE, VELOCITY_RANGE]),\n                },\n                2: {\n                    'position': ([-18, 5], [-5, 5]),\n                    'rotation_y':([-270,80],),\n                    'velocity': ([-VELOCITY_RANGE, VELOCITY_RANGE], [-VELOCITY_RANGE, VELOCITY_RANGE]),\n                },\n                3: {\n                    'position': ([-18, 5], [-5, 5]),\n                    'rotation_y': ([-270,80],),\n                    'velocity': ([-VELOCITY_RANGE, VELOCITY_RANGE], [-VELOCITY_RANGE, VELOCITY_RANGE]),\n                }\n            }\n        }\n\n    def reset(self):\n        obs = self.env.reset()\n        base = self.default_watch_position#np.random.choice([self.default_train_position, self.default_watch_position])\n        max_divergence = np.random.random()\n        if max_divergence < self.min_divergence:\n            state = base\n            max_divergence = 0\n        else:\n            state = copy.deepcopy(base)\n            for data_point, limits in self.limits['ball_info'].items():\n                if data_point not in self.randomize['ball']:\n                    continue\n                p1 = np.random.uniform(limits[0][0],limits[0][1])\n                if len(limits) == 2:\n                    p2 = np.random.uniform(limits[1][0],limits[1][1])\n                    state['ball_info'][data_point][0] += p1*max_divergence*self.k\n                    state['ball_info'][data_point][1] += p2*max_divergence*self.k\n                else:\n                    state['ball_info'][data_point] += p1\n            for agent_id in self.limits['player_info']:\n                for data_point, limits in self.limits['player_info'][agent_id].items():\n                    if data_point not in self.randomize['player']:\n                        continue\n                    p1 = np.random.uniform(limits[0][0],limits[0][1])\n                    if len(limits) == 2:\n                        p2 = np.random.uniform(limits[1][0],limits[1][1])\n                        state['player_info'][agent_id][data_point][0] += p1*max_divergence*self.k\n                        state['player_info'][agent_id][data_point][1] += p2*max_divergence*self.k\n                    else:\n                        state['player_info'][agent_id][data_point] += p1*max_divergence*self.k\n        #print(\"Randomizing ambient\", max_divergence, self.k)\n        #print(\"setted state\", state)\n        self.env_channel.set_parameters(\n            ball_state = state['ball_info'],\n            players_states = state['player_info']\n        )\n        return obs\n\nclass CurriculumWrapper(gym.core.Wrapper):\n    def __init__(self, env):\n        super(CurriculumWrapper, self).__init__(env)\n        self.env = env\n        self.current_env = env\n        self.cur_level = 0\n        self.number_of_tasks = 6\n\n    def reset(self):\n        return self.current_env.reset()\n    \n    def step(self, action):\n        return self.current_env.step(action)\n\n    #@override(TaskSettableEnv)\n    def sample_tasks(self, n_tasks):\n        \"\"\"Implement this to sample n random tasks.\"\"\"\n        return [random.randint(0, self.number_of_tasks) for _ in range(n_tasks)]\n    \n    #@override(TaskSettableEnv)\n    def set_task(self, task) -> None:\n        \"\"\"Sets the specified task to the current environment\n        Args:\n            task: task of the meta-learning environment\n        \"\"\"\n        if task == 0:\n            self.current_env = self.env\n        elif task == 1:\n            randomize = {'ball': [], 'player': ['rotation_y']}\n            self.current_env = RandomEnvWrapper(self.env, randomize=randomize)\n        elif task == 2:\n            randomize = {'ball': ['position'], 'player': ['rotation_y']}\n            self.current_env = RandomEnvWrapper(self.env, randomize=randomize)\n        elif task == 3:\n            randomize = {'ball': ['position'], 'player': ['rotation_y', 'velocity']}\n            self.current_env = RandomEnvWrapper(self.env, randomize=randomize)\n        elif task == 4:\n            randomize = {'ball': ['position', 'velocity'], 'player': ['rotation_y', 'velocity']}\n            self.current_env = RandomEnvWrapper(self.env, randomize=randomize)\n        elif task == 5:\n            randomize = {'ball': ['position', 'velocity'], 'player': ['rotation_y', 'velocity']}\n            self.current_env = RandomEnvWrapper(self.env, randomize=randomize, min_divergence=0.5, k=0.7)\n        else:\n            print(f\"Invalid Task number {task}, setting enviroment task 0\")\n            self.current_env = self.env\n            task = 0\n\n        self.cur_level = task\n\n    #@override(TaskSettableEnv)\n    def get_task(self):\n        \"\"\"Implement this to get the current task (curriculum level).\"\"\"\n        return self.cur_level\n\n    def next_task(self):\n        self.set_task((self.cur_level + 1) %self.number_of_tasks)","repo_name":"eduagarcia/teampequi-rl-ceia-2021","sub_path":"wrappers.py","file_name":"wrappers.py","file_ext":"py","file_size_in_byte":14234,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"4684825335","text":"import os, pyttsx3\r\n\r\nimport speech_recognition as sr\r\n\r\n\r\ndef takeCommands():\r\n    r = sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        print('I am ready Now for your Order!')\r\n        r.pause_threshold = 0.7\r\n        audio = r.listen(source)\r\n        try:\r\n            print(\"Loding . . .\")\r\n            query = r.recognize_google(audio)\r\n            print('the command is :', query)\r\n\r\n        except Exception as e:\r\n            print(e)\r\n            print('could you please repeat again ? ')\r\n            return \"None\"\r\n\r\n        import time\r\n        time.sleep(2)\r\n        return query\r\n\r\n\r\ndef machineSound(audio):\r\n    machine = pyttsx3.init()\r\n    machine.say(audio)\r\n    machine.runAndWait()\r\n    \r\n\r\nmachineSound(\"how are you mohamed, are you happy today\")\r\nwhile True:\r\n    command = takeCommands()\r\n    if \"yeah\" or \"yea\" or \"sure\" or \"yes\" or \"i hope today will be nice\" or \"do you think that\" or \" give me an advice\" in command:\r\n        machineSound(\"ok you should not sit on the pc too much\")\r\n        break\r\n        \r\nmachineSound('Should i turn off the pc ?')\r\n\r\nwhile True:\r\n    command = takeCommands()\r\n\r\n    if \"exit\" in command:\r\n        machineSound(\"sure bye\")\r\n        break\r\n\r\n    elif 'yes' in command:\r\n        machineSound(\"it will be shutdown Now \")\r\n        os.system(\"shutdown /s /t 30\")\r\n\r\n    elif \"no\" in command:\r\n        machineSound(\"Enjoy your rest time \")","repo_name":"Mohamed-0Sabry/AI-assistant","sub_path":"AI_Assistant.py","file_name":"AI_Assistant.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9757952875","text":"__author__ = \"Noupin, TensorFlow\"\r\n\r\n#Third Party Imports\r\nimport os\r\nimport tensorflow_datasets as tfds\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#First Party Import\r\nfrom customSchedule import CustomSchedule\r\nfrom tunable import Tunable\r\nfrom constants import Constants\r\nimport utilities\r\n\r\nclass Preprocessing():\r\n    def __init__(self):\r\n        #Loading the data\r\n        self.examples, self.metadata = tfds.load('ted_hrlr_translate/{langShort}_to_en'.format(langShort=Constants.languageMap[Tunable.tunableVars[\"language\"]]), \r\n                                                 with_info=True,\r\n                                                 as_supervised=True)\r\n        self.train_examples, self.val_examples = self.examples['train'], self.examples['validation']\r\n\r\n        #English\r\n        if not os.path.exists(Constants.tokenizerPath + \"english.subwords\"):\r\n            print(\"Creating English Subword Tokenizer.\")\r\n            self.tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus(\r\n                        (en.numpy() for pt, en in self.train_examples), target_vocab_size=2**13)\r\n            self.tokenizer_en.save_to_file(Constants.tokenizerPath + \"english\")\r\n            print(\"Finsihed Creating English Subword Tokenizer\")\r\n        else:\r\n            print(\"Loading English Subword Tokenizer.\")\r\n            self.tokenizer_en = tfds.features.text.SubwordTextEncoder.load_from_file(Constants.tokenizerPath + \"english\")\r\n            print(\"Finsihed Loading English Subword Tokenizer\")\r\n\r\n\r\n        #Portuguese\r\n        if not os.path.exists(Constants.tokenizerPath + \"{lang}.subwords\".format(lang=Tunable.tunableVars[\"language\"])):\r\n            print(\"Creating {lang} Subword Tokenizer.\".format(lang=Tunable.tunableVars[\"language\"].title()))\r\n            self.tokenizer_pt = tfds.features.text.SubwordTextEncoder.build_from_corpus(\r\n                        (pt.numpy() for pt, en in self.train_examples), target_vocab_size=2**13)\r\n            self.tokenizer_pt.save_to_file(Constants.tokenizerPath + Tunable.tunableVars[\"language\"])\r\n            print(\"Finsihed Creating {lang} Subword Tokenizer\".format(lang=Tunable.tunableVars[\"language\"].title()))\r\n        else:\r\n            print(\"Loading {lang} Subword Tokenizer.\".format(lang=Tunable.tunableVars[\"language\"].title()))\r\n            self.tokenizer_pt = tfds.features.text.SubwordTextEncoder.load_from_file(Constants.tokenizerPath + Tunable.tunableVars[\"language\"])\r\n            print(\"Finsihed Loading {lang} Subword Tokenizer\".format(lang=Tunable.tunableVars[\"language\"].title()))\r\n\r\n\r\n\r\n        self.train_dataset = self.train_examples.map(self.tf_encode)\r\n        self.train_dataset = self.train_dataset.filter(utilities.filter_max_length)\r\n        self.train_dataset = self.train_dataset.cache() #cache the dataset to memory to get a speedup while reading from it.\r\n        self.train_dataset = self.train_dataset.shuffle(Tunable.tunableVars[\"BUFFER_SIZE\"]).padded_batch(Tunable.tunableVars[\"BATCH_SIZE\"])\r\n        self.train_dataset = self.train_dataset.prefetch(tf.data.experimental.AUTOTUNE)\r\n\r\n\r\n        self.val_dataset = self.val_examples.map(self.tf_encode)\r\n        self.val_dataset = self.val_dataset.filter(utilities.filter_max_length).padded_batch(Tunable.tunableVars[\"BATCH_SIZE\"]) #Bringing the dataset to MAX_LENGTH\r\n\r\n        self.pt_batch, self.en_batch = next(iter(self.val_dataset))\r\n\r\n\r\n        self.input_vocab_size = self.tokenizer_pt.vocab_size + 2\r\n        self.target_vocab_size = self.tokenizer_en.vocab_size + 2\r\n\r\n        self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\r\n            from_logits=True, reduction='none')\r\n    \r\n    #Adding a start and end token to the input target\r\n    def encode(self, lang1, lang2):\r\n        lang1 = [self.tokenizer_pt.vocab_size] + self.tokenizer_pt.encode(\r\n            lang1.numpy()) + [self.tokenizer_pt.vocab_size+1]\r\n        \r\n        lang2 = [self.tokenizer_en.vocab_size] + self.tokenizer_en.encode(\r\n            lang2.numpy()) + [self.tokenizer_en.vocab_size+1]\r\n        \r\n        return lang1, lang2\r\n\r\n    def tf_encode(self, pt, en):\r\n        result_pt, result_en = tf.py_function(self.encode, [pt, en], [tf.int64, tf.int64])\r\n        result_pt.set_shape([None])\r\n        result_en.set_shape([None])\r\n\r\n        return result_pt, result_en\r\n\r\n    def loss_function(self, real, pred):\r\n        mask = tf.math.logical_not(tf.math.equal(real, 0))\r\n        loss_ = self.loss_object(real, pred)\r\n\r\n        mask = tf.cast(mask, dtype=loss_.dtype)\r\n        loss_ *= mask\r\n\r\n        return tf.reduce_sum(loss_)/tf.reduce_sum(mask)\r\n\r\n    def plot_attention_weights(self, attention, sentence, result, layer):\r\n        fig = plt.figure(figsize=(16, 8))\r\n\r\n        sentence = self.tokenizer_pt.encode(sentence)\r\n\r\n        attention = tf.squeeze(attention[layer], axis=0)\r\n\r\n        for head in range(attention.shape[0]):\r\n            ax = fig.add_subplot(2, 4, head+1)\r\n\r\n            ax.matshow(attention[head][:-1, :], cmap='viridis')\r\n\r\n            fontdict = {'fontsize': 10}\r\n\r\n            ax.set_xticks(range(len(sentence)+2))\r\n            ax.set_yticks(range(len(result)))\r\n\r\n            ax.set_ylim(len(result)-1.5, -0.5)\r\n                \r\n            ax.set_xticklabels(\r\n                ['<start>']+[self.tokenizer_pt.decode([i]) for i in sentence]+['<end>'], \r\n                fontdict=fontdict, rotation=90)\r\n\r\n            ax.set_yticklabels([self.tokenizer_en.decode([i]) for i in result \r\n                                if i < self.tokenizer_en.vocab_size], \r\n                                fontdict=fontdict)\r\n\r\n            ax.set_xlabel('Head {}'.format(head+1))\r\n\r\n        plt.tight_layout()\r\n        plt.show()","repo_name":"Noupin/GTLM","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":5743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17008716069","text":"'''\nRecieves an Excel file as input and outputs (prints) all the emails that is\nfinds in it.\n\nExample:\n> python emails.py contacts.xls > emails.txt\n'''\nimport sys\nimport occurrences as occ\n\npattern = r'([a-zA-Z0-9\\-\\_\\+]+\\@[a-zA-Z0-9\\-\\.]+\\.[a-zA-Z]+)'\n\n\nif __name__ == '__main__':\n    filename = sys.argv[1]\n\n    try:\n        data = occ.load_data(filename)\n    except occ.InvalidFile as ife:\n        print(ife)\n        exit(1)\n    except occ.InvalidFileType as ifte:\n        print(ifte)\n        exit(2)\n\n    emails = occ.get_occurrences(pattern, data)\n\n    for email in emails:\n        print(email)\n","repo_name":"diogoaguiar/comms-scripts","sub_path":"extract_contacts/emails.py","file_name":"emails.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"48333249473","text":"import time\n\nfrom pyrobot import Robot\n\n\ndef main():\n    target_joints = [[0.408, 0.721, -0.471, -1.4, 0.920], [-0.675, 0, 0.23, 1, -0.70]]\n\n    bot = Robot(\"locobot\")\n    bot.arm.go_home()\n\n    for joint in target_joints:\n        bot.arm.set_joint_positions(joint, plan=False)\n        time.sleep(1)\n\n    bot.arm.go_home()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"facebookresearch/pyrobot","sub_path":"examples/locobot/manipulation/joint_position_control.py","file_name":"joint_position_control.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":2174,"dataset":"github-code","pt":"77"}
+{"seq_id":"44000372325","text":"import mysql.connector\nfrom flask import render_template,g\nimport sys\nfrom app.database.db_config import db_config\n\n\nclass dbManager:\n\n    @staticmethod\n    def get_db():\n        #access to database\n        db = getattr(g, '_database', None)\n        if db is None:\n            db = g._database = dbManager.connect_to_database()\n        return db\n\n    @staticmethod\n    def teardown_db(exception):\n        #close the database\n        db = getattr(g, '_database', None)\n        if db is not None:\n            db.close()\n\n    @staticmethod\n    def connect_to_database():\n        #Connect database\n        return mysql.connector.connect(user=db_config['user'],\n                                       password=db_config['password'],\n                                       host=db_config['host'],\n                                       database=db_config['database'])\n\n    @staticmethod\n    def delete_all_data(table):\n        '''method delete all data in one table'''\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        try:\n            cursor.execute(\"truncate table \"+table)\n            cursor.execute(\"commit\")\n        except:\n            e = sys.exc_info()\n            db.rollback()\n            dbManager.teardown_db(e)\n            return render_template(\"error.html\", message=\"database error: \" + str(e))\n\n\n\n    @staticmethod\n    def updata_autoscaling_parameter(max_worker, min_worker, cooling_time, cpu_up_threshold, cpu_down_threshold, extend_ratio, shrink_ratio):\n        '''update autocaling table date'''\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        query = \"update autoscaling set max_worker= %s ,min_worker= %s,cooling_time= %s,cpu_up_threshold= %s,cpu_down_threshold= %s,extend_ratio= %s,shrink_ratio= %s where name = 'config'\"\n        cursor.execute(query, (max_worker, min_worker,cooling_time,cpu_up_threshold, cpu_down_threshold, extend_ratio, shrink_ratio))\n        cursor.execute(\"commit\")\n        # except:\n        #     e = sys.exc_info()\n        #     db.rollback()\n        #     dbManager.teardown_db(e)\n        #     return render_template(\"error.html\", message=\"database error: \" + str(e))\n\n\n    @staticmethod\n    def fetch_autoscaling_parameter():\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        try:\n            query = \"SELECT * FROM autoscaling \"\n            cursor.execute(query)\n            result = cursor.fetchall()\n            return result\n        except:\n            e = sys.exc_info()\n            db.rollback()\n            dbManager.teardown_db(e)\n            return render_template(\"error.html\", message=\"database error: \" + str(e))\n\n    @staticmethod\n    def checkuser(username):\n        '''method search data in SQL with target condition'''\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        try:\n            query = \"SELECT * FROM accounts WHERE username = %s\"\n            cursor.execute(query, (username,))\n            account = cursor.fetchone()\n            return account\n        except:\n            e = sys.exc_info()\n            db.rollback()\n            dbManager.teardown_db(e)\n            return render_template(\"error.html\", message=\"database error: \" + str(e))\n\n    @staticmethod\n    def write_admin():\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        username = \"admin\"\n        password_hash = \"pbkdf2:sha256:150000$NEDS8BFQ$7d7730a1563942bb85897c5dc5997a6d539b8cb9cfeaa2260d06f43f5f7cd7bd\"\n        email = \"ece1779group@gmail.com\"\n        admin_auth = 1\n        try:\n            cursor.execute(\"Insert into accounts (username, password_hash, email,admin_auth) \"\n                           \"values (%s, %s, %s, %s)\", (username, password_hash, email, admin_auth))\n            cursor.execute(\"commit\")\n        except:\n            e = sys.exc_info()\n            db.rollback()\n            dbManager.teardown_db(e)\n            return render_template(\"error.html\", message=\"database error: \" + str(e))\n\n    @staticmethod\n    def write_pool_size(pool_size):\n        db = dbManager.get_db()\n        cursor = db.cursor(dictionary=True)\n        try:\n            query = \"update autoscaling set max_worker= %s where name = 'config'\"\n            cursor.execute(query, (pool_size,))\n            cursor.execute(\"commit\")\n        except:\n            e = sys.exc_info()\n            db.rollback()\n            dbManager.teardown_db(e)\n            return render_template(\"error.html\", message=\"database error: \" + str(e))","repo_name":"collinwzw/ECE1779A2","sub_path":"app/database/dbManager.py","file_name":"dbManager.py","file_ext":"py","file_size_in_byte":4524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17808896799","text":"from copyreg import pickle\nfrom distutils.log import warn\nimport os\nimport pickle\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch\nfrom torchvision import transforms\nfrom dataset import *\nimport models\nfrom pytorch_lightning.loggers import WandbLogger, CSVLogger\nfrom utils import *\n\ndef main(args, mode):    \n    loggers = [\n        CSVLogger(save_dir=args[\"run_root_dir\"], \n                  name=None, version='csvs'),\n    ]\n    \n    if args['use_wandb'] and (mode == \"train\" or mode == \"train+evaluate+export\"):\n        print(f\"Using Weights&Biases under project {args['project_name']} \")\n        loggers.append(\n            WandbLogger(\n                name=args[\"run_name\"],\n                save_dir=args[\"run_root_dir\"],\n                project=args[\"project_name\"]\n            )\n        )\n        \n    callbacks = [\n        pl.callbacks.model_checkpoint.ModelCheckpoint(\n            dirpath=os.path.join(args[\"run_root_dir\"], \"checkpoints\"),\n            monitor='val_loss',\n            save_last=True,\n            save_top_k=1,\n            mode='min',\n            auto_insert_metric_name=True,\n            filename='checkpoint-{epoch}-{val_loss:.2f}'\n        )\n    ]\n    # data\n    data_loader = IrisDataModule(\n        root_dir=args[\"dataset_root\"],\n        batch_size=args[\"batch_size\"],\n        num_workers=args[\"num_workers\"],\n        num_in_channels=args[\"num_in_channels\"],\n        train_subset=args[\"dataset_subsets\"],\n        train_pseudolabels=args[\"train_pseudolabels\"] if \"train_pseudolabels\" in args else None,\n        auto_crop=args[\"auto_crop\"],\n        unwrap=args[\"unwrap\"],\n        shuffle=args[\"shuffle\"],\n        train_transform=train_transform(**args[\"train_transform\"]),\n        val_transform=val_transform(**args[\"val_transform\"]),\n        test_transform=test_transform(**args[\"test_transform\"]),\n        predict_transform=predict_transform(**args[\"predict_transform\"]),\n    )\n    \n    if \"resume_checkpoint\" in args:\n        print(f\"Resuming from checkpoint {args['resume_checkpoint']} \")\n        model = models.__dict__[args[\"model\"]].load_from_checkpoint(\n            args[\"resume_checkpoint\"], hparams_file=args_file[\"resume_hparams\"]\n        )\n    else:\n        print(f\"Creating new model\")\n        model = models.__dict__[args[\"model\"]](**args[\"model_args\"])\n\n    # training\n    trainer = pl.Trainer(\n        max_epochs=args[\"max_epochs\"],\n        accelerator=args[\"accelerator\"],\n        devices=args[\"devices\"],\n        num_nodes=args[\"num_nodes\"],\n        default_root_dir=args[\"run_root_dir\"],\n        log_every_n_steps=args[\"log_steps\"],\n        accumulate_grad_batches=args[\"grad_batches\"],\n        logger=loggers,\n        callbacks=callbacks,\n        gradient_clip_val=5, gradient_clip_algorithm=\"norm\",\n    )\n    if mode == \"train\" or mode == \"train+evaluate+export\":\n        print(f\"Starting trainning\")\n        trainer.fit(\n            model,\n            data_loader,\n            ckpt_path=args[\"resume_checkpoint\"] if \"resume_checkpoint\" in args else None\n        )\n    if mode == \"export\" or mode == \"train+evaluate+export\":\n        print(\"Exporting checkpoints backbone\")\n        encoder_state_dict = model.encoder.state_dict()\n        torch.save(encoder_state_dict, os.path.join(\n            args[\"run_root_dir\"],\n            'encoder-{}.pickle'.format(args[\"run_name\"])\n        ))\n    if mode == \"evaluate\" or mode == \"train+evaluate+export\":\n        print(\"Evaluating model\")\n        for predict_dataset in data_loader.iris_predict_list:\n            print(f\"Predict for dataset: {predict_dataset}\")\n            data_loader.iris_predict_select(predict_dataset)\n            data = trainer.predict(\n                model,\n                data_loader,\n                return_predictions=True\n            )\n            #unpack results\n            results = unpack_results(data)\n            #If evaluated output has label field\n            if \"label\" in results:\n                save_labeled_results(\n                    results, args[\"run_root_dir\"], \n                    args[\"run_name\"], predict_dataset\n                )            \n            #If evaluated output is unlabeled\n            else:\n                save_unlabeled_results(\n                    results, args[\"run_root_dir\"], \n                    args[\"run_name\"], predict_dataset\n                )\n    print(\"Finished running\")\n\ndef get_args_parser(add_help=True):\n    import argparse\n    parser = argparse.ArgumentParser(\n        description=\"Recognition Training script\", \n        add_help=add_help\n    )\n    parser.add_argument(\n        '--args-path',\n        type=str,\n        default='args.yaml',\n        help=f\"Path to argument file for training (default: args.yaml)\"\n    )\n    parser.add_argument(\n        '--mode',\n        choices=[\"train\", \"evaluate\", \"export\", \"train+evaluate+export\"],\n        type=str,\n        default=\"train+evaluate+export\",\n        help=f\"Mode to train or evaluate (default: train+evaluate+export)\"\n    )\n    parser.add_argument(\n        '--resume-dir',\n        type=str,\n        help=f\"Path to resume directory\"\n    )\n    parser.add_argument(\n        '--resume-checkpoint',\n        default='last.ckpt',\n        type=str,\n        help=f\"Resume checkpoint (default: last.ckpt)\"\n    )\n    return parser\n\n\nif __name__ == \"__main__\":\n    args_program = get_args_parser().parse_args()\n    import yaml\n    import time\n\n    if args_program.resume_dir != None:\n        args_program.args_path = os.path.join(\n            args_program.resume_dir, 'args.yaml'\n        )\n\n        with open(args_program.args_path, 'r') as f:\n            args_file = yaml.load(f,yaml.FullLoader)\n        \n        args_file['resume_dir'] = args_program.resume_dir\n        args_file['resume_checkpoint'] = os.path.join(\n            args_file['resume_dir'], \"checkpoints\",\n            args_program.resume_checkpoint\n        )\n        args_file[\"resume_hparams\"] = os.path.join(\n            args_file['resume_dir'], \"csvs\",\n            \"hparams.yaml\"\n        )\n        if args_file[\"run_root_dir\"] != args_file['resume_dir']:\n            warn('Resume directory is different as in argsfile')\n            args_file[\"run_root_dir\"] = args_program.resume_dir\n    else:\n        with open(args_program.args_path, 'r') as f:\n            args_file = yaml.load(f, yaml.FullLoader)\n        \n        args_file[\"run_name\"] = args_file['model']\n        if 'backbone' in args_file['model_args']:\n            args_file[\"run_name\"] += '-' + args_file['model_args']['backbone']\n        if 'metric' in args_file['model_args']:\n            args_file[\"run_name\"] += '-' + args_file['model_args']['metric']\n        args_file[\"run_name\"] += '-' + name_gen(time.time_ns()//1_000_000_000)\n\n        args_file[\"run_root_dir\"] = os.path.join(\n            args_file[\"root_dir\"], args_file[\"run_name\"]\n        )\n\n        if not os.path.exists(args_file[\"run_root_dir\"]):\n            os.makedirs(args_file[\"run_root_dir\"])\n\n        with open(os.path.join(args_file[\"run_root_dir\"],\"args.yaml\"), 'w') as f:\n            yaml.dump(args_file, f)\n\n    main(args=args_file, mode=args_program.mode)","repo_name":"nosebastian/DP","sub_path":"lightning/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21984815141","text":"\"\"\"\n找零\n面值1元、4元、16元、64元共计4种硬币，以及面值1024元的纸币。现在小Y使用1024元的纸币购买了一件价值为N(0<N≤1024)的商品，请问最少他会收到多少硬币\n时间限制：C/C++ 1秒，其他语言2秒\n空间限制：C/C++ 256M，其他语言512M\n示例1\n输入例子：\n200\n输出例子：\n17\n例子说明：\n12个64元硬币，3个16元硬币，2个4元硬币\n\n\"\"\"\n\n\ndef func(N):\n    li = [64, 16, 4, 1]\n    li.sort(reverse=True)\n    print(li)\n    m = [0 for _ in range(len(li))]\n    for index, value in enumerate(li):\n        m[index] = N // value\n        N = N % value\n    print(m, N)\n    ret = 0\n    for val in m:\n        ret += val\n    print(ret)\n\n\nif __name__ == '__main__':\n    func(1024 - 200)\n","repo_name":"HkwJsxl/PythonFullStack","sub_path":"Interview/B站试题/B站-找零问题.py","file_name":"B站-找零问题.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"5881399255","text":"from telegram.ext import Updater, CommandHandler, CallbackContext\nfrom config.auth import token\n\nimport logging\n\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s \\\n    - %(message)s', level=logging.INFO)\nlogger = logging.getLogger('romerovargasbot')\n\n\ndef start(update: Updater, context: CallbackContext):\n    logger.info('He recibido un comando start')\n    context.bot.send_message(\n        chat_id=update.message.chat_id,\n        text=\"Soy de Jerez de la Frontera, cuna del fino, de los caballos \"\n             \"cartujanos y Capital Mundial del Motociclismo\")\n\n\nif __name__ == '__main__':\n\n    updater = Updater(token=token, use_context=True)\n    dispatcher = updater.dispatcher\n\n    dispatcher.add_handler(CommandHandler('start', start))\n\n    updater.start_polling()\n    updater.idle()\n","repo_name":"mentecatoDev/bots_de_telegram_en_python","sub_path":"src/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44617380426","text":"import pickle\nfrom scipy.optimize import minimize\nimport numpy as np\nimport scipy.stats\nfrom scipy.special import factorial\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\nfrom tqdm import tqdm\nfrom utils import get_random_free_params\nfrom fitting_functions import *\n\n\n# Read in data                 #\npkl_path = '../S2_data/data.pkl'\nwith open(pkl_path, 'rb') as f:\n    data = pickle.load(f)\n\n# set the model\nmodel= 'JPM' #'JPM'\nimplementation='full'\npreprocess = True\nprint('MODEL CHOSE: {}\\t{}\\tpreprocess:{}'.format(model,implementation,preprocess))\n\n\n# define the free parameters\ninit_para = get_random_free_params(model=model,implementation=implementation)\n\nN_trails = 10\nnum_tester = 16\nif model == 'JPM' and implementation == 'full':\n    num_params = 14\nelif model == 'JPM' and implementation == 'reduced':\n    num_params = 13\nelif model == 'JPM' and implementation == 'mle':\n    num_params = 12\nelif model == 'BCI' and implementation == 'full':\n    num_params = 14\nelse:\n    raise Exception('model not implemented')\n\n\nparams_stored = np.zeros((N_trails,num_tester,14))\nneg_log_record = np.zeros((N_trails,num_tester))\n\nnan_check = [False] * num_tester\n\nif model == 'JPM':\n    neg_log_function = neg_log_guassian\nelif model == 'BCI':\n    neg_log_function = neg_log_bci\nelse:\n    raise Exception('Not Implemented Error')\n\n\nfor j in tqdm(range(num_tester)):\n    for i in tqdm(range(N_trails)):\n        x0 = get_random_free_params(model=model,implementation=implementation)\n        res = minimize(neg_log_function, x0, args=(j, data, model, implementation,preprocess), method='BFGS', tol=1e-4)  # Nelder-Mead,BFGS,L-BFGS-B\n        params_stored[i,j,:] = parameter_prepocess(res.x,model=model,implementation=implementation,preprocess=preprocess)\n\n        # record the neg-log value (for choosing the lowest one)\n        neg_log = neg_log_function(res.x, j, data, model, implementation,preprocess)\n        # neg_log_2 = res.fun\n        neg_log_record[i, j] = neg_log\n\n        # if nan_check[j] == False and np.isnan(neg_log) == False:\n        #     nan_check[j] = True\n        #     i = 0\n\n        print('neg_log for {}th trail & {} tester: {}'.format(i,j,neg_log))\n        print(params_stored[i,j,:])\n\n\n# neg_log_record_n = np.sum(neg_log_record,axis=1)\n# print(len(neg_log_record_n))\n\nneg_log_record = np.array(neg_log_record)\n\nmin_index = np.nanargmin(neg_log_record,axis=0)\nprint('min neg_log :{}\\ncorresponding index: {}'.format(np.nanmin(neg_log_record,axis=0),min_index))\n\nbest_params = []\nfor i in range(num_tester):\n    best_params.append(params_stored[min_index[i],i,:])\n\nbest_params = np.array(best_params)\nprint('best_params:{}'.format(best_params))\n\n# store the best\nfitted_param_path = '../fitted_params/fitted_params_jpm_full_4.npy'\nnp.save(fitted_param_path, best_params)\n\n# store the whole params\nfitted_param_path = '../fitted_params/fitted_params_jpm_full_4_all.npy'\nnp.save(fitted_param_path, params_stored)\n\n# neg_log = []  # use to record the neg-log-multi-nomial likelihood\n# def minimize_with_params()\n# tester_index = 11\n# x0 = np.array(init_para)\n# res = minimize(neg_log_guassian, x0, args=(tester_index,data,model,implementation), method='Nelder-Mead', tol=1e-6)  # 'Nelder-Mead' , 'BFGS'\n\n'''\nif model == 'JPM' and implementation == 'full':\n    [sigma_0_s,sigma_0_a,mu_vg, mu_vb, mu_ag, mu_ab,sigma_vh, sigma_vm, sigma_vl, sigma_ah, sigma_am, sigma_al,interval_gd,interval_db] = res.x\nelif model == 'JPM' and implementation == 'reduced':\n    [sigma_0, mu_vg, mu_vb, mu_ag, mu_ab, sigma_vh, sigma_vm, sigma_vl, sigma_ah, sigma_am, sigma_al,\n     interval_gd, interval_db] = res.x\n    sigma_0_s,sigma_0_a = 0,sigma_0\nelse:\n    raise Exception('model not implemented')\n\nprint('sigma_0:{}\\t{}'.format(sigma_0_s,sigma_0_a))\nprint('mu_vg:{},mu_vb:{},mu_ag:{}, mu_ab:{}'.format(mu_vg, mu_vb, mu_ag, mu_ab))\nprint('sigma_vh:{}, sigma_vm:{}, sigma_vl:{}, sigma_ah:{}, sigma_am:{}, sigma_al:{}'.format(sigma_vh, sigma_vm, sigma_vl, sigma_ah, sigma_am, sigma_al))\nprint('interval_gd:{},interval_db:{}'.format(interval_gd,interval_db))\n'''\n\n\n# print(neg_log_guassian(res.x,tester_index,data,model,implementation))\n\n","repo_name":"nina-weng/Bayesian-Models-of-Audiovisual-Integration-in-Speech-Perception","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39484844242","text":"from django import template\nfrom django.utils.safestring import mark_safe\nregister = template.Library()\n\n\n\ndef recursive_related_objs_lookup(objs):\n    ul_ele = \"<ul>\"\n    for obj in objs:\n        li_ele = '''<li> %s: %s </li>'''% (obj._meta.verbose_name, obj.__str__().strip(\"<>\"))\n        ul_ele += li_ele\n\n        for m2m_field in obj._meta.local_many_to_many:\n            sub_ul_ele = \"<ul>\"\n            m2m_field_obj = getattr(obj,m2m_field.name)\n            for o in m2m_field_obj.select_related():\n                li_ele = '''<li> %s: %s </li>''' % (m2m_field.verbose_name, o.__str__().strip(\"<>\"))\n                sub_ul_ele +=li_ele\n\n            sub_ul_ele += \"</ul>\"\n            ul_ele += sub_ul_ele\n\n        for related_obj in obj._meta.related_objects:\n            if 'ManyToManyRel' in related_obj.__repr__():\n\n                if hasattr(obj, related_obj.get_accessor_name()):\n                    accessor_obj = getattr(obj, related_obj.get_accessor_name())\n                    print(\"-------ManyToManyRel\",accessor_obj,related_obj.get_accessor_name())\n                    if hasattr(accessor_obj, 'select_related'):\n                        target_objs = accessor_obj.select_related()\n                        sub_ul_ele = \"<ul style='color:red'>\"\n                        for o in target_objs:\n                            li_ele = '''<li> %s: %s </li>''' % (o._meta.verbose_name, o.__str__().strip(\"<>\"))\n                            sub_ul_ele += li_ele\n                        sub_ul_ele += \"</ul>\"\n                        ul_ele += sub_ul_ele\n\n            elif hasattr(obj, related_obj.get_accessor_name()):\n                accessor_obj = getattr(obj, related_obj.get_accessor_name())\n                if hasattr(accessor_obj,'select_related'):\n                    target_objs = accessor_obj.select_related()\n\n                else:\n                    print(\"one to one i guess:\", accessor_obj)\n                    target_objs = accessor_obj\n\n                if len(target_objs) >0:\n                    nodes = recursive_related_objs_lookup(target_objs)\n                    ul_ele += nodes\n    ul_ele += \"</ul>\"\n    return ul_ele\n\n\n@register.simple_tag\ndef display_obj_related(obj):\n    objs = [obj,]\n    if obj:\n        model_class = obj._meta.model\n        mode_name = obj._meta.model_name\n        return mark_safe(recursive_related_objs_lookup(objs))\n\n\n\n","repo_name":"baiyun66/facerecognition","sub_path":"crm/templatetags/crm_tags.py","file_name":"crm_tags.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"14774488030","text":"\"\"\"\nLeetcode: https://leetcode.com/problems/binary-search/ (similar) - not submitted\nTC - O(NlogN), SC - O(1)\nChallenge - assigning l, r and midpoints could get sensitive and program could go in loop.\n\"\"\"\n\n\n# Python code to implement iterative Binary\n# Search. \n\n# It returns location of x in given array arr  \n# if present, else returns -1 \ndef binarySearch(arr, l, r, x):\n    while l <= r:\n        mid = (l + r) // 2\n        if arr[mid] == x:\n            return mid\n        elif arr[mid] > x:\n            r = mid - 1\n        else:\n            l = mid + 1\n\n    return -1\n\n\n# Test array \narr = [2, 3, 4, 10, 40]\nx = 10\n\n# Function call \nresult = binarySearch(arr, 0, len(arr) - 1, x)\n\nif result != -1:\n    print(\"Element is present at index % d\" % result)\nelse:\n    print(\"Element is not present in array\")\n","repo_name":"hlalwani-max/PreCourse-2","sub_path":"Exercise_1.py","file_name":"Exercise_1.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"69961204408","text":"# MNIST Loader Class\n# Input: path t ofolder containing MNIST train and test files\n# Output: Numpy arrays containing: train images, train labels, test images and test labels\n\nclass MnistLoader(object):\n    def __init__(self, input_path):\n        self.training_images_path = join(input_path, 'train-images.idx3-ubyte')\n        self.training_labels_path = join(input_path, 'train-labels.idx1-ubyte')\n        self.test_images_path     = join(input_path, 'test-images.idx3-ubyte')\n        self.test_labels_path     = join(input_path, 'test-labels.idx1-ubyte')\n    \n    def read_file(self, images_path, labels_path):        \n        images = []\n        labels = []   \n        \n        with open(images_path, 'rb') as file:\n            magic, size, rows, cols = struct.unpack(\">IIII\", file.read(16))\n            image_data = array(\"B\", file.read())\n        with open(labels_path, 'rb') as file:\n            magic, size = struct.unpack(\">II\", file.read(8))\n            labels = array(\"B\", file.read())           \n        \n        for i in range(size):\n            images.append([0] * rows * cols)\n        for i in range(size):\n            img = np.array(image_data[i * rows * cols:(i + 1) * rows * cols])\n            img = img.reshape(28, 28)\n            images[i][:] = img            \n        \n        return images, labels\n            \n    def load_data(self):\n        im_train, la_train = self.read_file(self.training_images_path, self.training_labels_path)\n        im_test, la_test = self.read_file(self.test_images_path, self.test_labels_path)\n        return (im_train, la_train),(im_test, la_test)  \n","repo_name":"inverb/Generative-Flow-Network-project","sub_path":"src/nice/data_load.py","file_name":"data_load.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17036573707","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.contrib.keras.python.keras import backend as K\nimport Model_linker as op_linker\nfrom flip_gradient import flip_gradient\n\n\nshape_to_return=None\nd_W_fc0=None\nd_b_fc0=None\ndef input_placeholder(image_size, image_channel, label_cnt):\n    with tf.name_scope('inputlayer'):\n        inputs = tf.placeholder(\"float\", [None, image_size, image_size, image_size, image_channel], 'inputs')\n        labels = tf.placeholder(\"float\", [None, label_cnt], 'labels')\n        dropout_keep_prob = tf.placeholder(\"float\",None, name='keep_prob')\n        learning_rate = tf.placeholder(\"float\", None, name='learning_rate')\n        domain_label = tf.placeholder(\"float\", [None,2], name='domain')\n        flip_gra=tf.placeholder(tf.float32, [])\n\n    return inputs, labels, dropout_keep_prob, learning_rate,domain_label,flip_gra\n\ndef train_inputs(image_size, image_channel, label_cnt) :\n    with tf.name_scope('coninputlayer'):\n        inputs = tf.placeholder(\"float\", [None, image_size, image_size, image_size, image_channel], 'input')\n        labels = tf.placeholder(\"float\", [None, label_cnt], 'label')\n        dropout_keep_prob = tf.placeholder(\"float\", None, name='keep_prob')\n        learning_rate = tf.placeholder(\"float\", None, name='learning_rate')\n        domain_label = tf.placeholder(\"float\", [None, 2], name='domain')\n        flip_gra = tf.placeholder(tf.float32, [], name='flip')\n\n    return inputs, labels, dropout_keep_prob, learning_rate, domain_label, flip_gra\n\n\n\n\ndef inference(inputs, dropout_keep_prob, label_cnt):\n    # todo: change lrn parameters\n    with tf.variable_scope(\"convolution\"):\n            with tf.variable_scope('conv1layer'):\n                conv1 = op_linker.conv(inputs, 7, 96, 3)   # (input data, kernel size, #output channels, stride_size)\n                #conv1 = op.lrn(conv1)\n                conv1 = tf.nn.max_pool3d(conv1, ksize=[1, 2, 2, 2, 1], strides=[1, 1, 1, 1, 1], padding='VALID')\n\n            # conv layer 2\n            with tf.variable_scope('conv2layer'):\n                conv2 = op_linker.conv(conv1, 5, 256, 1, 0.1)\n                #conv2 = op.lrn(conv2)\n                conv2 = tf.nn.max_pool3d(conv2, ksize=[1, 2, 2, 2, 1], strides=[1, 1, 1, 1, 1], padding='VALID')\n\n            # conv layer 3\n            with tf.variable_scope('conv3layer'):\n                conv3 = op_linker.conv(conv2, 3, 384, 1, 0.1)\n                #no bias\n\n            # conv layer 4\n            with tf.variable_scope('conv4layer'):\n                conv4 = op_linker.conv(conv3, 3, 384, 1, 0.1)\n\n            # conv layer 5\n            with tf.variable_scope('conv5layer'):\n                conv5 = op_linker.conv(conv4, 3, 256, 1, 0.1)\n                conv5 = tf.nn.max_pool3d(conv5, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1], padding='SAME')\n\n            # fc layer 1\n            with tf.variable_scope('fc1layer'):\n                global  shape_to_return\n                global  d_b_fc0\n                global  d_W_fc0\n                fc1,shape_to_return,d_W_fc0,d_b_fc0 = op_linker.fc(conv5, 4096, 0.1, use_weight=True)\n                fc1 = tf.nn.dropout(fc1, dropout_keep_prob)\n                #bias changed\n\n            # fc layer 2\n            with tf.variable_scope('fc2layer'):\n                fc2 = op_linker.fc(fc1, 4096, 0.1)\n                fc2 = tf.nn.dropout(fc2, dropout_keep_prob)\n                #bias changed\n\n            # fc layer 3 - output\n            with tf.variable_scope('fc3layer'):\n                print(\" \",end=\"\\n\")\n                print(\"Graph Built\")\n                final_layer=op_linker.fc(fc2, label_cnt, 0.1, activation_func=tf.nn.softmax)\n                return final_layer\n                #bias changed\n\n\n\ndef autoencoder(inputs,batch_size):\n\n  with tf.variable_scope('autoencoder') as scope:\n\n     #scope.reuse_variables()\n        # encoder\n     with tf.variable_scope('conv1layer'):\n        #print(\"Main\", inputs.shape)\n        net = op_linker.conv(inputs, 7, 96, 3)\n\n        net = tf.nn.max_pool3d(net, ksize=[1, 2, 2, 2, 1], strides=[1, 1, 1, 1, 1], padding='VALID')\n        #print(\"shape 1\",net.shape)\n\n     with tf.variable_scope('conv2layer'):\n        net = op_linker.conv(net, 5, 256, 1)\n        net = tf.nn.max_pool3d(net, ksize=[1, 2, 2, 2, 1], strides=[1, 1, 1, 1, 1], padding='VALID')\n        #print(\"shape 2\", net.shape)\n\n     with tf.variable_scope('conv3layer'):\n        net = op_linker.conv(net, 3, 384, 1)\n        #print(\"shape 3\", net.shape)\n\n     with tf.variable_scope('conv4layer'):\n        net = op_linker.conv(net, 3, 384, 1)\n        #print(\"shape 4\", net.shape)\n\n     with tf.variable_scope('conv5layer'):\n        net = op_linker.conv(net, 3, 256, 1)\n\n        #net = tf.nn.max_pool3d(net, ksize=[1, 3, 3, 3, 1], strides=[1, 2, 2, 2, 1], padding='SAME')\n        #print(\"shape 5\", net.shape)\n\n\n\n        # decoder\n     with tf.variable_scope('decon1layer'):\n        net = op_linker.deconv(net, 3, 384, 1, batch_size)\n\n\n     with tf.variable_scope('decon2layer'):\n        net = op_linker.deconv(net, 3, 384, 1, batch_size)\n\n     with tf.variable_scope('decon3layer'):\n\n        net = op_linker.deconv(net, 2, 256, 1, batch_size, conv_padding='VALID')  # for max pooling\n        net = op_linker.deconv(net, 5, 256, 1, batch_size)\n\n\n     with tf.variable_scope('decon4layer'):\n        net = op_linker.deconv(net, 2, 96, 1, batch_size, conv_padding='VALID') #for max pooling\n        net = op_linker.deconv(net, 7, 96, 3, batch_size)\n\n       #net = tf.nn.max_pool3d(net, ksize=[1, 2, 2, 2, 1], strides=[1, 1, 1, 1, 1], padding='VALID')\n\n     with tf.variable_scope('decon5layer'):\n        net = op_linker.deconv(net, 1, 1, 1, batch_size)\n        # activation_fn = tf.nn.tanh\n        #print(\"check \",net.shape)\n        return net\n\n\n\ndef domain_parameters(flip_value):\n\n    with tf.variable_scope('domain_predictor'):\n        # Flip the gradient when backpropagating through this operation\n        global  shape_to_return\n        global  d_W_fc0\n        global  d_b_fc0\n        l = flip_value\n        feature = shape_to_return\n        feat = feature   #flip_gradient(feature, l)\n        d_h_fc0 = tf.nn.relu(tf.matmul(feat, d_W_fc0) + d_b_fc0)\n\n        d_W_fc1 = weight_variable([4096, 2])\n        d_b_fc1 = bias_variable([2])\n        final_la =  tf.matmul(d_h_fc0, d_W_fc1) + d_b_fc1\n        domain_pred = tf.nn.softmax(final_la)\n        return domain_pred\n\n\n\ndef weight_variable(shape):\n    initial = tf.truncated_normal(shape, stddev=0.1)\n    return tf.Variable(initial)\n\n\ndef bias_variable(shape):\n    initial = tf.constant(0.1, shape=shape)\n    return tf.Variable(initial)\n\n\ndef accuracy(logits, labels):\n    # accuracy\n    with tf.name_scope('accuracy'):\n        accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)), tf.float32))\n        tf.summary.scalar('accuracy', accuracy)\n    return accuracy\n\n\ndef domain_accuracy(logits, labels):\n    # accuracy\n    with tf.name_scope('domain_accuracy'):\n        accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)), tf.float32))\n        tf.summary.scalar('domain_accuracy', accuracy)\n    return accuracy\n\n\ndef loss(logits, labels):\n    with tf.name_scope('loss'):\n        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=labels))\n        tf.summary.scalar('loss', loss)\n    return loss\n\n\n\ndef loss_autoencoder(d_inputs, ae_output):\n    with tf.name_scope('autoencoder_loss'):\n        loss = tf.reduce_mean(tf.square(ae_output- d_inputs))\n        tf.summary.scalar('autoencoder_loss', loss)\n    return loss\n\n\n\n\ndef domain_loss(logits, labels):\n    with tf.name_scope('domain_loss'):\n        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=labels))\n        tf.summary.scalar('domain_loss', loss)\n    return loss\n\n\ndef train_rms_prop(loss, learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp'):\n    return tf.train.RMSPropOptimizer(learning_rate, decay, momentum, epsilon, use_locking, name).minimize(loss)\n\n\n\n\ndef new_encoder(inputs_,padding=\"SAME\",stride=1):\n    conv1 = tf.layers.conv3d(inputs=inputs_, filters=16, kernel_size=(3, 3, 3), padding=padding, strides=stride,activation=tf.nn.relu)\n    maxpool1 = tf.layers.max_pooling3d(conv1, pool_size=(2, 2, 2), strides=(2, 2, 2), padding=padding)\n    conv2 = tf.layers.conv3d(inputs=maxpool1, filters=32, kernel_size=(3, 3, 3), padding=padding, strides=stride,\n                             activation=tf.nn.relu)\n    maxpool2 = tf.layers.max_pooling3d(conv2, pool_size=(3, 3, 3), strides=(3, 3, 3), padding=padding)\n    conv3 = tf.layers.conv3d(inputs=maxpool2, filters=96, kernel_size=(2, 2, 2), padding=padding, strides=stride,\n                             activation=tf.nn.relu)\n    maxpool3 = tf.layers.max_pooling3d(conv3, pool_size=(2, 2, 2), strides=(2, 2, 2), padding=padding)\n    # latent internal representation\n\n    # decoder\n    unpool1 = K.resize_volumes(maxpool3, 2, 2, 2, \"channels_last\")\n    deconv1 = tf.layers.conv3d_transpose(inputs=unpool1, filters=96, kernel_size=(2, 2, 2), padding=padding,\n                                         strides=stride, activation=tf.nn.relu)\n    unpool2 = K.resize_volumes(deconv1, 3, 3, 3, \"channels_last\")\n    deconv2 = tf.layers.conv3d_transpose(inputs=unpool2, filters=32, kernel_size=(3, 3, 3), padding=padding,\n                                         strides=stride, activation=tf.nn.relu)\n    unpool3 = K.resize_volumes(deconv2, 2, 2, 2, \"channels_last\")\n    deconv3 = tf.layers.conv3d_transpose(inputs=unpool3, filters=16, kernel_size=(3, 3, 3), padding=padding,\n                                         strides=stride, activation=tf.nn.relu)\n\n    output = tf.layers.dense(inputs=deconv3, units=1)\n    #print(\"main shape \",output.shape)\n    output = tf.reshape(output, (10,10,10,1))\n    return  output","repo_name":"mvisionai/ADNI_MODEL","sub_path":"3D_ARCH/Alex3D.py","file_name":"Alex3D.py","file_ext":"py","file_size_in_byte":9952,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"25028353774","text":"import string\r\n\r\n\r\nclass Establishment:\r\n    def __init__(self, id: int, district: string, county: string,\r\n                 parish: string, address: string, latitude: float,\r\n                 longitude: float, inspec_utility: float, inspec_duration: int, opening_hours: list):\r\n        self.id = id\r\n        self.district = district\r\n        self.county = county\r\n        self.parish = parish\r\n        self.address = address\r\n        self.latitude = latitude\r\n        self.longitude = longitude\r\n        self.inspec_duration = inspec_duration * 60 #transform to seconds\r\n        self.inspec_utility = inspec_utility\r\n        self.opening_hours = opening_hours\r\n        if id == 0:\r\n            self.visited = True\r\n        else:\r\n            self.visited = False\r\n\r\n    #inspec duration + travel time + getWaitingTime()\r\n","repo_name":"JozeRamos/AI_Project","sub_path":"establishment.py","file_name":"establishment.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10230515820","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn import BCEWithLogitsLoss\nfrom entity_align.utils.Util import row_wise_dot\n\n#This model corresponds to AlignDot in our paper\n#First, strings converted to list of character embeddings\n#Then, lstm runs over character embeddings\n#Finally, lstm embeddings at last time stamp dot producted together and judgement made off dot product\nclass AlignDot(torch.nn.Module):\n    def __init__(self,config,vocab):\n        super(AlignDot, self).__init__()\n        self.config = config\n        self.vocab = vocab\n\n        # Character embeddings\n        self.embedding = nn.Embedding(vocab.size+1, config.embedding_dim, padding_idx=0)\n\n        # Sequence encoder of strings (LSTM)\n        self.rnn = nn.LSTM(config.embedding_dim, config.rnn_hidden_size, 1, bidirectional = config.bidirectional)\n\n        self.loss = BCEWithLogitsLoss()\n        #self.loss = nn.MarginRankingLoss(margin=1) for margin loss\n        if self.config.bidirectional == True:\n            self.num_directions = 2\n        else:\n            self.num_directions = 1\n        # Variables for initial states of LSTM (these are different for train and dev because dev might be of different batch sizes)\n        self.h0 = Variable(torch.zeros(self.num_directions, config.batch_size, config.rnn_hidden_size).cuda(), requires_grad=False)\n        self.c0 = Variable(torch.zeros(self.num_directions, config.batch_size, config.rnn_hidden_size).cuda(), requires_grad=False)\n        self.h0_dev = Variable(torch.zeros(self.num_directions, config.dev_batch_size, config.rnn_hidden_size).cuda(), requires_grad=False)\n        self.c0_dev = Variable(torch.zeros(self.num_directions, config.dev_batch_size, config.rnn_hidden_size).cuda(), requires_grad=False)\n        self.ones = Variable(torch.ones(config.batch_size,1).cuda())\n\n    def print_loss(self,source,pos,neg, source_len,pos_len,neg_len):\n        source_embed = self.embed(source,source_len)\n        pos_embed = self.embed(pos,pos_len)\n        neg_embed = self.embed(neg,neg_len)\n        print(row_wise_dot(source_embed , pos_embed)- row_wise_dot(source_embed , neg_embed ))\n\n\n    def compute_loss(self,source,pos,neg, source_len,pos_len,neg_len):\n        \"\"\" Compute the loss (BPR) for a batch of examples\n        :param source: Entity mentions\n        :param pos: True aliases of the Mentions\n        :param neg: False aliases of the Mentions\n        :param source_len: lengths of mentions\n        :param pos_len: lengths of positives\n        :param neg_len: lengths of negatives\n        :return:\n        \"\"\"\n        source_embed = self.embed(source,source_len)\n        pos_embed = self.embed(pos,pos_len)\n        neg_embed = self.embed(neg,neg_len)\n        loss = self.loss(\n            row_wise_dot(source_embed , pos_embed )\n            - row_wise_dot(source_embed , neg_embed ),\n            self.ones)\n        #For margin loss:\n        #loss = self.loss(\n        #    row_wise_dot(source_embed, pos_embed), row_wise_dot(source_embed, neg_embed),\n        #    self.ones\n        #)\n        return loss\n\n    def score_pair(self,source, target, source_len, target_len):\n        \"\"\"\n\n        :param source: Batchsize by Max_String_Length\n        :param target: Batchsize by Max_String_Length\n        :return: Batchsize by 1\n        \"\"\"\n        source_embed = self.embed_dev(source, source_len)\n        target_embed = self.embed_dev(target, target_len)\n        scores = row_wise_dot(source_embed, target_embed)\n        return scores\n\n    def embed(self,string_mat,string_len):\n        \"\"\"\n        :param string_mat: Batch_size by max_string_len\n        :return: batch_size by embedding dim\n        \"\"\"\n        string_mat = torch.from_numpy(np.transpose(string_mat)).cuda()\n        indices = Variable(string_mat) #torch.cuda.LongTensor(np.transpose(string_mat)))\n        string_len = torch.from_numpy(string_len).cuda()\n        lengths = Variable(string_len.squeeze() * self.config.batch_size)\n        embed_token = self.embedding(indices)\n        final_emb, final_hn = self.rnn(embed_token, (self.h0, self.c0))\n        reshaped = final_emb.resize(self.config.batch_size * self.config.max_string_len, self.config.rnn_hidden_size * self.num_directions)\n        offset = Variable(torch.cuda.LongTensor([x for x in range(0, self.config.batch_size)]))\n        lookup = lengths + offset\n        last_state = reshaped.index_select(0,lookup)\n        return last_state\n\n    def embed_dev(self,string_mat,string_len, print_embed=False,batch_size=None):\n        \"\"\"\n        :param string_mat: Batch_size by max_string_len\n        :return: batch_size by embedding dim\n        \"\"\"\n        if not batch_size:\n            this_batch_size = self.config.dev_batch_size\n            this_h0 = self.h0_dev\n            this_c0 = self.c0_dev\n        else:\n            print(\"irregular batch size {}\".format(batch_size))\n            this_batch_size = batch_size\n            this_h0 = Variable(torch.zeros(self.num_directions, batch_size,\n                                           self.config.rnn_hidden_size).cuda(),\n                               requires_grad=False)\n            this_c0 = Variable(torch.zeros(self.num_directions, batch_size,\n                                           self.config.rnn_hidden_size).cuda(),\n                               requires_grad=False)\n        string_mat = torch.from_numpy(np.transpose(string_mat)).cuda()\n        indices = Variable(string_mat)\n        string_len = torch.from_numpy(string_len).cuda()\n        lengths = Variable(string_len.squeeze() * this_batch_size)\n        embed_token = self.embedding(indices)\n        if print_embed==True:\n            return embed_token\n        final_emb, final_hn = self.rnn(embed_token, (this_h0,this_c0))\n        if self.config.bidirectional == True:\n            reshaped = final_emb.resize(this_batch_size * self.config.max_string_len, self.config.rnn_hidden_size * self.num_directions)\n        else:\n            reshaped = final_emb.resize(this_batch_size * self.config.max_string_len, self.config.rnn_hidden_size)\n        offset = Variable(torch.cuda.LongTensor([x for x in range(0, this_batch_size)]))\n        lookup = lengths + offset\n        last_state = reshaped.index_select(0, lookup)\n        return last_state\n\n    def score_dev_test_batch(self,batch_queries,\n                             batch_query_lengths,\n                             batch_targets,\n                             batch_target_lengths,\n                             batch_size):\n        if batch_size == self.config.dev_batch_size:\n            source_embed = self.embed_dev(batch_queries, batch_query_lengths)\n            target_embed = self.embed_dev(batch_targets, batch_target_lengths)\n        else:\n            source_embed = self.embed_dev(batch_queries, batch_query_lengths,batch_size=batch_size)\n            target_embed = self.embed_dev(batch_targets, batch_target_lengths,batch_size=batch_size)\n        scores = row_wise_dot(source_embed, target_embed)\n        return scores\n","repo_name":"iesl/learned-string-alignments","sub_path":"src/python/entity_align/model/AlignDot.py","file_name":"AlignDot.py","file_ext":"py","file_size_in_byte":7022,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"77"}
+{"seq_id":"35811345198","text":"import numpy as np\nfrom astropy import units as u\nimport poppy\n\n\"\"\"\nThis code has been ported over from POPPY's PowerSpectrumWFE class to run for versions of\nPOPPY that do not include the PowerSpectrumWFE class in wfe.py.\n\nDifference between Fresnel paper and version on STScI's github is declaration of wfe_radius:\n    The Fresnel paper version interally calculates wfe_radius from beam diameter that fills sample plane.\n    It is not a mandatory parameter when using wfe_rms.\n\n    The official version (on STScI's github) has a wfe_radius value required to use wfe_rms.\n    \nThe PSD calculation for both versions remain the same.\n\nIf you are running this code to insert into POPPY, pass in the following values:\n    pixscale = pixscale at the wavefront (wavefront.pixelscale)\n    samp = sample size (538 in Fresnel paper; found in wavefront.n)\n    oversamp = 1/beam_ratio (0.25 in Fresnel paper; found in wavefront.oversample maybe?)\n    wfe_radius = None (will default in code, which is what the Fresnel paper used)\n    map_size='full'\nPOPPY prefers if you use the full wavefront PSD when passing in WFE as FITS.\n\"\"\"\n\n@u.quantity_input(wfe_rms=u.nm, wfe_radius=u.m, incident_angle=u.deg, pixscale=u.m)\ndef get_opd(psd_parameters=None, psd_weight=None, pixscale=None, seed=1234, samp=None,\n            oversamp=None, wfe_rms=None, wfe_radius=None, incident_angle=0*u.deg,\n            apply_reflection=False, map_size='crop', wave=None):\n    \"\"\"\n    Parameters\n    ----------\n    psd_parameters: list (for single PSD set) or list of lists (multiple PSDs)\n        List of specified PSD parameters.\n        If there are multiple PSDs, then each list element is a list of specified PSD parameters.\n        i.e. [ [PSD_list_0], [PSD_list_1]]\n        The PSD parameters in a list are ordered as follows:\n        [alpha, beta, outer_scale, inner_scale, surf_roughness]\n        where:            \n            alpha: float \n                The PSD index value.\n            beta: astropy quantity\n                The normalization constant. In units of :math: `\\frac{m^{2}}{m^{\\alpha-2}}`\n                Numerator assumes surface units of meters\n                Denominator assumes spatial frequency units are 1/m\n            outer_scale: astropy quantity\n                The outer scale value, where the low spatial frequency flattens. \n                Unit requirement: meters\n            inner_scale: float\n                Inner scale value, where the high spatial frequency flattens.\n            surf_roughness: astropy quantity\n                Surface roughness normalization. Should match units of PSD.\n    psd_weight: iterable list of floats\n        Specifies the weight muliplier to set onto each model PSD.\n        If none passed, then defaults to an array of 1's for equal weight placement.\n    pixscale: astropy quantity\n        Spatial pixel scale resolution of the optic.\n        Checks that must be in meters.\n    seed : integer\n        Seed for the random phase screen generator\n        If none passed, defaults to 1234 seed.\n    samp: integer\n        Sample size for the final wfe optic. Math is done in oversampled mode, but returned in samp size.\n        Default to None.\n    oversamp: integer\n        Ratio quantity for scaling samp to calculate PSD screen size.\n        Default to None.\n    wfe_rms: astropy quantity\n        Optional. Use this to force the wfe RMS\n        If a value is passed in, this is the paraxial surface rms value (not OPD) in meters.\n        If None passed, then the wfe RMS produced is what shows up in PSD calculation.\n        Default to None.\n    wfe_radius: astropy quantity\n        Optional. If wfe_rms is passed, then the psd wfe is scaled to a beam of this radius.\n        If a value is not passed in, then assumes beam diameter fills entire sample plane and uses that.\n        Default to None.\n    incident_angle: astropy quantity\n        Adjusts the WFE based on reflected beam distortion.\n        Does not distort the beam (remains circular), but will get the rms equivalent value.\n        Can be passed as either degrees or radians.\n        Default is 0 degrees (paraxial).\n    apply_reflection: boolean\n        Applies 2x scale for the OPD as needed for reflection.\n        Default to False, which will only return surface.\n        Set to True if the PSD model only accounts for surface and want OPD.\n    map_size: string\n        Choose what type of PSD WFE screen is returned.\n        If 'crop', then will return the PSD WFE cropped to samp.\n        If 'full', then will return the PSD WFE at the full oversampled array.\n        Default to 'crop'.\n    wave: POPPY Fresnel Wavefront object\n        poppy wavefront information for doing things\n        \n    Returns\n    -------\n    map_return: numpy array with astropy quantity\n        OPD array sized to samp and scaled according to wfe_rms in units of meters.\n    \"\"\"\n    \n    # Parameter checker\n    # check if wave is present, then can fill in some potentially missing variables.\n    if wave is not None:\n        pixscale = wave.pixelscale * u.pix # sets to only meters\n        oversamp = wave.oversample\n        samp = wave.shape[0]\n        if wave.ispadded is True:\n            screen_size = int(wave.shape[0])\n            samp = int(screen_size/oversamp)\n        else:\n            screen_size = samp * oversamp\n    else:\n        if samp is None:\n            raise Exception(\"Missing: samp value.\")\n        if oversamp is None:\n            raise Exception(\"Missing: oversamp value.\")\n        if pixscale is None:\n            raise Exception(\"Missing: pixelscale value (meters).\")\n        screen_size = samp * oversamp\n    \n    # check the incident angle units that it is not unreasonable\n    if incident_angle >= 90*u.deg:\n        raise ValueError(\"Incident angle must be less than 90 degrees, or equivalent in other units.\")\n    \n    # verify that if wfe_rms was passed, there is also a wfe_radius component.\n    if wfe_rms is not None and wfe_radius is None:\n        if wave is None:\n            raise Exception(\"If you are renormalizing the wfe with wfe_rms, need to pass in a POPPY Fresnel Wavefront object.\")\n        else: \n            wfe_radius = pixscale * samp / 2 # assumes beam diameter fills entire sample plane\n    \n    # if psd_weight wasn't passed in but psd_parameters was, then default to equal weight.\n    if psd_weight is None:\n        psd_weight = np.ones((len(psd_parameters)))\n        \n    # verify the oversample isn't smaller than 1 (otherwise, ruins the scaling)\n    if oversamp < 1:\n        raise ValueError(\"Oversample must be no less smaller than 1.\")\n    \n    # verify that opd_crop is reasonable\n    if map_size != 'full' and map_size != 'crop':\n        raise ValueError(\"map_size needs to be either 'full' or 'crop', please try again.\")\n    \n    # use pixelscale to calculate spatial frequency spacing\n    screen_size = samp * oversamp\n    dk = 1/(screen_size * pixscale) # 1/m units\n    \n    # build the spatial frequency map\n    cen = int(screen_size/2)\n    maskY, maskX = np.mgrid[-cen:cen, -cen:cen]\n    ky = maskY*dk.value\n    kx = maskX*dk.value\n    k_map = np.sqrt(kx**2 + ky**2)\n    \n    # calculate the PSD\n    psd_tot = np.zeros((screen_size, screen_size))\n    for n in range(0, len(psd_weight)):\n        # loop internal localized PSD variables\n        alpha = psd_parameters[n][0]\n        beta = psd_parameters[n][1]\n        outer_scale = psd_parameters[n][2]\n        inner_scale = psd_parameters[n][3]\n        surf_roughness = psd_parameters[n][4]\n        \n        # unit check\n        psd_units = beta.unit / ((dk.unit**2)**(alpha/2))\n        assert surf_roughness.unit == psd_units, \"PSD parameter units are not consistent, please re-evaluate parameters.\"\n        surf_unit = (psd_units*(dk.unit**2))**(0.5)\n        \n        # initialize loop-internal PSD matrix\n        psd_local = np.zeros_like(psd_tot)\n        \n        # calculate the PSD equation based on outer_scale presence\n        if outer_scale.value == 0: # skip or else PSD explodes\n            # temporary overwrite of k_map at k=0 to stop div/0 problem\n            k_map[cen][cen] = 1/dk.value\n            # calculate PSD as usual\n            psd_denom = (k_map**2)**(alpha/2)\n            # calculate the immediate PSD value\n            psd_interm = (beta.value*np.exp(-((k_map*inner_scale)**2))/psd_denom)\n            # overwrite PSD at k=0 to be 0 instead of infinity\n            psd_interm[cen][cen] = 0\n            # return k_map back to original state\n            k_map[cen][cen] = 0\n        else:\n            psd_denom = ((outer_scale.value**(-2)) + (k_map**2))**(alpha/2) # unitless currently\n            psd_interm = (beta.value*np.exp(-((k_map*inner_scale)**2))/psd_denom)\n            \n        # apply the surface roughness\n        psd_local = psd_interm + surf_roughness.value\n        \n        # apply the sum with the weight of the PSD model\n        psd_tot = psd_tot + (psd_weight[n] * psd_local) # should all be m2 [surf_unit]2, but unitless for all calc\n        \n    # set the random noise\n    psd_random = np.random.RandomState()\n    psd_random.seed(seed)\n    rndm_noise = np.fft.fftshift(np.fft.fft2(psd_random.normal(size=(screen_size, screen_size))))\n    \n    psd_scaled = (np.sqrt(psd_tot/(pixscale.value**2)) * rndm_noise)\n    wfe_map = ((np.fft.ifft2(np.fft.ifftshift(psd_scaled)).real*surf_unit).to(wfe_rms.unit)).value\n    \n    # set the rms value based on the active region of the beam\n    if wfe_rms is not None:\n        # build the aperture\n        circ = poppy.optics.CircularAperture(name='beam diameter', radius=wfe_radius)\n        ap = circ.get_transmission(wave)\n        \n        # get the active pixels\n        active_ap = wfe_map[ap==True]\n        \n        # calculate the rms, then rescale the entire map.\n        rms_measure = np.sqrt(np.mean(np.square(active_ap))) # measured rms from declared aperture\n        wfe_map *= (wfe_rms/rms_measure).value # appropriately scales entire wfe_map\n\n    # apply angle adjustment for rms\n    if incident_angle.value != 0:\n        wfe_map /= np.cos(incident_angle).value\n        \n    # Set the reflection\n    if apply_reflection == True:\n        wfe_map *= 2\n    \n    if map_size == 'crop' and oversamp > 1:\n        # resize the beam to the sample size from the screen size\n        map_return = poppy.utils.pad_or_crop_to_shape(array=wfe_map, target_shape=(samp, samp))\n    else:\n        map_return = wfe_map\n    \n    return map_return*wfe_rms.unit\n","repo_name":"jlumbres/model_kit","sub_path":"model_kit/psd_wfe_poppy.py","file_name":"psd_wfe_poppy.py","file_ext":"py","file_size_in_byte":10434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3364070306","text":"import time,random\nimport torch\nimport torch.nn.functional as F\nimport argparse\nimport networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom data_preprocess import load_data\nfrom sklearn.metrics import confusion_matrix\n\ntorch.manual_seed(12345)\nrandom.seed(12345)\nnp.random.seed(12345)\n\nparser = argparse.ArgumentParser(description='Insert Arguments')\n\nparser.add_argument(\"--dataset\", type=str, default=\"cora\", help=\"dataset used for training\")\nparser.add_argument(\"--split\", type=float, default=0.6, help=\"test/train dataset split percentage\")\nparser.add_argument(\"--hidden_channels\", type=int, default=16, help=\"size of GNN hidden layer\")\nparser.add_argument(\"--learning_rate\", type=float, default=0.01, help=\"learning rate for training\")\nparser.add_argument(\"--epochs\", type=int, default=50, help=\"epochs for training\")\n\nargs = parser.parse_args()\n\n#Load Dataset using Pytorch Geometric. In case of non-connected components we take the giant component.\nglobal_graph, num_of_features, num_of_classes, num_of_nodes = load_data(args)\n\n#Create GCN model based on Kipf original paper/code\nfrom model import GCN\nmodel = GCN(nfeat=num_of_features, nhid=args.hidden_channels, nclass=num_of_classes, dropout=0.5)\noptimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay=5e-4)\n\n#Create our unique Client with his characteristics\nfrom Client import Client\nClient0 = Client(0,nx.to_numpy_matrix(global_graph),nx.get_node_attributes(global_graph, \"x\"),nx.get_node_attributes(global_graph, \"y\"))\n\n#Create train-test mask with 70-20 split\ntrain_mask = torch.randn((num_of_nodes)) < 0.7\ntest_mask = torch.logical_not(train_mask)\n\n\nlabels = torch.tensor(list(Client0.y.values()))\ndraw_acc = []\n\n#A function to compute accuracy\ndef accuracy(output, labels):\n    preds = output.max(1)[1].type_as(labels)\n    correct = preds.eq(labels).double()\n    correct = correct.sum()\n    return correct / len(labels)\n\n#Trainer function\ndef train(epoch):\n    t = time.time()\n    model.train()\n    optimizer.zero_grad()\n    output = model(torch.tensor(list(Client0.x.values())), torch.tensor(Client0.A.astype(np.float32)))\n    loss_train = F.nll_loss(output[train_mask], labels[train_mask])\n    acc_train = accuracy(output[train_mask], labels[train_mask])\n    draw_acc.append(acc_train)\n    loss_train.backward()\n    optimizer.step()\n    print('Epoch: {:04d}'.format(epoch+1),\n          'loss_train: {:.4f}'.format(loss_train.item()),\n          'acc_train: {:.4f}'.format(acc_train.item()),\n          'time: {:.4f}s'.format(time.time() - t))\n\n#Tester function\ndef test():\n    model.eval()\n    output = model(torch.tensor(list(Client0.x.values())), torch.tensor(Client0.A.astype(np.float32)))\n    loss_test = F.nll_loss(output[test_mask], labels[test_mask])\n    acc_test = accuracy(output[test_mask], labels[test_mask])\n    print(\"Test set results:\",\n          \"loss= {:.4f}\".format(loss_test.item()),\n          \"accuracy= {:.4f}\".format(acc_test.item()))\n    pred = output.argmax(dim=1)\n    conf_matrix = confusion_matrix(labels[test_mask], pred[test_mask])\n    print(\"+++ Confusion Matrix +++\")\n    print(conf_matrix)\n\n\n# Train model\nt_total = time.time()\nfor epoch in range(args.epochs+1):\n    train(epoch)\nprint(\"Optimization Finished!\")\nprint(\"Total time elapsed: {:.4f}s\".format(time.time() - t_total))\n\n# Testing\ntest()\n\n\nplt.figure(figsize=(10,5))\nplt.title(\"Training Accuracy for Centralized Experiment\")\nplt.plot(draw_acc,label=\"Accuracy\")\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.legend()\nplt.show()","repo_name":"nikopavl4/Federated-Learning-on-Graphs","sub_path":"Node_Classification v2/Centralized/centralized.py","file_name":"centralized.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"33445613944","text":"# https://www.codewars.com//kata/58ac1abdff4e78738f000805\r\n\r\ndef the_biggest_search_keys(*args):\r\n    args = list(args)\r\n    args.sort(key=len)\r\n    if not args:\r\n        return \"''\"\r\n    lst = [x for x in args if len(x) == len(args[-1])]\r\n    lst.sort()\r\n    lst = [\"'\" + x + \"'\" for x in lst]\r\n    if len(lst) > 1:\r\n        return \", \".join(lst)\r\n    return lst[0]\r\n","repo_name":"ryazd/Codewars","sub_path":"6kyu/What The Biggest Search Keys.py","file_name":"What The Biggest Search Keys.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7234173245","text":"import cv2\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom Ui_Form import Ui_Form\nfrom pattern.Mediator import Mediator\nfrom message.Instruction import Instruction\n\ndef cvMat2QImage(mat):\n    mat_rgb = cv2.cvtColor(mat, cv2.COLOR_BGR2RGB)\n    image = QtGui.QImage(mat_rgb.data, mat_rgb.shape[1], mat_rgb.shape[0], QtGui.QImage.Format_RGB888)\n    return image\n\nclass MainWindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        QtWidgets.QMainWindow.__init__(self)\n        self.ui = Ui_Form()\n        self.ui.setupUi(self)\n        self.ui.btnOpen.clicked.connect(self.btnOpen_on_clicked)\n        self.ui.btnClose.clicked.connect(self.btnClose_on_clicked)\n        self.initTable()\n\n    def closeEvent(self, event):\n        Mediator().closeAll()\n\n    def btnOpen_on_clicked(self):\n        Mediator().openTheDoor()\n\n    def btnClose_on_clicked(self):\n        Mediator().closeTheDoor()\n\n    def initTable(self):\n        items = [\"农夫山泉\", \"可口可乐\", \"美年达\", \"康师傅\", \"芬达\", \"蛇草水\"]\n        i = 0\n        for item in items:\n            self.ui.tableLeft.insertRow(i)\n            self.ui.tableLeft.setItem(i, 0, QtWidgets.QTableWidgetItem(str(i+1)))\n            self.ui.tableLeft.setItem(i, 1, QtWidgets.QTableWidgetItem(item))\n            self.ui.tableLeft.setItem(i, 2, QtWidgets.QTableWidgetItem(str(20)))\n            i += 1\n\n\n    def showImage(self, mat):\n        qImage = cvMat2QImage(mat)\n        self.ui.lblImg.setPixmap(QtGui.QPixmap.fromImage(qImage))\n\n    def showDetectResult(self, msg):\n        self.ui.listResult.addItem(QtWidgets.QListWidgetItem(msg))\n\n    def setDoorStatus(self, status : str):\n        self.ui.lblStatus.setText(status)\n\n    def updateLeftStatus(self, instruction : Instruction):\n        classid = instruction.item.id\n        num = instruction.item.num\n        operation = instruction.operation\n        items = self.ui.tableLeft.findItems(str(classid), QtCore.Qt.MatchExactly)\n        if len(items) != 0:\n            row = items[0].row()\n            originNum = eval(self.ui.tableLeft.item(row, 2).text())\n            if operation == \"add\":\n                num = originNum - num\n            elif operation == \"del\":\n                num = originNum + num\n            self.ui.tableLeft.setItem(row, 2, QtWidgets.QTableWidgetItem(str(num)))\n            self.ui.tableLeft.viewport().update()\n","repo_name":"srdp-vm/VMmachine2.0","sub_path":"MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10715996263","text":"# Imports from python.  # NOQA\nimport os\nfrom setuptools import find_packages\nfrom setuptools import setup\n\n\n# Imports from django-assignment-desk.\nfrom assignment_desk import __version__\n\n\nREPO_URL = 'https://github.com/DallasMorningNews/django-assignment-desk/'\n\nPYPI_VERSION = '.'.join(str(v) for v in __version__)\n\nwith open(os.path.join(os.path.dirname(__file__), 'README.md')) as readme:\n    README = readme.read()\n\n\n# allow setup.py to be run from any path\nos.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))\n\nsetup(\n    name='django-assignment-desk',\n    version=PYPI_VERSION,\n    packages=find_packages(),\n    # packages=find_packages(exclude=['demo']),\n    include_package_data=True,\n    license='AGPLv3',\n    description='A simple app to manage newsroom staff assignments.',\n    long_description=README,\n    long_description_content_type='text/markdown',\n    url=REPO_URL,\n    download_url='{repo_url}archive/{version}.tar.gz'.format(**{\n        'repo_url': REPO_URL,\n        'version': PYPI_VERSION,\n    }),\n    author='Alma Washington and Allan James Vestal, The Dallas Morning News',\n    author_email='newsapps@dallasnews.com',\n    classifiers=[\n        'Environment :: Web Environment',\n        'Framework :: Django',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: GNU Affero General Public License v3',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python',\n        # Replace these appropriately if you are stuck on Python 2.\n        'Programming Language :: Python :: 2',\n        'Programming Language :: Python :: 2.6',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Topic :: Internet :: WWW/HTTP',\n        'Topic :: Internet :: WWW/HTTP :: Dynamic Content',\n    ],\n    install_requires=[\n        'Django>=1.11.0',\n        'django-bootstrap3~=9.0.0',\n        'django-colorfield~=0.1.10',\n        'django-editorial-staff~=0.8.0',\n        # 'slacker~=0.9.0',\n    ],\n)\n","repo_name":"DallasMorningNews/django-assignment-desk","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21756926476","text":"\n\"\"\"Transforms coordinates based on pyproj and EPSG codes\n\nAuthors:\n--------\n\n* Sveinung Himle <himsve@kartverket.no>\n\n\nDescription:\n------------\n\n\nExample:\n--------\n\n    >>> python input.csv output.csv 7789 4936\n\n    input.csv is at the format ID(pointname) X Y Z Epoch\n\n    Or:\n\n    >>> python --input input.csv --output output.csv 7789 \"+proj=utm +zone=32 +ellps=GRS80\"\n    \n    Or with area extent:\n    \n    >>> python --input input.csv --output output.csv 7789 4936 --area 1352\n\n\"\"\"\n\n# Standard library imports\nimport numpy as np\nimport csv as csvinput\nimport os\nimport argparse\nfrom datetime import date\n\n# External library imports\nfrom pyproj import Transformer, transform, CRS\nfrom pyproj.aoi import AreaOfInterest, AreaOfUse, BBox\nfrom pyproj.database import query_utm_crs_info, query_crs_info\nfrom pyproj.datadir import get_data_dir, get_user_data_dir\nfrom pyproj._show_versions import _get_proj_info\n\n# Internal library imports\nfrom utilies import get_boundary, try_parse_int, projsync\n\nparser = argparse.ArgumentParser(description='Transforms coordinates from csv files at format ID(pointname) x y z epoch).')\n\nparser.add_argument('sourcecrs', metavar='SourceCrs', type=str, help='EPSG code or proj string of source crs')\nparser.add_argument('targetcrs', metavar='TargetCrs', type=str, help='EPSG code or proj string of target crs')\n\nparser.add_argument('--input', metavar='InputFile', type=str, help='Path to input csv file')\nparser.add_argument('--output', metavar='OutputFile', type=str, help='Path to output csv file')\nparser.add_argument('--area', metavar = \"Area\", type=int, help = 'EPSG code area extent')\nparser.add_argument('-d', metavar = \"D\", type=int, help = 'Number of decimals')\n\nargs = parser.parse_args()\nprint('args is: ', args)\n\nprojsync()\n\noutputFile = None\n\nif args.input is not None:\n    inFileName = args.input\nelse:\n    inFileName = \"\"\n\nif args.output is not None:\n    outFileName = args.output\nelse:\n    outFileName = \"\"\n\nDecimals = 4\n\nif args.d is not None:\n    Decimals = args.d\n\nvarFormat =str('>18.' + str(Decimals) + 'f')\n\nif outFileName != \"\":\n    outputFile = open(outFileName, \"w\")\n\nsourcecrs_epsg = args.sourcecrs\ntargetcrs_epsg = args.targetcrs\n\nsourcecrs_epsg = try_parse_int(sourcecrs_epsg)\ntargetcrs_epsg = try_parse_int(targetcrs_epsg)\n\nsourcecrs = CRS.from_user_input(sourcecrs_epsg)\ntargetcrs = CRS.from_user_input(targetcrs_epsg)\n\n'''\nTest på rekkjefylgje på x og y:\n'''\n\n# Frå source crs\nis_geocentric_srs = sourcecrs.is_geocentric\nis_geographic_srs = sourcecrs.is_geographic\nis_vertical_srs = sourcecrs.is_vertical\nis_projected_srs = sourcecrs.is_projected\nis_compound_srs = sourcecrs.is_compound\n\n# Frå target crs\nis_geocentric_trg = targetcrs.is_geocentric\nis_geographic_trg = targetcrs.is_geographic\nis_vertical_trg = targetcrs.is_vertical\nis_projected_trg = targetcrs.is_projected\nis_compound_trg = targetcrs.is_compound\n\n'''\nDette gjeld både input- og output-crs\nViss is_projected = True eller is_geocentric = True => rekkjefylgje (x, y)\nViss is_geographic = True => (y, x)\n\nNB! Unngå å bruke crs som berre er is_vertical = True, t.d. 5941. Bruk heller 5942.\n'''\n \n# Denne er sett til False. Då er det betre å leggje logikken i is_geographic, is_geocentric eller is_projected\nm_always_xy = False\n\nif args.area is not None:\n    area = args.area\n    # print(get_data_dir())\n    # print(get_user_data_dir())\n    connectionString = get_data_dir() + '\\proj.db'\n    bound = get_boundary(connectionString, area)\n    transformer = Transformer.from_crs(sourcecrs_epsg, targetcrs_epsg, area_of_interest = bound, always_xy=m_always_xy)\nelse:\n    transformer = Transformer.from_crs(sourcecrs_epsg, targetcrs_epsg, always_xy=m_always_xy)\n\npointCount = 0\n\nif os.path.exists(inFileName):\n    if outputFile is not None:\n        outputFile.writelines('Proj version: ' + str(_get_proj_info()) + '\\n')\n        outputFile.writelines('From crs: ' + str(sourcecrs_epsg) + ', to crs ' + str(targetcrs_epsg) + '\\n')        \n        outputFile.writelines('Date: ' + str(date.today()) + '\\n')\n        \n    with open(inFileName) as csvfile:\n        spamreader = csvinput.reader(csvfile, skipinitialspace = True, delimiter=' ', quotechar='|')\n        for row in spamreader:\n            while ('' in row) : row.remove('')\n\n            if len(row) < 3:\n                continue\n\n            id = row[0]\n            x = float(row[1]) if len(row) > 1 else 0\n            y = float(row[2]) if len(row) > 2 else 0\n            z = float(row[3]) if len(row) > 3 else 0\n            e = float(row[4]) if len(row) > 4 else 0\n\n            if len(row) == 3:\n                res = transformer.transform(x, y)                \n                output_line = (\"{: <8}\".format(id) + format(res[0], varFormat) + format(res[1], varFormat))\n                input_line = (\"{: <8}\".format(id) + format(x, varFormat) + format(y, varFormat))\n            elif len(row) == 4:\n                res = transformer.transform(x, y, z)                \n                output_line = (\"{: <8}\".format(id) + format(res[0], varFormat) + format(res[1], varFormat) + format(res[2], varFormat))\n                input_line = (\"{: <8}\".format(id) + format(x, varFormat) + format(y, varFormat) + format(z, varFormat))\n            else:\n                res = transformer.transform(x, y, z, e)                \n                output_line = (\"{: <8}\".format(id) + format(res[0], varFormat) + format(res[1], varFormat) + format(res[2], varFormat) + format(res[3], varFormat))                \n                input_line = (\"{: <8}\".format(id) + format(x, varFormat) + format(y, varFormat) + format(z, varFormat) + format(e, varFormat))                \n\n            pointCount = pointCount + 1\n\n            if outputFile is not None:\n                outputFile.writelines(output_line)\n                outputFile.writelines('\\n')\n\n            print(\"Source coordinates: \", input_line)\n            print(\"Target coordinates: \", output_line)\nelse:\n    while True:\n        inputcoord = input(\"Enter input coordinates (X Y Z Epoch): \")\n        \n        if inputcoord == '' or inputcoord.lower() == 'exit' or inputcoord.lower() == 'quit':\n            break\n\n        splitline = inputcoord.split()\n        if len(splitline) == 2:\n            x = float(splitline[0])\n            y = float(splitline[1])\n            res = transformer.transform(x, y)\n            output_line = (format(res[0], varFormat) + format(res[1], varFormat))               \n        elif len(splitline) ==3:\n            x = float(splitline[0])\n            y = float(splitline[1])\n            z = float(splitline[2])\n            res = transformer.transform(x, y, z)\n            output_line = (format(res[0], varFormat) + format(res[1], varFormat) + format(res[2], varFormat))          \n        elif len(splitline) == 4:\n            x = float(splitline[0])\n            y = float(splitline[1])\n            z = float(splitline[2])\n            e = float(splitline[3])\n            res = transformer.transform(x, y, z, e)\n            output_line = (format(res[0], varFormat) + format(res[1], varFormat) + format(res[2], varFormat) + format(res[3], varFormat))          \n       \n        pointCount = pointCount + 1\n\n        print(output_line)\n\n        if outputFile is not None:\n            outputFile.writelines(output_line)\n            outputFile.writelines('\\n')\n\nif outputFile is not None:\n    outputFile.close()\n\nprint('Transformations succeed!')\nprint(f'{pointCount} points were transformed')\n","repo_name":"himsve/skpyproj","sub_path":"src/skpyproj.py","file_name":"skpyproj.py","file_ext":"py","file_size_in_byte":7443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43372650178","text":"# An alphabetical continuous string is a string consisting of consecutive letters in the alphabet.\n# In other words, it is any substring of the string \"abcdefghijklmnopqrstuvwxyz\".\n# For example, \"abc\" is an alphabetical continuous string, while \"acb\" and \"za\" are not.\n# Given a string s consisting of lowercase letters only, return the length\n#  of the longest alphabetical continuous substring.\n# -------------------------\n# 1 <= s.length <= 10 ** 5\n# s consists of only English lowercase letters.\nfrom string import ascii_lowercase\nfrom random import choice\n\n\ndef longest_continuous_sub(s: str) -> int:\n    # working_sol (41.30%, 84.60%) -> (370ms, 17.37mb)  time: O(n) | space: O(1)\n    max_seq: int = 1\n    cur_seq: int = 1\n    for x in range(1, len(s)):\n        # Record on sequence break.\n        if (ord(s[x]) - ord(s[x - 1])) != 1:\n            max_seq = max(max_seq, cur_seq)\n            cur_seq = 1\n            continue\n        cur_seq += 1\n    # Extra max() check, if sequence was never broken.\n    return max(cur_seq, max_seq)\n\n\n# Time complexity: O(n) -> traversing whole input_string, once => O(n).\n# n - len of input_string^^|\n# Auxiliary space: O(1) -> only 2 extra constant INTs used => O(1).\n\n\ntest: str = \"abacaba\"\ntest_out: int = 2\nassert test_out == longest_continuous_sub(test)\n\ntest = \"abcde\"\ntest_out = 5\nassert test_out == longest_continuous_sub(test)\n\n# test -> Failed -> I was rebuilding it without pointers, forgot that 0 index is already checked -> range(1, len(s)).\ntest = \"orcvscn\"\ntest_out = 1\nassert test_out == longest_continuous_sub(test)\n\ntest = ''\nfor _ in range(10 ** 5):\n    test += choice(ascii_lowercase)\nprint(test)\n","repo_name":"Massprod/leetcode-testing","sub_path":"leetcode_problems/p2414_length_of_the_longest_alphabetical_continuous_substring.py","file_name":"p2414_length_of_the_longest_alphabetical_continuous_substring.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"921819306","text":"def sosu(num):\n    t = 2\n    if num < 2:\n        return False\n\n    while t * t <= num:\n        if num % t == 0:\n            return False\n        t += 1\n    return True\n\ndef saangn(num, visit):\n  result = 0\n  for i in list(str(num)):\n    result += int(i)**2\n  if result == 1:\n    return True\n  elif result in visit:\n    return False\n  else:\n    visit.append(result)\n    return saangn(result, visit)\n\n\n\nnumber = int(input())\nsosuArr,visit = [],[]\nfor i in range(2,number+1):\n  if sosu(i): sosuArr.append(i)\n\nfor i in sosuArr:\n  if saangn(i,visit):\n    print(i)\n  visit = []","repo_name":"snowedev/Algorithm","sub_path":"By-Python/baekjoon/수학/소수상근수.py","file_name":"소수상근수.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11487305457","text":"from pathlib import Path\nfrom typing import List, Optional\n\nimport click\nimport numpy as np\nimport scipy.ndimage\nfrom tqdm import tqdm\n\nfrom brainways.project.brainways_subject import BrainwaysSubject\nfrom brainways.utils._imports import NAPARI_AVAILABLE\n\n\ndef stack_pad_images(images: List[np.ndarray]) -> np.ndarray:\n    stack_height = max(image.shape[0] for image in images)\n    stack_width = max(image.shape[1] for image in images)\n    stack_image = np.zeros(\n        (len(images), stack_height, stack_width), dtype=images[0].dtype\n    )\n    for i, image in enumerate(images):\n        stack_image[i, : image.shape[0], : image.shape[1]] = image\n    return stack_image\n\n\ncur_index = -1\n\n\n@click.command()\n@click.option(\n    \"--input\",\n    type=Path,\n    help=\"Input directory of subjects to display area for.\",\n)\n@click.option(\"--struct\", help=\"Structure acronym to display\")\n@click.option(\"--print-all\", is_flag=True, help=\"Print all structures to console.\")\ndef display_area(input: Path, struct: str, print_all: bool):\n    if not NAPARI_AVAILABLE:\n        raise ImportError(\n            \"Please install napari to display area: \"\n            \"`pip install napari` or `pip install brainways[all]`\"\n        ) from None\n\n    import napari\n\n    paths = list(input.glob(\"*\"))\n    all_struct_images = {}\n    all_struct_annotations = {}\n    all_struct_cells = {}\n    subject: Optional[BrainwaysSubject] = None\n    for subject_path in tqdm(paths):\n        if subject is not None:\n            subject = BrainwaysSubject.open(\n                subject_path, atlas=subject.atlas, pipeline=subject.pipeline\n            )\n        else:\n            subject = BrainwaysSubject.open(subject_path)\n            subject.load_pipeline()\n        struct_id = subject.pipeline.atlas.brainglobe_atlas.structures[struct][\"id\"]\n        struct_images = []\n        struct_annotations = []\n        struct_cells = []\n        for i, document in tqdm(subject.valid_documents):\n            image_to_atlas_transform = subject.pipeline.get_image_to_atlas_transform(\n                document.params, document.lowres_image_size\n            )\n            atlas_slice = subject.pipeline.atlas_registration.get_atlas_slice(\n                document.params.atlas\n            )\n            annotation = atlas_slice.annotation.numpy()\n\n            if struct_id not in annotation:\n                continue\n\n            lowres_image = subject.read_lowres_image(document)\n            annotation_on_lowres_image = (\n                image_to_atlas_transform.inv()\n                .transform_image(\n                    annotation.astype(np.float32),\n                    output_size=lowres_image.shape,\n                    mode=\"nearest\",\n                )\n                .astype(int)\n            )\n\n            highres_image = subject.read_highres_image(document)\n            highres_to_lowres_ratio = highres_image.shape[0] / lowres_image.shape[0]\n            annotation_on_highres_image = scipy.ndimage.zoom(\n                annotation_on_lowres_image,\n                zoom=highres_to_lowres_ratio,\n                order=0,\n            )\n            cells = subject.get_valid_cells(document)\n            cells_on_highres_image = cells[[\"x\", \"y\"]].values * (\n                highres_image.shape[1],\n                highres_image.shape[0],\n            )\n\n            ys, xs = np.where(annotation_on_highres_image == struct_id)\n            x0, x1 = xs.min(), xs.max()\n            y0, y1 = ys.min(), ys.max()\n\n            struct_images.append(highres_image[y0:y1, x0:x1])\n            struct_annotations.append(annotation_on_highres_image[y0:y1, x0:x1])\n            struct_cells.append(\n                cells_on_highres_image[\n                    (cells_on_highres_image[:, 0] >= x0)\n                    & (cells_on_highres_image[:, 0] <= x1)\n                    & (cells_on_highres_image[:, 1] >= y0)\n                    & (cells_on_highres_image[:, 1] <= y1)\n                ]\n                - (x0, y0)\n            )\n\n        struct_images = stack_pad_images(struct_images)\n        struct_annotations = stack_pad_images(struct_annotations)\n        struct_cells_disp = []\n        for i, cells in enumerate(struct_cells):\n            struct_cells_disp.append(\n                np.concatenate([np.ones((len(cells), 1)) * i, cells[:, ::-1]], axis=1)\n            )\n        struct_cells_disp = np.concatenate(struct_cells_disp)\n\n        all_struct_images[subject_path] = struct_images\n        all_struct_annotations[subject_path] = struct_annotations\n        all_struct_cells[subject_path] = struct_cells_disp\n        break\n\n    viewer = napari.Viewer()\n\n    def next_prev(next: bool):\n        global cur_index\n        viewer.layers.clear()\n        if next:\n            cur_index = min(cur_index + 1, len(paths))\n        else:\n            cur_index = max(cur_index - 1, 0)\n        path = paths[cur_index]\n        viewer.title = str(path)\n        viewer.add_image(all_struct_images[path])\n        viewer.add_labels(all_struct_annotations[path])\n        viewer.add_points(all_struct_cells[path], face_color=\"red\", edge_color=\"red\")\n\n    @viewer.bind_key(\"n\")\n    def next(_):\n        next_prev(True)\n\n    @viewer.bind_key(\"p\")\n    def prev(_):\n        next_prev(False)\n\n    next_prev(True)\n\n    napari.run()\n","repo_name":"bkntr/brainways","sub_path":"src/brainways/scripts/display_area.py","file_name":"display_area.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"43048378700","text":"\"\"\"Email backend that writes messages to a file.\"\"\"\n\nimport datetime\nimport os\nimport random\n\nfrom flask_mailman.backends.console import EmailBackend as ConsoleEmailBackend\n\n\nclass ImproperlyConfigured(Exception):\n    \"\"\"Application is somehow improperly configured\"\"\"\n\n    pass\n\n\nclass EmailBackend(ConsoleEmailBackend):\n    def __init__(self, *args, file_path=None, **kwargs):\n        # Since we're using the console-based backend as a base,\n        # force the stream to be None, so we don't default to stdout\n        kwargs['stream'] = None\n        super().__init__(*args, **kwargs)\n        self._fname = None\n        if file_path is not None:\n            self.file_path = file_path\n        else:\n            self.file_path = self.mailman.file_path\n        self.file_path = os.path.abspath(self.file_path)\n        try:\n            os.makedirs(self.file_path, exist_ok=True)\n        except FileExistsError:\n            raise ImproperlyConfigured(\n                'Path for saving email messages exists, but is not a directory: %s' % self.file_path\n            )\n        except OSError as err:\n            raise ImproperlyConfigured(\n                'Could not create directory for saving email messages: %s (%s)' % (self.file_path, err)\n            )\n        # Make sure that self.file_path is writable.\n        if not os.access(self.file_path, os.W_OK):\n            raise ImproperlyConfigured('Could not write to directory: %s' % self.file_path)\n\n    def write_message(self, message):\n        self.stream.write(message.message().as_bytes() + b'\\n')\n        self.stream.write(b'-' * 79)\n        self.stream.write(b'\\n')\n\n    def _get_filename(self):\n        \"\"\"Return a unique file name.\"\"\"\n        if self._fname is None:\n            timestamp = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n            fname = \"%s-%s.log\" % (timestamp, random.randrange(int(1e15)))\n            self._fname = os.path.join(self.file_path, fname)\n        return self._fname\n\n    def open(self):\n        if self.stream is None:\n            self.stream = open(self._get_filename(), 'ab')\n            return True\n        return False\n\n    def close(self):\n        try:\n            if self.stream is not None:\n                self.stream.close()\n        finally:\n            self.stream = None\n","repo_name":"waynerv/flask-mailman","sub_path":"flask_mailman/backends/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","stars":86,"dataset":"github-code","pt":"77"}
+{"seq_id":"40066911866","text":"import numpy as np\nimport heapq\nimport copy\nimport ccgowl.models.grab.pway_funcs as fn2\nfrom ccgowl.models.grab.quic import QUIC_lmat\nfrom sklearn.cluster import KMeans\n\n#####################################\n# ALL CODE TAKEN FROM HERE:\n# https://github.com/mjhosseini/grab\n#####################################\n\n\nmatsession = None\n\n\nclass Edge:  # Used for Max heap: The heap package is originally min heap!\n    def __init__(self, i, k, score):\n        self.i = i\n        self.k = k\n        self.score = score\n\n    def __lt__(self, other):\n        return self.score > other.score\n\n    def __eq__(self, other):\n        return self.score == other.score\n\n\ninf = 100000\n\n\ndef pway_post_process(pathways, P):\n    for k in range(len(pathways)):\n        pathways[k] = list(set(pathways[k]))\n        pathways[k] = np.sort(pathways[k])\n\n    p_new = list()\n    for i in range(len(pathways)):\n        p1 = pathways[i]\n        shouldAdd = 1\n        for j in range(len(pathways)):\n            if (i == j):\n                continue\n            p2 = pathways[j]\n            if (set(p1).issubset(set(p2))):\n                if (i > j or len(p1) != len(p2)):\n                    shouldAdd = 0\n                    break\n        if (shouldAdd == 1):\n            p_new.append(p1)\n\n    K = len(p_new)\n\n    Z_new = np.zeros(shape=(P, K))\n\n    for k in range(len(p_new)):\n        p = set(p_new[k])\n        for j in p:\n            Z_new[j, k] = 1\n\n    return (p_new, Z_new)\n\n\ndef init_pathways(S, P, C, K, Max, clusters=None):\n    if clusters == None:\n        clusters = fn2.my_spectral_clustering(S, K)\n\n    # print \"res of spec: \", clusters\n\n    scores = list()\n    Maxes = list()\n    pways = list()\n    must_add = []\n    for k in range(K):\n        scores.append([])\n        if len(clusters[k]) > Max:\n            npop = len(clusters[k]) - Max\n            for i in range(npop):\n                j = clusters[k].pop()\n                must_add.append(j)\n        C = C - len(clusters[k])\n        Maxes.append(Max - len(clusters[k]))\n        for i in range(P):\n            if i in clusters[k]:\n                scores[k].append(-1)\n                continue\n            s = 0\n            for j in clusters[k]:\n                s = s + np.abs(S[i, j])\n\n            if (len(clusters[k]) != 0):\n                s = s / len(clusters[k])\n            else:\n                s = -inf\n            scores[k].append(s)\n\n        pways.append([])\n        for j in clusters[k]:\n            pways[k].append(j)\n\n    scoresA = np.zeros(shape=(P, K))\n    for k in range(K):\n        for i in range(P):\n            scoresA[i, k] = scores[k][i]\n\n    p_new = assign_pways(scoresA, C, P, K, Maxes, must_add, call_from_init=True)\n    z_new = get_z_from_p(p_new, P, K)\n\n    sum = 0\n    for k in range(len(z_new)):\n        sum = sum + len(z_new[k])\n    add_Z_to_pway(z_new, pways, 0, P)\n\n    (p, z) = pway_post_process(pways, P)\n\n    return (p, z)\n\n\ndef kmeansScores(scores, P, K):\n    X = np.ndarray(shape=(P, K))\n    for i in range(P):\n        for k in range(K):\n            X[i, k] = scores[k][i]\n    labels = KMeans(n_clusters=K).fit_predict(X)\n    return labels\n\n\ndef assign_pways(scores, C, P, K, Maxes, must_add=None, call_from_init=False):\n    scores = copy.copy(scores)\n    scores = scores.T\n\n    p_new = list();\n    for k in range(K):\n        tmp = []\n        p_new.append(tmp)\n\n    assigned_count = [0] * P\n    assigned = 0;\n\n    all_allowed = np.int(C)\n\n    var_list = range(P)\n    if (must_add != None):\n        second_priority = set(var_list).difference(must_add)\n        var_list = list(np.sort(must_add))\n        var_list.extend(np.sort(list(second_priority)))\n    # 1) Max assignment for each var\n    if call_from_init:\n        var_list = must_add  # If call_from_init, we only add the ones that don't have any pathways\n\n    for i in var_list:\n        if ((all_allowed - assigned) <= 0):\n            break\n        scp = list()\n        for k in range(K):\n            scp.append(scores[k][i])\n        tmp = heapq.nlargest(K, range(len(scp)), scp.__getitem__)\n\n        best_k = tmp[0]\n        idx = 0\n        while (len(p_new[best_k]) >= Maxes[best_k]):\n            idx = idx + 1\n            if (idx == K):\n                best_k = -1\n                break\n            best_k = tmp[idx]\n\n        if (best_k == -1 or scores[best_k][i] < 0):\n            continue\n        assigned = assigned + 1\n        scores[best_k][i] = -inf\n        assigned_count[i] = assigned_count[i] + 1\n        p_new[best_k].append(i)\n\n    scorelist = list()\n    for i in range(P):\n        for k in range(K):\n            e = Edge(i, k, scores[k][i])\n            heapq.heappush(scorelist, e)\n\n    all_allowed = all_allowed - assigned\n    idx = 0\n    count = 0\n\n    num_inf = 0\n    all_len = P * K\n    while count < all_allowed and idx < all_len:\n        edge = heapq.heappop(scorelist)\n        idx = idx + 1\n\n        k = edge.k\n        var_idx = edge.i\n        if edge.score == -inf:\n            num_inf = num_inf + 1\n\n        if (len(p_new[k]) >= Maxes[k] or edge.score == -inf):\n            continue\n\n        assigned_count[var_idx] = assigned_count[var_idx] + 1\n        p_new[k].append(var_idx)\n\n        count = count + 1\n\n    for k, p in enumerate(p_new):\n        p_new[k] = np.sort(list(set(p)))\n\n    return p_new\n\n\n# This is very similar to assign_pways2, but just first does kmeans on U.\n# For now, assume Max=P\ndef assign_pways2(U, scores, C, P, K, Maxes, must_add=None):\n    p_new = list();\n    for k in range(K):\n        tmp = []\n        p_new.append(tmp)\n\n    assigned_count = [0] * P\n    assigned = 0;\n\n    k = 0\n    all_allowed = np.int(C)\n\n    var_list = range(P)\n    if (must_add != None):\n        second_priority = set(var_list).difference(must_add)\n        var_list = list(np.sort(must_add))\n        var_list.extend(np.sort(list(second_priority)))\n\n    # 1) Max assignment for each var\n    # What happens at the end of this?: each var is added to exactly one cluster\n\n    labels = kmeansScores(scores, P, K)\n\n    for i in var_list:\n        best_k = labels[i]\n        assigned = assigned + 1\n        scores[best_k][i] = -inf\n        assigned_count[i] = assigned_count[i] + 1\n        p_new[best_k].append(i)\n\n    # recompute score based on the clusters...\n    for i in range(P):\n        for k in range(K):\n            if scores[k][i] == -inf:\n                continue\n            s = 0\n            for j in range(P):\n                if i != j and labels[j] == k:\n                    s += np.dot(U[:, i].T, U[:, j])\n            scores[k][i] = s\n            if s != 0:\n                scores[k][i] /= len(p_new[k])\n\n    # 3) assign from max weight, just don't assign more than capacity\n\n    scorelist = list()\n    for i in range(P):\n        for k in range(K):\n            # print (scores[k][i], \" \")\n            e = Edge(i, k, scores[k][i])\n            heapq.heappush(scorelist, e)\n\n    # heapq.heapify(scorelist)\n\n    all_allowed = all_allowed - assigned\n    idx = 0\n    count = 0\n\n    num_inf = 0\n    all_len = P * K\n    while count < all_allowed and idx < all_len:\n        edge = heapq.heappop(scorelist)\n        # print edge.score, edge.k, edge.i\n        idx = idx + 1\n\n        k = edge.k\n        var_idx = edge.i\n        if edge.score == -inf:\n            num_inf = num_inf + 1\n\n        # print (\"len is \",len(p_new[k]),M)\n        if (len(p_new[k]) >= Maxes[k] or edge.score == -inf):\n            continue\n        # print (\"len is \",len(p_new[k]),M)\n\n        assigned_count[var_idx] = assigned_count[var_idx] + 1\n        # print \"adding \", var_idx, \" to \", k\n        p_new[k].append(var_idx)\n\n        count = count + 1\n\n    for k, p in enumerate(p_new):\n        p_new[k] = np.sort(list(set(p)))\n        # print \"here: \", p_new[k]\n\n        # for i in range(P):\n        # if (assigned_count[i] == 0):\n        # print (i, \" is \", 0)\n\n        # print \"len is assign_pways2:\"\n        # for k in range(K):\n        # print len(p_new[k])\n\n    return p_new\n\n\ndef get_z_from_p(p_new, P, K):\n    z_new = list()\n    for i in range(P):\n        z_new.append([])\n    for k in range(K):\n        for t in range(len(p_new[k])):\n            idx = p_new[k][t]\n            z_new[idx].append(k)\n    return z_new\n\n\ndef make_lmbda_matrix(Z, P, lmbda, W=None):\n    if W is not None:\n        ZZT = W\n    else:\n        ZZT = np.matrix(Z) * np.matrix(Z.T);\n    ret = np.ones(shape=(P, P)) * lmbda;\n    ret -= lmbda * ZZT\n    ret = np.maximum(ret, 0)\n    return ret;\n\n\ndef BCD(S, lmbda=.15, K=5, o_size=.25, max_iter=20, tol=1e-4, dual_max_iter=600, dual_tol=1e-4):\n    P = S.shape[1];\n    Max = (int)((1 + o_size) * ((float)(P) / K))\n    capacity_increase = 1.3\n\n    C = P * ((K * Max) / (float)(P))\n\n    Max = np.int(Max * capacity_increase)\n\n    Max = np.min([Max, P])\n\n    Z = None\n    W = None\n    Theta = None\n\n    prev_ll = 0\n    train_lls = []\n    test_lls = []\n    objectives = []\n\n    partial_accs = []\n\n    for i in range(max_iter):\n        print(\"###############\")\n        print(\"BCD \", i)\n\n        (W, z) = zstep(S, W, Theta, P, K, C, lmbda, Max, dual_tol, dual_max_iter)\n\n        if i == 0:\n            Theta0 = None\n        else:\n            Theta0 = Theta\n\n        lmbdas = make_lmbda_matrix(Z, P, lmbda, W)\n        Theta = QUIC_lmat(S, lmbdas, Theta0)\n\n        train_ll = fn2.get_likelihood(S, Theta)\n\n        obj = fn2.get_obj(S, Theta, W, lmbda)\n\n        print(\"Train, likelihood:\", train_ll)\n        print(\"TrainR, objective:\", obj)\n\n        if (np.abs(train_ll - prev_ll) <= tol):\n            break;\n        prev_ll = train_ll\n\n    pathways = get_pways_from_W(W, P, K, Max, capacity_increase, o_size)\n\n    return (Theta, pathways)\n\n\ndef get_pways_from_W(W, P, K, Max, capacity_increase, o_size):\n    (w, v) = np.linalg.eigh(W)\n    w = np.maximum(w, 0)\n    w = w[P - K:P]\n    w = np.diag(np.sqrt(w))\n    v = v[:, P - K:P]\n    Z = np.matrix(v) * np.matrix(w)\n    pathways = fn2.get_pathways_from_U_kmeans(Z.T, Max, capacity_increase, K, P, o_size)\n    return pathways\n\n\ndef map_Z(Z, lmbda, beta, tau, K, P):\n    Mrow = 1\n    Mcol = np.sqrt(tau)\n    C2 = beta\n\n    prevZ = Z\n    K = Z.shape[1]\n    for piter in range(10):\n\n        norm2s = []\n        for i in range(P):\n            norm2 = fn2.norm(Z[i, :], 2)\n            norm2s.append(fn2.norm(Z[i, :], 1))\n\n            if norm2 > Mrow:\n                Z[i, :] /= (norm2 / Mrow)\n                norm2 = fn2.norm(Z[i, :], 2)\n\n        norm2 = fn2.norm(Z, 2)\n\n        if (norm2 > C2):\n            Z /= (norm2 / C2)\n\n        for k in range(K):\n            norm2 = fn2.norm(Z[:, k], 2)\n            if norm2 > Mcol:\n                Z[:, k] /= (norm2 / Mcol)\n\n        if (prevZ == Z).all():\n            break\n        prevZ = Z\n    return Z\n\n\ndef solve_z(T, P, alpha2, beta2, tau, z, etha=1, tol=1e-4, maxIter=600):\n    if z is None:\n        z = np.zeros(P)\n\n    alpha2 /= tau\n    prevf = 1e9\n\n    for k in range(maxIter):\n        M = T - np.diag(z)\n        (w, v) = np.linalg.eigh(M)\n\n        f = 0\n\n        for i in range(P - beta2, P):\n            if w[i] > 0:\n                f += w[i]\n\n        f += alpha2 * np.sum(z)\n        # print \"now f: \", f\n        if (prevf - f) < tol:\n            break\n        prevf = f\n\n        for i in range(len(w)):\n            if i < P - beta2:\n                w[i] = 0\n                continue\n            if w[i] >= 0:\n                w[i] = 1\n            else:\n                w[i] = 0\n\n        Mp = np.matrix(v) * np.matrix(np.diag(w)) * np.matrix(v.T)\n\n        g = -np.diag(Mp) + alpha2\n\n        if (k < maxIter / 2):\n            z -= etha * g\n        else:\n            z -= etha * (1 / np.sqrt(k - maxIter / 2 + 1)) * g\n\n        z = np.maximum(z, 0)\n\n    # Now, let's construct W\n    M = T - np.diag(z)\n    (w, v) = np.linalg.eigh(M)\n\n    for i in range(len(w)):\n        if i < P - beta2:\n            w[i] = 0\n            continue\n        if w[i] >= 0:\n            w[i] = 1\n        else:\n            w[i] = 0\n\n    W = np.matrix(v) * np.matrix(np.diag(w)) * np.matrix(v.T)\n    W *= tau\n\n    return (W, z)\n\n\ndef zstep(S, W, Theta, P, K, C, lmbda, Max, dual_tol, dual_max_iter):\n    coef = 1\n\n    beta = np.sqrt(P * lmbda / (2 * lmbda))\n    beta /= coef\n\n    tau = beta ** 2 / K\n\n    # Here, we will do hard initialization\n    if (W is None):\n        (pathways, Z) = init_pathways(S, P, C, K, Max)\n\n        Z = map_Z(Z, lmbda, beta, tau, K, P)\n\n        W = np.matrix(Z) * np.matrix(Z.T)\n\n        get_pways_from_W(W, P, K, Max, 1.3, .25)\n\n        return (W, None)\n\n    Theta = np.abs(Theta)\n\n    beta2 = K\n    (W, z) = solve_z(Theta, P, 1.0, beta2, tau, None, tol=dual_tol, maxIter=dual_max_iter)\n\n    return (W, z)\n\n\ndef add_Z_to_pway(Z, pways, start, end):\n    for i in range(start, end):\n        for t in range(len(Z[i - start])):\n            k = Z[i - start][t]\n            pways[k].append(i)\n\n\ndef get_likelihood(X, T):\n    X = np.array(X)\n    return np.linalg.slogdet(T)[1] - np.sum(X * T)\n","repo_name":"cmazzaanthony/ccgowl","sub_path":"ccgowl/models/grab/GRAB.py","file_name":"GRAB.py","file_ext":"py","file_size_in_byte":12814,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"6270787405","text":"from selenium import webdriver\nimport time\nfrom urllib.request import (urlopen,urlparse,urlretrieve)\n\nfrom selenium.webdriver.common.keys import Keys\nimport pandas as pd\n\nimport os\n\n# 구글 이미지 URL\nchrome_path=\"./chromedriver.exe\"\n\nbase_url=\"https://chrome.google.co.kr/imghp\"\n\n# 구글 검색할 때는 옵션이 필요함\nchrome_options=webdriver.ChromeOptions()\nchrome_options.add_argument(\"lang=ko_KR\") #한국어\n# 윈도우 창 사이즈 바꾸기\nchrome_options.add_argument(\"window-size=1920x1080\")\n\n# 드라이버 연결\ndriver = webdriver.Chrome(chrome_path,chrome_options=chrome_options)\ndriver.get(base_url)\n# 엘리멘트 로드될 때까지 지정한 시간만큼 대기할 수 있도록 하는 옵션\ndriver.implicitly_wait(3)\n# 잘 나오는지 캡쳐\ndriver.get_screenshot_as_file(\"google_screen.png\")\n# 드라이버 종료\ndriver.close()\n\n# 스크롤 조절\ndef selenium_scroll_option():\n    SCROLL_PAUSE_SEC = 1\n\n    # 스크롤 높이 가져옴\n    last_height=driver.execute_script(\n        \"return document.body.scrollHeight\") #구글에서 키워드로 가져오면 된다\n    \n    while True:\n        # 끝까지 스크롤 다운\n        driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight);\")\n        # 대기 타임 걸기\n        time.sleep(SCROLL_PAUSE_SEC)\n        # 스크롤 다운 후 스크롤 높이 다시 가져오기\n        new_height=driver.execute_script(\"return document.body.scrollHeight\")\n\n        if new_height == last_height:\n            break\n        last_height=new_height\n\na='공룡'\nimage_name=\"dinosaur\"\ndriver=webdriver.Chrome(chrome_path) #드라이버 불러오기\ndriver.get(\"http://www.google.co.kr/imghp?hl=ko\") #어디 url에 검색할 지 인자 넣어줌\nbrowser=driver.find_element_by_name('q') #검색어 키\nbrowser.send_keys(a)\nbrowser.send_keys(Keys.RETURN)\n\n\n\n#사진 더 보기\n#//*[@id=\"islmp\"]/div/div/div/div[1]/div[2]/div[2]/input\nselenium_scroll_option #스크롤하여 이미지 확보\ndriver.find_element_by_xpath('//*[@id=\"islmp\"]/div/div/div/div[1]/div[2]/div[2]/input').click() #버튼 누르기\nselenium_scroll_option() #스크롤 내리기\n\n#이미지 저장 src 요소를 리스트업 해서 이미지 url 저장\nimage=driver.find_elements_by_css_selector(\".rg_i.Q4Luwd\")\n#클래스 네임에서 공백은 .을 찍어줌\n\n#이미지 가져오기\nimage_url=[]\nfor i in image:\n    if i.get_attribute(\"src\")!=None:\n        image_url.append(i.get_attribute(\"src\"))\n    else:\n        image_url.append(i.get_attribute(\"data-src\"))\n\n#전체 이미지 개수 살펴보기\nprint(f\"전체 다운로드한 이미지 개수 : {len(image_url)}\")\n\n#판다스에 한 차원 내려서 저장하기\nimage_url=pd.DataFrame(image_url)[0].unique()\n\n#해당하는 파일에 이미지를 다운로드\n#dino 디렉토리를 만들기\nos.makedirs(\"./dino\",exist_ok=True)\ndino=\"./dino/\"\n\nif image_name=='shark':\n    for t,url in enumerate(image_url,0):\n        urlretrieve(url,dino+image_name+\"_\"+str(t)+\".png\") #다운해주는 기능\n\n    \n    driver.close()\n\nprint(\"완료\")","repo_name":"burninghering/AI_School","sub_path":"Week_11/0523/실습/cr_image_hyerin.py","file_name":"cr_image_hyerin.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14804373997","text":"# Django settings for vapour project.\n\nimport os\nimport sys\n\nROOT_PATH = os.path.dirname(__file__)\n\nsys.path.append(os.path.abspath(\"lib\"))\n\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\n\nADMINS = (\n     (\"Sergio Fernandez\", \"sergio.fernandez@fundacionctic.org\"),\n     (\"Diego Berrueta\", \"diego.berrueta@fundacionctic.org\")\n)\n\nMANAGERS = ADMINS\n\nDATABASE_ENGINE = 'django.db.backends.'  # 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.\nDATABASE_NAME = ''             # Or path to database file if using sqlite3.\nDATABASE_USER = ''             # Not used with sqlite3.\nDATABASE_PASSWORD = ''         # Not used with sqlite3.\nDATABASE_HOST = ''             # Set to empty string for localhost. Not used with sqlite3.\nDATABASE_PORT = ''             # Set to empty string for default. Not used with sqlite3.\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# If running in a Windows environment this must be set to the same as your\n# system time zone.\nTIME_ZONE = 'CET'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en-us'\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = False\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/home/media/media.lawrence.com/media/\"\nMEDIA_ROOT = os.path.join(ROOT_PATH, \"..\", \"media\")\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\nMEDIA_URL = \"/media/\"\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = os.path.join(ROOT_PATH, \"static\")\n\n# URL prefix for static files.\n# Example: \"http://media.lawrence.com/static/\"\nSTATIC_URL = '/static/'\n\n# URL prefix for admin static files -- CSS, JavaScript and images.\n# Make sure to use a trailing slash.\n# Examples: \"http://foo.com/static/admin/\", \"/static/admin/\".\nADMIN_MEDIA_PREFIX = '/resources/admin/'\n\n# Additional locations of static files\nSTATICFILES_DIRS = (\n    # Put strings here, like \"/home/html/static\" or \"C:/www/django/static\".\n    # Always use forward slashes, even on Windows.\n    # Don't forget to use absolute paths, not relative paths.\n    os.path.join(ROOT_PATH, \"..\", \"static\"),\n)\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n#    'django.contrib.staticfiles.finders.DefaultStorageFinder',\n)\n\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 'd26h#_0*xdub&cj)v$67qu!*7*wl^nxg5j7a20%08)n*x1khsl'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n    'django.template.loaders.filesystem.Loader',\n    'django.template.loaders.app_directories.Loader',\n#     'django.template.loaders.eggs.Loader',\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.middleware.common.CommonMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n)\n\nROOT_URLCONF = \"vapour.urls\"\n\nTEMPLATE_DIRS = (\n    # Put strings here, like \"/home/html/django_templates\" or \"C:/www/django/templates\".\n    # Always use forward slashes, even on Windows.\n    # Don't forget to use absolute paths, not relative paths.\n    os.path.join(ROOT_PATH, \"templates'\")\n)\n\nINSTALLED_APPS = (\n     'django.contrib.staticfiles',\n#    'django.contrib.auth',\n#    'django.contrib.contenttypes',\n#    'django.contrib.sessions',\n#    'django.contrib.sites', \n     \"vapour.common\",   \n     \"vapour.cup\",\n     \"vapour.strainer\",\n     \"vapour.teapot\",\n)\n\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': True,\n    'formatters': {\n        'simple': {\n            'format': '%(asctime)s %(filename)s:%(lineno)d %(levelname)s: %(message)s'\n        },\n        'verbose': {\n            'format': '%(asctime)s %(filename)s:%(lineno)d %(process)d %(thread)d %(levelname)s: %(message)s'\n        }\n    },\n    'handlers': {\n        'console': {\n            'level' : 'INFO',\n            'class' : 'logging.StreamHandler',\n            'formatter': 'simple'\n            \n        }\n    },\n    'loggers': {\n        'vapour': {\n            'handlers': ['console'],\n            'level': 'INFO'\n        },\n        'vapour.dev': {\n            'handlers': ['console'],\n            'level': 'DEBUG'\n        }\n    }\n}\n\n# Hosts/domain names that are valid for this site; required if DEBUG is False\n# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts\nALLOWED_HOSTS = []\n\n#Custom stuff for vapour\nREQ_BASE_URL = \"http://validator.linkeddata.org/vapour#req\"\nPATH_RDF_FILES = os.path.join(ROOT_PATH, \"..\", \"rdf\")\nPATH_TEMPLATES = os.path.join(ROOT_PATH, \"strainer/templates/\")\nALLOW_INTRANET = True\n\n","repo_name":"berrueta/vapour","sub_path":"apps/vapour/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13541012744","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 17 16:21:00 2020\n\n@author: Fernando Mazzone\n\"\"\"\ndef SIR_2C_estocastico(N1,N2,n,R01,R02,media_transf,delta_s):\n    \"\"\"\n    Dos ciudades sobre las que avanza una epidemia. En cada una de ellas se\n    asume un modelos SIR discreto. Se asume que en cada iteración con \n    probabilidad p un individuo de una ciudad puede pasar a la otra.\n    La primera ciudad con un número de reproducción básico R01 y la segunda \n    con R02. La unidad de tiempo es el periodo de infectividad y la población\n    total está normalizada N=1.\n    Parametros:\n        n: int (#iteraciones)\n        R01: float64,  número de reproducción básico ciudad 1\n        R02: float64,  número de reproducción básico ciudad 2\n        p: float64, probabilidad que se intercambie Delta_H habitantes\n           entre ciudades\n        Delta_H: float 64 algo así como un individuo\n    \"\"\"\n    \n    x0=np.array([[N1-10, N2 ],[10,0],[0,0]],dtype=int)\n    \n    ## delta_s son los incrementos de tiempos. \n    ##Debe ser elegido\n    ##               delta_s<min{1,1/max{R01,R02}}\n    \n    beta01,beta02=R01/N1,R02/N2\n    \n    \n    \n    X=np.zeros([3,2,n],dtype=int)\n    X[:,:,0]=x0\n    Transfer=np.zeros([3,2,n-1],dtype=int)\n    I1d=np.zeros(n-1,dtype=int)\n    I2d=np.zeros(n-1,dtype=int)\n    \n    for i in range(n-1):\n        X0,I1d[i],I2d[i],T0=SIR_est_2C(X[:,:,i],beta01,beta02,media_transf,delta_s)\n        X[:,:,i+1]=X0\n        Transfer[:,:,i]=T0\n\n    return X,I1d,I2d,Transfer\n\n\n\ndef SIR_est_2C(x,beta01,beta02,media_transf, delta_s):\n    \n    \n    ## Decidir cuantos habitantes pasan de una población a otra\n    \"\"\"\n    S1,I1,R1=x[:,0]\n    S2,I2,R2=x[:,1]\n\n    \n    N1=float(S1+I1+R1)\n    N2=float(S2+I2+R2)\n    \"\"\"\n    \n    N1=float(np.sum(x[:,0]))\n    N2=float(np.sum(x[:,1]))\n    \n    Transfer1=poisson(delta_s*media_transf*x[:,0]/N1)\n    Transfer2=poisson(delta_s*media_transf*x[:,1]/N2)\n \n    Transfer1=np.minimum(Transfer1,x[:,0])\n    Transfer2=np.minimum(Transfer2,x[:,1])\n    \n    Transfer=np.vstack((Transfer1,Transfer2)).T\n    \n    \n    x[:,0]=Transfer2-Transfer1+x[:,0]\n    x[:,1]=Transfer1-Transfer2+x[:,1]\n    S1,I1,R1=x[:,0]\n    S2,I2,R2=x[:,1]\n    \n    \n    \"\"\"\n    mu1=beta01*delta_s*S1*I1\n    mu2=delta_s*I1\n    nu1=beta02*delta_s*S2*I2\n    nu2=delta_s*I2\n    \"\"\"\n    mu1=beta01*delta_s*S1*I1\n    mu2=delta_s*I1\n    nu1=beta02*delta_s*S2*I2\n    nu2=delta_s*I2 \n    \n    \n    I1_dot=poisson(lam=mu1)\n    R1_dot=poisson(lam=mu2)\n    I2_dot=poisson(lam=nu1)\n    R2_dot=poisson(lam=nu2)\n    \n    Delta=np.array([[I1_dot,I2_dot],[R1_dot,R2_dot]])\n\n    \n    Delta=np.minimum(Delta,x[:2,:2])\n    \n    \n\n    S1_new=S1-Delta[0,0]\n    I1_new=I1-Delta[1,0]+Delta[0,0]\n    R1_new=R1+Delta[1,0]\n    \n    S2_new=S2-Delta[0,1]\n    I2_new=I2-Delta[1,1]+Delta[0,1]\n    R2_new=R2+Delta[1,1]\n    \n   \n    \n    X=np.array([[S1_new,S2_new ],[I1_new,I2_new],[R1_new,R2_new]])\n    return X,I1_dot,I2_dot, Transfer\n\n\n\n\nfrom numpy.random import rand, binomial, multinomial, poisson\n\n\n###################  EXPERIMENTO #############################################\n##############################################################################\n\nfig, ((ax1,ax2,ax3),(ax4,ax5,ax6))=plt.subplots(2,3,figsize=(20,15))\n\nR01,R02=1.1,5.1\n\n\n\nN1=100000\nN2=100000\ndelta_s=.1*min(1,N1/R01,N2/R02)\nmedia_transf=10.0\nn=1000\n\n\nX=np.zeros([3,2,n],dtype=int)\nT=np.zeros([3,2,n-1],dtype=int)\nI1d=np.zeros(n-1,dtype=int)\nI2d=np.zeros(n-1,dtype=int)\n\nnro_exp=1\nfor j in range(nro_exp):\n    X1,I1d1,I2d1,T1=SIR_2C_estocastico(N1,N2,n,R01,R02,media_transf,delta_s)\n    X+=X1\n    T+=T1\n    I1d+=I1d1\n    I2d+=I2d1\nX=X/nro_exp\nT=T/nro_exp\nI1d=I1d/nro_exp\nI2d=I2d/nro_exp\n\n\n\ns=np.arange(0,n*delta_s,delta_s)\n\n\n\n\n\nax1.plot(s,X[:,0,:].T,marker='o')\nax1.set(yscale='log',ylim=(1,N1+N2))\nax1.set_title('Ciudad 1')\nax1.legend(['S','I','R'])\n\n\nax2.plot(s,X[:,1,:].T,marker='o')\nax2.set(yscale='log',ylim=(1,N1+N2))\nax2.set_title('Ciudad 2')\nax2.legend(['S','I','R'])\n\n\nY=np.sum(X,axis=1) ## Suma de las dos ciudades\nIa=np.sum(Y[1:,:],axis=0) ## Infectados acumulados\nIad=np.diff(Ia)  ## infectados diarios\n\n\n###  Grafica de infectados acumulados###################################\n\nax3.plot(s,Ia,s[1:],Iad)\nax3.set(yscale='log',ylim=(1,N1+N2))\nax3.set_title('Pais')\nax3.legend(['I_acum','I','R'])\n\nax4.plot(s[:-1],T[:,0,:].T.cumsum(axis=0),marker='o')\nax4.set_title('Ciudad 1-Migraciones')\nax4.legend(['S','I','R'])\n\n\nax5.plot(s[:-1],T[:,1,:].T.cumsum(axis=0),marker='o')\nax5.set_title('Ciudad 2-Migraciones')\nax5.legend(['S','I','R'])\n\nax6.plot(s[:-1],I1d,s[:-1],I2d,marker='o')\nax6.set_title('Ciudadades 1 y 2-Casos nuevos')\n","repo_name":"fdmazzone/COVID-19","sub_path":"Experimentos/SIR-Estocastico-2C.py","file_name":"SIR-Estocastico-2C.py","file_ext":"py","file_size_in_byte":4634,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29380967569","text":"def encontrar_substrings_unicos(string, n):\n    freq_dict = {}\n\n    for i in range(len(string) - n + 1):\n        substring = string[i:i + n]\n\n        freq_dict[substring] = freq_dict.get(substring, 0) + 1\n\n    substrings_unicos = [substring for substring, freq in freq_dict.items() if freq == 1]\n\n    return substrings_unicos\n\nstring_input = input(\"Ingrese el string: \")\nn_input = int(input(\"Ingrese el valor de n: \"))\n\nsubstrings = encontrar_substrings_unicos(string_input, n_input)\n\nif len(substrings) > 0:\n    for substring in substrings:\n        print(substring)\nelse:\n    print(\"ninguna\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej3/hito2_ej3_fa124adb2ada50a72589804cf48793ed.py","file_name":"hito2_ej3_fa124adb2ada50a72589804cf48793ed.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14203992835","text":"# Below are a set of scores that students \n# have received in the past semester. \n# Write code to determine how many are \n# 90 or above and assign that result to \n# the value a_scores.\n# \n# Pseudo Code\n# 1. Retrieve past scores: Given in string\n# 2. Cast score as Integer and Convert past score \n#     string into 'List of Past Scores'.\n# 3. Define: Accumulating Sum Variable for 'Total\n#    Score above 90' as variable a_scores and set it 0.\n# 4. Define interating variable and iterate\n#    through the 'List of Past Scores'.\n# 5. For each iteration in 'List of Past Scores',\n#    determine if the score is 90 or above?\n# 6. If the score is 90 or above, add 1 to the \n#    'Total Score above 90' varuable a_scores\n# 7. Print the 'Total Score Above 90' a_scores variable\n#\nscores = \"67 80 90 78 93 20 79 89 96 97 92 88 79 68 58 90 98 100 79 74 83 88 80 86 85 70 90 100\"\nscores = [int(score) for score in scores.split()]\na_scores = 0\nfor score in scores:\n     if score >= 90:\n         a_scores+=1\nprint(a_scores)\n","repo_name":"nguyenvx007/Python-University-of-Michigan","sub_path":"Python Basic - Course 1/Wk4-Course1-assessment-12-Final Week-1-4_9.16.5.1.py","file_name":"Wk4-Course1-assessment-12-Final Week-1-4_9.16.5.1.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14751289470","text":"def solution(board, moves):\n    answer = 0\n\n    bucket = [-1]\n    for move in moves:\n\n        for idx, i in enumerate([line[move-1] for line in board]):\n            if i != 0:\n                if bucket[len(bucket)-1] == i:\n                    answer += 2\n                    bucket.pop()\n                else :\n                    bucket.append(i)\n\n                board[idx][move-1] = 0\n                break\n                \n    return answer\n\nprint(solution([[0,0,0,0,0],[0,0,1,0,3],[0,2,5,0,1],[4,2,4,4,2],[3,5,1,3,1]],\n[1,5,3,5,1,2,1,4]))\n\n\n","repo_name":"dolgogae/algorithm","sub_path":"programmers/python/크레인인형뽑기게임.py","file_name":"크레인인형뽑기게임.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70607457850","text":"\ndef apply_rules(s, rules):\n    return ''.join(map(lambda c: rules.setdefault(c, c), s))\n\ndef evaluate(s, rules, iterations):\n    for _ in range(iterations):\n        s = apply_rules(s, rules)\n    return s\n\ndef main():\n    N, M = map(int, input().split())\n    rules = {}\n    for _ in range(N):\n        f, _, t = input().split()\n        rules[f] = t\n    seed = input()\n    print(evaluate(seed, rules, M))\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"CianLR/judge-solutions","sub_path":"kattis/lindenmayorsystem.py","file_name":"lindenmayorsystem.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"14551118569","text":"from gensim import models\nfrom sklearn.cluster import KMeans\nimport cleaning.py\n\nwordvec = models.Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\nvecs=[]\n\n#list of word tokenized sentences\nfor sentence in words_req:\n\tfor words in sentence:\n\t\tword=wordvec[words]\n\t\tvecs.append(word)\n#returns a list of vectors for the particular item\n\nkmeans = KMeans(init='k-means++', n_clusters=3, n_iter=20)\nkmeans.fit(vecs)\n\n","repo_name":"divapriya/Smart-Web-Crawler-using-ML-techniques","sub_path":"Assignment/clustering.py","file_name":"clustering.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"71993700090","text":"from __future__ import print_function\n\nimport inspect\nimport lldb\nimport optparse\nimport shlex\nimport sys\nimport m_config_lldb\n\n\nclass MPrint:\n    program = 'mprint'\n\n    @classmethod\n    def register_lldb_command(cls, debugger, module_name):\n        parser = cls.create_options()\n        cls.__doc__ = parser.format_help()\n        # Add any commands contained in this module to LLDB\n        command = 'command script add -c %s.%s %s' % (module_name,\n                                                      cls.__name__,\n                                                      cls.program)\n        debugger.HandleCommand(command)\n        if m_config_lldb.debug:\n            print('The \"{0}\" command has been installed, type \"help {0}\" or \"{0} '\n                  '--help\" for detailed help.'.format(cls.program))\n\n    @classmethod\n    def create_options(cls):\n\n        usage = \"usage: %prog [options] arg\"\n        description = ('Basically:  mprint : Prints Maple runtime object data')\n\n        # Pass add_help_option = False, since this keeps the command in line\n        #  with lldb commands, and we wire up \"help command\" to work by\n        # providing the long & short help methods below.\n        parser = optparse.OptionParser(\n            description=description,\n            prog=cls.program,\n            usage=usage,\n            add_help_option=False)\n\n        \"\"\"Prints Maple runtime object data\n        mprint: prints a Maple object's data:\n        mprint <addr-in-hex>: e.g. mprint 0x13085\n        \"\"\"\n\n        parser.add_option(\n            '-h',\n            '--help',\n            action='store_true',\n            dest='mprint_help',\n            help=description,\n            default=False)\n\n        return parser\n\n    def get_short_help(self):\n        return \"Prints Maple runtime object data\"\n\n    def get_long_help(self):\n        return self.help_string\n\n    def __init__(self, debugger, unused):\n        self.parser = self.create_options()\n        self.help_string = self.parser.format_help()\n\n    def __call__(self, debugger, command, exe_ctx, result):\n        # Use the Shell Lexer to properly parse up command options just like a\n        # shell would\n        command_args = shlex.split(command)\n\n        try:\n            (options, args) = self.parser.parse_args(command_args)\n            print(options)\n            print(args)\n            if options.mprint_help:\n                print (\"  mprint      : prints Maple object data\")\n                print (\"  mprint <addr-in-hex>: e.g. mprint 0x13085\")\n            else:\n                print(\"Executing command\")\n\n        except:\n            # if you don't handle exceptions, passing an incorrect argument to\n            # the OptionParser will cause LLDB to exit (courtesy of OptParse\n            # dealing with argument errors by throwing SystemExit)\n            result.SetError(\"option parsing failed\")\n            return\n        # not returning anything is akin to returning success\n\n\ndef __lldb_init_module(debugger, dict):\n    # Register all classes that have a register_lldb_command method\n    for _name, cls in inspect.getmembers(sys.modules[__name__]):\n        if inspect.isclass(cls) and callable(getattr(cls,\n                                                     \"register_lldb_command\",\n                                                     None)):\n            cls.register_lldb_command(debugger, __name__)\n\n\n","repo_name":"openmaple/MapleEngine","sub_path":"maple_debugger/mlldb/maple_debugger/LLDB/mlldb/m_print.py","file_name":"m_print.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5035975247","text":"###--------------------- This is the location of some functions contributing to Feature Selection operations-----------------------\n\n###---------------------IMPORTS FOR CLASSIFICATION\nimport numpy as np\nimport pandas as pd\nimport scipy\nimport locale\nfrom operator import itemgetter # to sort lists of tuples using directly one position in the index elements\n###---------------------IMPORTS COMPLEMENTARY FOR REGRESSION\n# from rpy2 import robjects\n# from rpy2.rinterface import RRuntimeError for python 2.7 solve later\n# from rpy2.rinterface_lib.embedded import RRuntimeError # for python 3.7\n# ---------------------Variables to initialise------------------------------------------\nlocale.setlocale(locale.LC_ALL, 'en_US.UTF-8') #for setting the characters format\n#====================================================================\n\n#====================================classification used functions\n#-------get the number of feature in a df\ndef length_features_list(trainset, index_of_first_feat_in_cols_list): # take a frame as arg and index_of_first_feat_in_cols_list (5) as arg\n\tlen_fts_list = len(list(trainset)[index_of_first_feat_in_cols_list:])\n\treturn len_fts_list\n##!! here the index of the first feature is to be given but in the future, make a function to detect in (hint : the index of the first column name to not contain sample id ctype etc.)\n\n#---process of limiting training data to only fts who shows variation (at least one variation in a sample)\ndef eliminate_non_variable_fts(frame):\n\tframe_with_variable_fts_only = frame.drop(frame.columns[frame.apply(lambda col: col.nunique(dropna=True) == 1)], axis=1)\n\treturn frame_with_variable_fts_only\n\n#-------------------------------functions to define the value of the maximal complexity\n# calcul de n/2 pour chaque train set\ndef maximal_complexity_as_half_tr(trainset):\n\tmaxfeatures = int(np.ceil(float(len(trainset)) / 2))\n\treturn maxfeatures\n# add others functions for others values (eg : the 3/4, 70% etc.)\n#-------------------------------functions that define the list of complexities to test seeking the best of them\ndef list_of_complexities_ext(minfeatures, maxfeatures,additional_complexity_value): # minfeatures = 2 for OMC\n\tcomplexities = list(range(minfeatures, maxfeatures)) + [additional_complexity_value]  # range de 2 a n/2 + number total of fts # sum of 2 lists is a bigger list with the two succesiively in it\n\tcomplexities = list(sorted(set(complexities)))  # make it a set, sort it and make it a list\n\treturn complexities\n\ndef list_of_complexities_ltd(minfeatures, maxfeatures):\n\tcomplexities = list(range(minfeatures, maxfeatures))\n\tcomplexities = list(sorted(set(complexities)))\n\treturn complexities\n\n#---process of limiting training data to only fts who shows variation (at least one variation in a sample) ## isolate\n# def ranker_by_pval_v1(frame_fts,frame_resp,feature_type,resp_col): ### deprecated\n# \tdict_pvalues = {}\n# \tif feature_type in [\"SNV\",\"CNA\"]: # cases of discrete values, do a fisher exact test on contigency table of fts var with response and extract pvalues\n# \t\tfor feat in list(frame_fts):\n# \t\t\tthe_feat_col = pd.Categorical(frame_fts[feat],categories=[0,1])\n# \t\t\tthe_resp_col = pd.Categorical(frame_resp[resp_col],categories=[\"Res\",\"Sen\"]) ##!! externalise to the for loop to do it only one time ##!! use categories list instead of hard code\n# \t\t\tcontingency_table_filled = pd.crosstab(the_feat_col,the_resp_col,dropna=False) # dropna=False is used to tell to the system to still count classes that were not predicted\n# \t\t\toddsratio, p_value_f = scipy.stats.fisher_exact(contingency_table_filled, alternative=\"two-sided\")\n# \t\t\tdict_pvalues[feat] = p_value_f\n# \telse : # case of reals values, find the two extreme situations of response segregation ability by the feature or do a t test\n# \t\tres_indexes = frame_resp.index[frame_resp[\"Resp_Class\"] == \"Res\"].tolist()\n# \t\tsen_indexes = frame_resp.index[frame_resp[\"Resp_Class\"] == \"Sen\"].tolist()\n# \t\tfor feat in list(frame_fts):\n# \t\t\tthe_resp_samples = frame_fts.loc[res_indexes,[feat]]\n# \t\t\tthe_sen_samples = frame_fts.loc[sen_indexes, [feat]]\n# \t\t\tif (the_resp_samples[feat].nunique(dropna=True) == 1) & (the_sen_samples[feat].nunique(dropna=True) == 1) : # the 2 groups values are uniquely described\n# \t\t\t\tif the_resp_samples[feat].unique()[0] != the_sen_samples[feat].unique()[0]: # by a different unique value\n# \t\t\t\t\tp_val_top = -1 # the perfect differenciation then\n# \t\t\t\t\tdict_pvalues[feat] = p_val_top\n# \t\t\t\telse:\n# \t\t\t\t\tp_val_worst = 1  # the worst differenciation then\n# \t\t\t\t\tdict_pvalues[feat] = p_val_worst\n# \t\t\telse: # not just one unique value exist in a group, the t-test is done then\n# \t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples, the_sen_samples, equal_var=False) #old line 0\n# \t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples.dropna()[feat], the_sen_samples.dropna()[feat], equal_var=False) #old line 1.5\n# \t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples.iloc[:,0].tolist(),the_sen_samples.iloc[:,0].tolist(),equal_var = False,nan_policy='propagate') # old line 1\n# \t\t\t\tt_stat, p_value_t = scipy.stats.ttest_ind(np.array(the_resp_samples.iloc[:, 0].tolist()), np.array(the_sen_samples.iloc[:, 0].tolist()), equal_var=False, nan_policy='omit')\n# \t\t\t\t##!! transforming a column to an array is done by putting it as a list and then as a numpy array\n# \t\t\t\t# ttest_ind suppose variance equals by default so underestimates p for unequal variances even if the t-statistic is the same (eg : case where size of samples are equals but on different scale :;\n# \t\t\t\t# When n1 != n2, the equal variance t-statistic is no longer equal to the unequal variance t-statistic:\n# \t\t\t\t# so we use  equal_var = False\n# \t\t\t\t## !!! to try : make a condittion if variance and size are equals, use equal_var = True else use equal_var = False\n# \t\t\t\t# nan_policy='propagate' to take into account the nan and 'omit' to not take them into account\n# \t\t\t\tdict_pvalues[feat] = p_value_t\n# \t# sorted_feats_by_pval = sorted(dict_pvalues, key=dict_pvalues.get, reverse=False) # deprecated as not working properly\n# \tsorted_feats_by_pval = [key for key, value in sorted(dict_pvalues.items(), key=itemgetter(1), reverse=False)] # default is reverse = False so can omit it\n# \t# sorted_pval = [value for key, value in sorted(dict_pvalues.items(), key=itemgetter(1), reverse=False)]  to report later on results # default is reverse = False so can omit it\n# \treturn dict_pvalues,sorted_feats_by_pval\n\n# new versio of the ranker by pval function\ndef ranker_by_pval_v2(frame_fts,frame_resp,feature_val_type,resp_col,encoded_classes):\n\tdict_pvalues = {} # a col for all the pvalues computed\n\tres_indexes = frame_resp.index[frame_resp[resp_col] == encoded_classes[0]].tolist()  # isolate the uniques values for each group of the two response in the population\n\tsen_indexes = frame_resp.index[frame_resp[resp_col] == encoded_classes[1]].tolist()\n\t# considering one feature, the 2 groups values can be uniquely described by one unique value each : prfect p_value attributed if the 2 values are different; worst p_value attributed if the 2 values are identifical...\n\tp_val_top = -1  # ...the p_value value for the perfect differenciation then\n\tp_val_worst = 1  # ...the p_value value for the worst differenciation then\n\tif feature_val_type == \"cat\" : # cases of discrete values, do a fisher exact test on contigency table of fts var with response and extract pvalues\n\t\tfor feat in list(frame_fts):\n\t\t\tthe_resp_samples = frame_fts.loc[res_indexes, [feat]]\n\t\t\tthe_sen_samples = frame_fts.loc[sen_indexes, [feat]]\n\t\t\tif (the_resp_samples[feat].nunique(dropna=True) == 1) & (the_sen_samples[feat].nunique(dropna=True) == 1) : # the 2 groups values are uniquely described\n\t\t\t\tif the_resp_samples[feat].unique()[0] != the_sen_samples[feat].unique()[0]: # by a different unique value\n\t\t\t\t\tdict_pvalues[feat] = p_val_top\n\t\t\t\telse:\n\t\t\t\t\tdict_pvalues[feat] = p_val_worst\n\t\t\telse : # not just one unique value exist in each group, the fisher exacttest is done then\n\t\t\t\tthe_feat_col = pd.Categorical(frame_fts[feat],categories=[0,1])\n\t\t\t\tthe_resp_col = pd.Categorical(frame_resp[resp_col],categories=encoded_classes) ## older [\"Res\",\"Sen\"] is now encoded_classes (an array of shape (2,) ##!! externalise to the for loop to do it only one time ##!! use categories list instead of hard code\n\t\t\t\tcontingency_table_filled = pd.crosstab(the_feat_col,the_resp_col,dropna=False) # dropna=False is used to tell to the system to still count classes that were not predicted\n\t\t\t\toddsratio, p_value_f = scipy.stats.fisher_exact(contingency_table_filled, alternative=\"two-sided\")\n\t\t\t\tdict_pvalues[feat] = p_value_f\n\telse : # case of reals values, find the two extreme situations of response segregation ability by the feature or do a t test\n\t\tfor feat in list(frame_fts):\n\t\t\tthe_resp_samples = frame_fts.loc[res_indexes,[feat]]\n\t\t\tthe_sen_samples = frame_fts.loc[sen_indexes, [feat]]\n\t\t\tif (the_resp_samples[feat].nunique(dropna=True) == 1) & (the_sen_samples[feat].nunique(dropna=True) == 1) : # True is the 2 groups values are uniquely described by a different unique value each...\n\t\t\t\tif the_resp_samples[feat].unique()[0] != the_sen_samples[feat].unique()[0]: # ...True if those two values are the same\n\t\t\t\t\tdict_pvalues[feat] = p_val_top\n\t\t\t\telse:\t\t\t\t\t# ....case where those two values are not the same\n\t\t\t\t\tdict_pvalues[feat] = p_val_worst\n\t\t\telse: # not just one unique value exist in each group, the t-test is done then\n\t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples, the_sen_samples, equal_var=False) #old line 0\n\t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples.dropna()[feat], the_sen_samples.dropna()[feat], equal_var=False) #old line 1.5\n\t\t\t\t# t_stat, p_value_t = scipy.stats.ttest_ind(the_resp_samples.iloc[:,0].tolist(),the_sen_samples.iloc[:,0].tolist(),equal_var = False,nan_policy='propagate') # old line 1\n\t\t\t\tt_stat, p_value_t = scipy.stats.ttest_ind(np.array(the_resp_samples.iloc[:, 0].tolist()), np.array(the_sen_samples.iloc[:, 0].tolist()), equal_var=False, nan_policy='omit')\n\t\t\t\t##!! transforming a column to an array is done by putting it as a list and then as a numpy array\n\t\t\t\t# ttest_ind suppose variance equals by default so underestimates p for unequal variances even if the t-statistic is the same (eg : case where size of samples are equals but on different scale :;\n\t\t\t\t# When n1 != n2, the equal variance t-statistic is no longer equal to the unequal variance t-statistic:\n\t\t\t\t# so we use  equal_var = False\n\t\t\t\t## !!! to try : make a condittion if variance and size are equals, use equal_var = True else use equal_var = False\n\t\t\t\t# nan_policy='propagate' to take into account the nan and 'omit' to not take them into account\n\t\t\t\tdict_pvalues[feat] = p_value_t\n\t# sorted_feats_by_pval = sorted(dict_pvalues, key=dict_pvalues.get, reverse=False) # deprecated as not working properly\n\tsorted_feats_by_pval = [key for key, value in sorted(dict_pvalues.items(), key=itemgetter(1), reverse=False)] # default is reverse = False so can omit it\n\tsorted_pvals = [value for key, value in sorted(dict_pvalues.items(), key=itemgetter(1), reverse=False)]  # to report later on results # default is reverse = False so can omit it\n\treturn dict_pvalues,sorted_feats_by_pval,sorted_pvals\n\n#----------go through the coupled value of mc and mcc and find the best one ie the OMC\ndef OMC_founder_in_dict_MCs_MCCs(dict_MCs_MCCs):\n\tOMC = list(dict_MCs_MCCs.keys())[0]  # allfts model complexity in fts by default taken as the OMC\n\tomc_mdl_mcc = list(dict_MCs_MCCs.values())[0]\n\tfor mc_as_key in list(dict_MCs_MCCs.keys())[1:]:  #  loop on the rest of the mdl_complexities\n\t\tif dict_MCs_MCCs[mc_as_key] > omc_mdl_mcc:\n\t\t\tOMC = mc_as_key\n\t\t\tomc_mdl_mcc = dict_MCs_MCCs[mc_as_key]\n\t\telif dict_MCs_MCCs[mc_as_key] == omc_mdl_mcc:\n\t\t\tif mc_as_key < OMC:\n\t\t\t\tOMC = mc_as_key\n\t\t\t# no need to attribute the correspondant value of mcc because an equal value is already there\n\t\t\telse:\n\t\t\t\tpass\n\t\telse:  # case of dict_of_mcc_values_by_mdl_to_update[mc_as_key] < omc_mdl_mcc\n\t\t\tpass\n\t# the OMC and its mcc variables is updated\n\treturn OMC,omc_mdl_mcc\n\n#-------function to get persistent features in list of lists\ndef make_list_of_persistents_fts_from_list_of_lists_of_fts(a_list_of_lists_of_fts):\n\tlist_of_persistent_fts = []\n\tfor list in a_list_of_lists_of_fts:\n\t\tfor feat in list:\n\t\t\tif feat not in list_of_persistent_fts:\n\t\t\t\tlist_of_persistent_fts.append(feat)\n#==================== classification used functions  (uncomment to use)\n\n#==================== regression used functions  (uncomment to use)\n#\n# #-------------------------------functions to select features (following a univariate test, a dict feat-p-value in test is created)\n# # lets define a function taking as arg :\n# # - presently computed featuretype (feature_type)\n# # - the part of the training folds frame with only the features columns (feats_frame)\n# # - the part of the training folds frame with only the resp column (frame_resp_col)\n# def feat_selection1(feature_type, feats_frame, frame_resp_col):\n# \tresults_fs = {}  # Feature selection output as a dict {feat-pvalue}\n#\n# \tif feature_type == \"SNV\" or feature_type == \"CNA\":  # t-test (for discrte values feats use Wilcoxonrank-sum test)\n# \t\tfor somecolumn in list(feats_frame): # loop on the list of the feats (list of the columns names)\n# \t\t\tmutindexes = feats_frame[somecolumn].loc[feats_frame[somecolumn] == True].index # give a list of indexes for the variant # strategy : for the whole frame get the lines having a given value; restrict that to the  frame but only cionsidering one column ; get indexes of the resulting lines (not needed to resptrict herte but it shows that in the spirit of the work only the resp column matter atthis point)\n# \t\t\twtindexes = feats_frame[somecolumn].loc[feats_frame[somecolumn] == False].index # give a list of indexes for the wt\n# \t\t\tpval = 1\n#\n# \t\t\ttry:  # NumPy wilcoxon not used due to it being nominal approximation  # to review and understand\n# \t\t\t\twilcoxon2 = robjects.r['wilcox.test']\n# \t\t\t\tv12 = robjects.FloatVector(list(frame_resp_col[mutindexes].values)) # the resp values for the mutated indexes\n# \t\t\t\tv22 = robjects.FloatVector(list(frame_resp_col[wtindexes].values)) # the resp values for the wt indexes\n# \t\t\t\twilcox_result2 = wilcoxon2(v12, v22, conf_int=True)\n# \t\t\t\tpval = wilcox_result2.rx2(\"p.value\")[0]\n# \t\t\texcept RRuntimeError:\n# \t\t\t\tpass  # If always mutated or not mutated, it is not single-handedly correlated with response\n# \t\t\tresults_fs[somecolumn] = pval  # Add p-values to dictionary\n# \telse:  # spearman test  (for real values feats use corr coef of the spearman test) (does a test of correlation between 2 variable a and b : a is the presently considered column (feat.) and b is the resp column\n# \t\tfor somecolumn in list(feats_frame):\n# \t\t\ttry:\n# \t\t\t\ttestresult = scipy.stats.spearmanr(list(feats_frame[somecolumn].values), frame_resp_col, axis=0, nan_policy='propagate')\n# \t\t\t\t# in the var testresult, we have a tuple of [0] = correlation and [1] = pvalue.\n# \t\t\t\t# lets extract the pvalue and add it as an entry of the dict resultfs\n# \t\t\t\tresults_fs[somecolumn] = testresult[1]\n# \t\t\texcept FloatingPointError:\n# \t\t\t\tpass  # Same issue as for Wilcoxon, if always mutated or not, it alone is not correlated\n#\n# \t# sorted_fs = []\n# \tsorted_fs = sorted(results_fs, key=results_fs.get, reverse=False) # une liste des cles du dict (les features) ordonne de maniere croissante de la valeur de p-value\n# \treturn results_fs, sorted_fs\n# \t# return results_fs, sorted(results_fs, key=results_fs.get, reverse=False)\n#====================for regression (uncomment to use)\n\n\n\n\n\n","repo_name":"Dioufamad/ATIP3_intro","sub_path":"CICS_dev_version/cics_engines/fs_engine.py","file_name":"fs_engine.py","file_ext":"py","file_size_in_byte":15655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10138964360","text":"h=list(map(int,input().split()))\nx=list(map(int,input().split()))\nlengthH = len(h)\nlengthX = len(x)\nn= lengthH + lengthX - 1\n\ndef getValue(a,y):\n    if 0<=y<len(a):\n        return a[y] \n    else:\n        return 0\n\noutputList=[]\n\nfor i in range(n):\n    t=0\n    for k in range(len(h)):\n        t+=getValue(x,k)*getValue(h,i-k)\n    outputList.append(t)\n\nprint(outputList)\n","repo_name":"grejojoby/dsip-experiments","sub_path":"linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71426940089","text":"import pandas as pd\nimport numpy as np\nimport gc\nimport random\n\ndtypes = {\n    'click_id':'uint32',\n    'ip':'uint32',\n    'app':'uint16',\n    'device':'uint16',\n    'os':'uint16',\n    'channel':'uint16',\n    'is_attributed':'uint8'\n}\n\nuse_col = ['ip', 'app', 'os', 'device', 'channel']\n\nheader = ['ip_uniq_app','ip_os_device_uniq_app','ip_uniq_device','ip_uniq_channel','ip_app_uniq_os','app_uniq_channel']\n\ntrain1 = pd.read_csv('../feature/train-day6.csv', dtype=dtypes, usecols=use_col)\ntrain2 = pd.read_csv('../feature/train-day7.csv', dtype=dtypes, usecols=use_col)\ntest = pd.read_csv('../feature/test.csv', dtype=dtypes, usecols=use_col)\ndataset = [train1, train2, test]\ndel train1, train2, test\ngc.collect()\n\ndict = {1:'train-day6', 2:'train-day7', 3:'test'}\n\ndef countuniq(data, group, counted):\n    agg_name = '_'.join(group)+'_uniq_'+counted\n    uniq = data[group+[counted]].groupby(by=group)[counted].nunique().astype('uint32').reset_index().rename(columns={counted:agg_name})\n    data = data.merge(uniq, on=group, how='left')\n    del uniq\n    gc.collect()\n    return data\n\ncount = 0\nfor data in dataset:\n    count += 1\n    data = countuniq(data, ['ip'], 'app')\n    data = countuniq(data, ['ip','os','device'], 'app')\n    data = countuniq(data, ['ip'], 'device')\n    data = countuniq(data, ['ip'], 'channel')\n    data = countuniq(data, ['ip', 'app'], 'os')\n    data = countuniq(data, ['app'], 'channel')\n    data.to_csv(\"../feature/\"+dict[count]+\"-uniq.csv\", columns = header, index=False)\n    print(\"Finished!\")\n","repo_name":"xuebai1990/TalkingData-Kaggle","sub_path":"generate/GenerateUnique.py","file_name":"GenerateUnique.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24634952027","text":"import tensorflow\nfrom django.shortcuts import render\nfrom django.core.files.storage import FileSystemStorage \nfrom tensorflow import keras as k\nimport json\nimport numpy as np\n# Create your views here.\n\n#loading the model:\n\nmodel=k.models.load_model(r'D:\\D_Apps\\RockPaperScissorsClassifier\\models\\thismodel.h5')\n\n\n\ndef index(request):\n    context={'a': 1}\n    return render(request,'index.html',context)\ndef predictImage(request):\n    print(request)\n    fileObj=request.FILES['filePath']\n    fsObj=FileSystemStorage()\n    filePathName=fsObj.save(fileObj.name,fileObj)\n    filePathName=fsObj.url(filePathName)\n    testimage  = '.'+filePathName\n\n\n    #taking image and loading:\n    img = k.preprocessing.image.load_img(testimage, target_size= (28,28))\n    x = k.preprocessing.image.img_to_array(img)\n    x=x/255\n    x=x.reshape(1,28, 28,3)\n    l = model.predict(x)\n    b = l[0]\n    this = b.argmax()\n    if(this == 0):\n        predictedLabel = \"Rock\"\n    else:\n        if(this == 1):\n            predictedLabel = \"Paper\"\n        else:\n            if(this == 2):\n                predictedLabel = \"Scissors\"\n    print(predictedLabel)\n\n    \n\n    context={'filePathName':filePathName, 'predictedLabel': predictedLabel}\n    return render(request,'index.html',context)","repo_name":"Ankit-ChAWLA/Rock-Paper-Scissors-Classifier","sub_path":"Classifier/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33514252049","text":"import numpy as np\nimport math\nfrom sklearn.cross_validation import StratifiedKFold, cross_val_score\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom sklearn import cross_validation, grid_search\n\n\nclass SemiSupervisedLearning:\n    def __init__(self, traindata, testdata, target, C_range = 10 ** np.arange(3,7, 0.5), gamma_range = 10 ** np.arange(0, -2, -0.25)):\n\n        if 3*len(traindata) > len(testdata):\n            raise Exception(\"This technique is only useful if test data is much bigger than training data.\")\n        self._traindata = traindata\n        self._testdata = testdata\n        self.target = target\n        self.C_range = C_range\n        self.gamma_range = gamma_range\n        self.dimreduce()\n\n    def dimreduce(self, dims = 10):\n        pca = PCA(n_components=dims, whiten=True)\n        self._traindata_comp = pca.fit_transform(self._traindata)\n        self._testdata_comp = pca.transform(self._testdata)\n\n    def train(self, threshold = 0.97):\n        params = [{'C':self.C_range, 'gamma':self.gamma_range}]\n        cvk = StratifiedKFold(self.target, n_folds=5)\n        svc = SVC(kernel = 'rbf', probability=True)\n        clf = grid_search.GridSearchCV(svc, param_grid=params)\n        clf.fit(self._traindata_comp, self.target)\n        score = cross_val_score(clf.best_estimator_, self._traindata_comp, self.target, cv = 20)\n        print(\"best classifier:\" , clf.best_estimator_)\n\n        test_prob_values = clf.best_estimator_.predict_proba(self._testdata_comp)\n\n        max_values = map(lambda x: max(x[0],x[1]), test_prob_values)\n        indices = np.where(np.asarray(max_values) > threshold)\n\n        C_range2 = 10.0 ** np.arange(6.75,7.5,.25)\n        gamma_range2 = 10.0 ** np.arange(-1.5,0,.05)\n\n        params2 = [{'C':C_range2, 'gamma':gamma_range2}]\n\n        self.clf2 = grid_search.GridSearchCV(svc, param_grid=params2,cv = cvk)\n\n        test_set_comp_subset = self._testdata_comp[indices]\n        target_test_subset = clf.best_estimator_.predict(test_set_comp_subset)\n\n        train_set_second = np.concatenate((self._traindata_comp, test_set_comp_subset),axis= 0)\n        target_second = np.concatenate((self.target, target_test_subset), axis = 0)\n\n        self.clf2.fit(train_set_second, target_second)\n        score2 = cross_val_score(self.clf2.best_estimator_, train_set_second, target_second, cv = 20)\n        print(\"best classifier:\", self.clf2.best_estimator_)\n        return self.clf2\n\n\n\n\n","repo_name":"adhaka/kaggle-challenges","sub_path":"datascience-london/SemiSupervisedLearning.py","file_name":"SemiSupervisedLearning.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23742859329","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport warnings\nfrom collections import OrderedDict\n\nimport math\n\nwarnings.filterwarnings('ignore')\n__author__ = 'yangsheng'\n\nDICT_PLATFORM = OrderedDict({\n                     '8916':['Y31', 'Y51', 'Y35A'],\n                     '8937':['Y55A', 'Y66'],\n                     '8939':['V3MaxA', 'V3', 'X6A', 'X6PlusA', 'X5ProV', 'Y37'],\n                     '6735':['Y33', 'Y35'],\n                     '6750':['V3MA', 'Y67'],\n                     '6752':['X5ProD', 'X6D', 'X6PlusD'],\n                     '8953':['X9', 'X9i'],\n                     '8976':['X9Plus','X6SA', 'X6SPlusA', 'X6SPlusD', 'X7', 'X7Plus', 'Xplay5A'],\n                     '8996':['Xplay5S', 'Xplay6']})\nDICT_MOD_PLAT = {}\nfor plat, mods in DICT_PLATFORM.items():\n    for mod in mods:\n        DICT_MOD_PLAT[mod] = plat\n\n\n\n\n\nMODELS = ['X6A', 'X6D', 'X6PlusA', 'X6PlusD', \"Y51\", 'X5L', 'X5Max+', 'X5MaxL', 'X5MaxV', 'X5M', 'X5ProD',\n 'X5ProD25', 'X5ProV',\n 'X5SL', 'X5V', 'Xplay3S', 'Xshot', 'Y13iL', 'Y13L', 'Y22iL', 'Y22L', 'Y23L', 'Y27',\n 'Y28L', 'Y29L', \"Y31\", 'Y33', 'Y37', 'Y35', 'Y35A', 'Xplay5A', 'X6SA', 'X6SPlusA', 'X6SPlusD', 'V3MaxA', 'V3',\n 'V3MA',\n 'Xplay5S', 'X7', 'X7Plus', 'Y55A', 'X9', 'Xplay6', 'Y67', 'X9Plus', 'X9i', 'Y66']\n\ndef ver_to_num(version):\n    '''\n    将软件版本号转化为数值，用于比较版本号大小\n    版本号越大，获得返回值越大\n    非法版本号将返回值 0\n    '''\n    arr = version.split(\".\")\n    curbit = 0\n    ver_num = 0\n    for num in arr[-1::-1]:\n        #08.06.03 这种版本号视作非法\n        if num and len(num) > 1 and num[0] == '0':\n            return 0\n        try:\n            ver_num += int(num) * math.pow(100, curbit)\n            curbit += 1\n        except Exception:\n            return 0\n    return ver_num\n\n\ndef get_series(modelname):\n    modelname = modelname.replace(' ', '').lower()\n    if modelname.startswith('vivoxplay'):\n        return 'Xplay系列'\n    elif modelname.startswith('vivox'):\n        return 'X系列'\n    elif modelname.startswith('vivoy'):\n        return 'Y系列'\n    elif modelname.startswith('vivov'):\n        return 'V系列'\n    else:\n        return '其他'\n\n","repo_name":"wangsanshi123/scrapy_test1","sub_path":"libs/vivoutil.py","file_name":"vivoutil.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"35585745779","text":"#!/usr/bin/python3\n\n# Adobe pdfMark Reference\n# https://opensource.adobe.com/dc-acrobat-sdk-docs/acrobatsdk/pdfs/acrobatsdk_pdfmark.pdf\n\n# in toc file, you must close the parenthesis()!! otherwise, gs fails.\n\n\nimport sys\nimport re\nimport subprocess\nimport tempfile, os, sys\nimport argparse\n\nclass Entry():\n\n    def __init__(self, name, page, children):\n        self.name = name\n        self.page = page\n        self.children = children # Entry list\n\n    def pritty_print(self, depth):\n        print(depth * '  ' + self.name + ':' + str(self.page))\n        for c in self.children:\n            c.pritty_print(depth+1)\n\n\n# Parse the start of the line for whitespace characters and return \"tab\";\n# should only be called once on first occurrence of an indent while tab == \"\"\ndef parse_tab(line):\n    tab = \"\"\n\n    # add whitespace characters to tab\n    for ch in line:\n        if (ch.isspace()):\n            tab += ch\n        else:\n            break\n\n    return tab\n\n\ndef toc_to_elist(toc):\n\n    tab = \"\" # indentation character(s) evaluated and assigned to this later\n    lines = toc.split('\\n')\n    cur_entry = [[]] # current entries by depth\n    offset = 0\n\n    for line in lines:\n\n        # if indentation style hasn't been evaluated yet and the line starts\n        # with a whitespace character, assume its an indent and assign all the\n        # leading whitespace to tab\n        if ((tab == \"\") and (line != \"\") and (line[0].isspace())):\n            tab = parse_tab(line)\n\n        depth = 0\n\n        # determine depth level of indent\n        if (tab != \"\"):\n            # find length of leading whitespace in string\n            ws_len = 0\n            for ch in line:\n                if (ch.isspace()):\n                    ws_len += 1\n                else:\n                    break\n\n            # count indent level up to first non-whitespace character;\n            # allows for \"indents\" to appear inside section titles e.g. if an\n            # indent level of a single space was chosen\n            depth = line.count(tab, 0, ws_len)\n\n        line = line.split('#')[0].strip() # strip comments and white spaces\n\n        if not line:\n            continue\n\n        if line[0] == '+':\n            offset += int(line[1:])\n            continue\n\n        if line[0] == '-':\n            offset -= int(line[1:])\n            continue\n\n        try:\n            (name, page) = re.findall(r'(.*) (\\d+)$', line)[0]\n            page = int(page) + offset\n            cur_entry = cur_entry[:depth+1] + [[]]\n            cur_entry[depth].append(Entry(name, page, cur_entry[depth+1]))\n\n        except:\n            # todo display line number\n            print('syntax error in toc-file. line:\\n' + line)\n            exit(1)\n\n    return cur_entry[0]\n\n\ndef elist_to_gs(elist):\n\n    def rec_elist_to_gslist(elist):\n        gs_list = []\n        for entry in elist:\n            gs_list.append(\"[/Page %d /View [/XYZ null null null] /Title <%s> /Count %d /OUT pdfmark\" \\\n                    % (entry.page, entry.name.encode(\"utf-16\").hex(), len(entry.children)))\n            gs_list += rec_elist_to_gslist(entry.children)\n        return gs_list\n\n    return '\\n'.join(rec_elist_to_gslist(elist))\n\n\ndef pdfoutline(inpdf, tocfilename, outpdf, gs='gs'):\n\n    with open(tocfilename) as f:\n        toc = f.read()\n        gs_command = elist_to_gs(toc_to_elist(toc))\n\n    tmp = tempfile.NamedTemporaryFile(mode = 'w', delete=False)\n    tmp.write(gs_command)\n    tmp.close()\n\n    process = subprocess.Popen(\\\n        [gs, '-o', outpdf, '-sDEVICE=pdfwrite', tmp.name, '-f', inpdf],\\\n        stdout=subprocess.PIPE)\n\n    # show progress bar\n    totalPage = 0\n    for line in process.stdout:\n        tot = re.findall(r'Processing pages 1 through (\\d+)', line.decode('ascii'))\n        if tot:\n            totalPage = int(tot[0])\n            printProgressBar(0, totalPage)\n            break\n\n    for line in process.stdout:\n        currentPage = re.findall(r'Page (\\d+)', line.decode('ascii').strip())\n        if currentPage:\n            printProgressBar(int(currentPage[0]), totalPage)\n\n    os.unlink(tmp.name)\n\n\n# https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\ndef printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 50, fill = '█', printEnd = \"\\r\"):\n    \"\"\"\n    Call in a loop to create terminal progress bar\n    @params:\n        iteration   - Required  : current iteration (Int)\n        total       - Required  : total iterations (Int)\n        prefix      - Optional  : prefix string (Str)\n        suffix      - Optional  : suffix string (Str)\n        decimals    - Optional  : positive number of decimals in percent complete (Int)\n        length      - Optional  : character length of bar (Int)\n        fill        - Optional  : bar fill character (Str)\n        printEnd    - Optional  : end character (e.g. \"\\r\", \"\\r\\n\") (Str)\n    \"\"\"\n    percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n    filledLength = int(length * iteration // total)\n    bar = fill * filledLength + '-' * (length - filledLength)\n    print('\\r%s |%s| %d/%d %s' % (prefix, bar, iteration, total, suffix), end = printEnd)\n    # Print New Line on Complete\n    if iteration == total:\n        print()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(prog=\"pdfoutline.py\", description='add table of contents to a pdf')\n\n    parser.add_argument('in_pdf', metavar = 'in.pdf', help = 'the input pdf file')\n    parser.add_argument('in_toc', metavar = 'in.toc', help = ' a table of contents file in the specified format ')\n    parser.add_argument('out_pdf', metavar = 'out.pdf', help = ' the output pdf file')\n    parser.add_argument('-g', '--gs_path', type=str, help = \"Path to the ghostscript executable\")\n\n    args = parser.parse_args()\n\n    if args.in_pdf == args.out_pdf:\n        print('Specify different names for input and output files.')\n        sys.exit()\n    if args.gs_path :\n        pdfoutline(args.in_pdf, args.in_toc, args.out_pdf, args.gs_path)\n    else:\n        pdfoutline(args.in_pdf, args.in_toc, args.out_pdf)\n","repo_name":"yutayamamoto/pdfoutline","sub_path":"pdfoutline.py","file_name":"pdfoutline.py","file_ext":"py","file_size_in_byte":6073,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"77"}
+{"seq_id":"4287267552","text":"from levelupapi.views.game import GameSerializer\nfrom levelupapi.views.event import EventSerializer\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import ValidationError\nfrom django.http import HttpResponseServerError\nfrom rest_framework import status\nfrom rest_framework.decorators import action\nfrom rest_framework.viewsets import ViewSet\nfrom rest_framework.response import Response\nfrom rest_framework import serializers\nfrom levelupapi.models import Event, Gamer, Game\n\nclass Profiles(ViewSet):\n    # handle GET request to profile resource, returns JSON of User info and events\n    def list(self, request):\n        gamer = Gamer.objects.get(user=request.auth.user)\n        events = Event.objects.filter(eventgamer__gamer=gamer)\n\n        events = EventSerializer(\n            events, many=True, context={'request': request})\n        gamer = GamerSerializer(\n            gamer, many=False, context={'request': request})\n        # there is NO MODEL for Profile so we gotta make an obj fr. scatch\n        profile = {}\n        profile['gamer'] = gamer.data\n        profile['events'] = events.data\n\n        return Response(profile)\n\nclass UserSerializer(serializers.ModelSerializer):\n    # JSON serializer for gamer's related DJANGO 'User'\n    class Meta:\n        model = User\n        fields = ('first_name', 'last_name', 'username')\n\nclass GamerSerializer(serializers.ModelSerializer):\n    # JSON serilizer for gamers\n    user = UserSerializer(many=False)\n\n    class Meta:\n        model = Gamer\n        fields = ('user', 'bio')\n\nclass GameSerializer(serializers.ModelSerializer):\n    # JSON serializer for games, not sure why exactly we are declaring it since not used yet as of chapter 13\n    class Meta:\n        model = Game\n        fields = ('title', )\n\nclass EventSerializer(serializers.ModelSerializer):\n    \"\"\"JSON serializer for events\"\"\"\n    game = GameSerializer(many=False)\n\n    class Meta:\n        model = Event\n        fields = ('id', 'game', 'location', 'event_time')","repo_name":"bschweiz/levelup","sub_path":"levelupapi/views/profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"426439669","text":"def resolve():\n    '''\n    code here\n    '''\n    import collections\n\n    N, M = [int(item) for item in input().split()]\n    path_list = [[int(item) for item in input().split()] for _ in range(M)]\n\n\n    edges = [[] for _ in range(N+1)]\n\n    for a, b in path_list:\n        edges[a].append(b)\n        edges[b].append(a)\n\n    stack = collections.deque()\n    fp = [0 for _ in range(N+1)]\n\n    def dfs(start):\n        prev = -1\n        stack.append([start, prev])\n\n        is_roop = False\n        while stack:\n            temp_node, prev = stack.pop()\n            next_edges = edges[temp_node]\n            fp[temp_node] = 1\n            # print('temp',temp_node)\n            # print('prev', prev)\n            if next_edges != []:\n                for next_node in next_edges:\n                    # print('next', next_node)\n                    if next_node != prev:\n                        if fp[next_node] == 1:\n                            is_roop = True\n                            # print(is_roop)\n                            pass\n                        else:\n                            stack.append([next_node, temp_node])\n\n\n        return False if is_roop else True\n\n    cnt = 0\n    for i in range(1,N+1):\n        # print('start', i)\n        # print('-------')\n        if fp[i] == 0:\n            if dfs(i):\n                cnt += 1\n    print(cnt)\n\nif __name__ == \"__main__\":\n    resolve()\n","repo_name":"staguchi0703/ant_book_dfs_bfs","sub_path":"ARC037B/resolve.py","file_name":"resolve.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36995278827","text":"# 3번의 기회\n# 0~10점\n# S : 1제곱 / D : 2제곱 / T : 3제곱\n# * : 해당점수 & 바로 전에 얻은 점수 2배 / 처음에 나오면 첫 점수만 2배(중첩가능)\n# # : 해당 점수 마이너스\n# *와 # 중첩시 : -2배\ndef SDT (lists) :\n    x = []\n    for l,i in enumerate(lists) :\n        if i == 'S' :\n            x.append(int(lists[l-1]))\n        elif i == 'D' :\n            x.append(int(lists[l-1]) ** 2)\n        elif i == 'T' :\n            x.append(int(lists[l-1]) ** 3)\n        elif i == '#' :\n            x[-1] = x[-1]*(-1)\n        elif i == '*' :\n            if len(x) == 1 :\n                x[0] = x[0]*2\n            elif len(x) > 1 :\n                x[-1] = x[-1]*2\n                x[-2] = x[-2]*2       \n    return x\ndef solution(dartResult):\n    lists = []\n    numbers = [str(range(0,11))]\n    for i in dartResult :\n        lists.append(i)\n    for i,j in enumerate(lists) :\n        if lists[i] == '1' and lists[i+1] == '0' :\n            del lists[i]\n            lists[i] = '10'\n    answer = sum(SDT(lists))\n    return answer","repo_name":"KimJinHye0n/python","sub_path":"Programmaers/Level1/[1차] 다트게임/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29426307519","text":"def numero_perfecto(a):\n    contador = a\n    suma = 0\n    while(contador != 1):\n        divisor = a/contador\n        if(int(divisor) == divisor):\n            suma = suma + divisor\n            contador = contador - 1\n        else:\n            contador = contador - 1\n    if(a == suma ):\n        return True\n    else:\n        return False\n\nif __name__==\"__main__\":\n    a=int(input(\"Ingrese a: \"))\n    print(numero_perfecto(a))\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej3/tema2_ej3_7b030bd3df0d0823a6ec8069cd0947bc.py","file_name":"tema2_ej3_7b030bd3df0d0823a6ec8069cd0947bc.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7229637966","text":"from math import exp\nimport torch\nfrom torch.autograd import Variable\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.modules.loss import _Loss\n\n\nclass CharbonnierLoss(nn.Module):\n    \"\"\"Charbonnier Loss (L1)\"\"\"\n\n    def __init__(self, eps=1e-3):\n        super(CharbonnierLoss, self).__init__()\n        self.eps = eps\n\n    def forward(self, x, y):\n        diff = x - y\n        loss = torch.mean(torch.sqrt((diff * diff) + (self.eps * self.eps)))\n        return loss\n\n\nclass EdgeLoss(nn.Module):\n    def __init__(self):\n        super(EdgeLoss, self).__init__()\n        k = torch.Tensor([[0.05, 0.25, 0.4, 0.25, 0.05]])\n        self.kernel = torch.matmul(k.t(), k).unsqueeze(0).repeat(3, 1, 1, 1)\n        if torch.cuda.is_available():\n            self.kernel = self.kernel.cuda()\n        self.loss = CharbonnierLoss()\n\n    def conv_gauss(self, img):\n        n_channels, _, kw, kh = self.kernel.shape\n        img = F.pad(img, (kw // 2, kh // 2, kw // 2, kh // 2), mode=\"replicate\")\n        return F.conv2d(img, self.kernel, groups=n_channels)\n\n    def laplacian_kernel(self, current):\n        filtered = self.conv_gauss(current)  # filter\n        down = filtered[:, :, ::2, ::2]  # downsample\n        new_filter = torch.zeros_like(filtered)\n        new_filter[:, :, ::2, ::2] = down * 4  # upsample\n        filtered = self.conv_gauss(new_filter)  # filter\n        diff = current - filtered\n        return diff\n\n    def forward(self, x, y):\n        loss = self.loss(self.laplacian_kernel(x), self.laplacian_kernel(y))\n        return loss\n\n\ndef gaussian(window_size, sigma):\n    gauss = torch.Tensor(\n        [\n            exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2))\n            for x in range(window_size)\n        ]\n    )\n    return gauss / gauss.sum()\n\n\ndef create_window(window_size, sigma, channel):\n    _1D_window = gaussian(window_size, sigma).unsqueeze(1)\n    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)\n    window = Variable(\n        _2D_window.expand(channel, 1, window_size, window_size).contiguous()\n    )\n    return window\n\n\nclass MS_SSIM(_Loss):\n    def __init__(self, size_average=True, max_val=255):\n        super(MS_SSIM, self).__init__(size_average=None, reduce=None, reduction=\"mean\")\n        self.size_average = size_average\n        self.channel = 3\n        self.max_val = max_val\n\n    def _ssim(self, img1, img2, size_average=True):\n        _, c, w, h = img1.size()\n        window_size = min(w, h, 11)\n        sigma = 1.5 * window_size / 11\n        window = create_window(window_size, sigma, self.channel).cuda()\n        mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=self.channel)\n        mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=self.channel)\n\n        mu1_sq = mu1.pow(2)\n        mu2_sq = mu2.pow(2)\n        mu1_mu2 = mu1 * mu2\n\n        sigma1_sq = (\n            F.conv2d(img1 * img1, window, padding=window_size // 2, groups=self.channel)\n            - mu1_sq\n        )\n        sigma2_sq = (\n            F.conv2d(img2 * img2, window, padding=window_size // 2, groups=self.channel)\n            - mu2_sq\n        )\n        sigma12 = (\n            F.conv2d(img1 * img2, window, padding=window_size // 2, groups=self.channel)\n            - mu1_mu2\n        )\n        C1 = (0.01 * self.max_val) ** 2\n        C2 = (0.03 * self.max_val) ** 2\n        V1 = 2.0 * sigma12 + C2\n        V2 = sigma1_sq + sigma2_sq + C2\n        ssim_map = ((2 * mu1_mu2 + C1) * V1) / ((mu1_sq + mu2_sq + C1) * V2)\n        mcs_map = V1 / V2\n        if size_average:\n            return ssim_map.mean(), mcs_map.mean()\n\n    def ms_ssim(self, img1, img2, levels=5):\n        weight = Variable(torch.Tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).cuda())\n        msssim = Variable(\n            torch.Tensor(\n                levels,\n            ).cuda()\n        )\n        mcs = Variable(\n            torch.Tensor(\n                levels,\n            ).cuda()\n        )\n        for i in range(levels):\n            ssim_map, mcs_map = self._ssim(img1, img2)\n            msssim[i] = ssim_map\n            mcs[i] = mcs_map\n            filtered_im1 = F.avg_pool2d(img1, kernel_size=2, stride=2)\n            filtered_im2 = F.avg_pool2d(img2, kernel_size=2, stride=2)\n            img1 = filtered_im1\n            img2 = filtered_im2\n        value = torch.prod(mcs[0 : levels - 1] ** weight[0 : levels - 1]) * (\n            msssim[levels - 1] ** weight[levels - 1]\n        )\n        return value\n\n    def forward(self, img1, img2):\n        return 0.16 * F.l1_loss(img1, img2) + 0.84 * (1 - self.ms_ssim(img1, img2))\n\n\nclass HINetLoss(nn.Module):\n    def __init__(self):\n        super(HINetLoss, self).__init__()\n        k = torch.Tensor([[0.05, 0.25, 0.4, 0.25, 0.05]])\n        self.kernel = torch.matmul(k.t(), k).unsqueeze(0).repeat(3, 1, 1, 1)\n        if torch.cuda.is_available():\n            self.kernel = self.kernel.cuda()\n        self.charbonnier_loss_fn = CharbonnierLoss()\n        self.ms_ssim_loss_fn = MS_SSIM()\n\n    def forward(self, x, y):\n        loss = (\n            self.charbonnier_loss_fn(x[0], y)\n            + self.charbonnier_loss_fn(x[1], y)\n            + 5 * (1 - self.ms_ssim_loss_fn.ms_ssim(x[0], y))\n            + 5 * (1 - self.ms_ssim_loss_fn.ms_ssim(x[1], y))\n        )\n        return 2 * loss\n\n\nclass GANLoss(nn.Module):\n    def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0):\n        super(GANLoss, self).__init__()\n        self.register_buffer(\"real_label\", torch.tensor(target_real_label))\n        self.register_buffer(\"fake_label\", torch.tensor(target_fake_label))\n        if use_lsgan:\n            self.loss = nn.MSELoss()\n        else:\n            self.loss = nn.BCELoss()\n\n    def get_target_tensor(self, input, target_is_real):\n        if target_is_real:\n            target_tensor = self.real_label\n        else:\n            target_tensor = self.fake_label\n        return target_tensor.expand_as(input)\n\n    def __call__(self, input, target_is_real):\n        target_tensor = self.get_target_tensor(input, target_is_real)\n        return self.loss(input, target_tensor)\n","repo_name":"TeamBCP5/image-reconstruction","sub_path":"utils/criterions.py","file_name":"criterions.py","file_ext":"py","file_size_in_byte":6111,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"33913402252","text":"from JMRPiFoundations.Skeleton.RPiSparkModule import RPiSparkModule\nimport Adafruit_BMP.BMP085 as BMP085 # Imports the BMP library\n\nclass BMP180(RPiSparkModule):\n    BMPSensor = None\n    \n    def setup(self):\n        # Create an 'object' containing the BMP180 data\n        self.BMPSensor = BMP085.BMP085()\n\n    def run(self):\n        self.RPiSpark.Screen.write('BMP-180 sensor', xy=(0,4), screenCenter=True)\n        dataTxt = \"Temp: {0:0.2f} C\\nBaro: {1:0.2f} Pa\\n Alt: {2:0.2f} m\".format(\n            self.BMPSensor.read_temperature(),\n            self.BMPSensor.read_pressure(),\n            self.BMPSensor.read_altitude())\n\n        self.RPiSpark.Screen.write(dataTxt, xy=(12,26), spacing=1)\n\n        # Draw a 2 pixels width line\n        self.RPiSpark.Screen.Canvas.line( (12, 20, 116, 20), 1, 3 )        \n        self.RPiSpark.Screen.refresh()","repo_name":"mobinrg/rpi_spark_examples","sub_path":"turorial/14.0/14_3_1_bmp180.py","file_name":"14_3_1_bmp180.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6670066906","text":"# 로또의 최고 순위와 최저 순위\ndef solution(lottos, win_nums):\n    cnt = lottos.count(0)\n    if cnt == 6:\n        cnt -= 1\n\n    if len(set(lottos) & set(win_nums)) < 2:\n        result = 6\n    else:\n        result = 7 - len(set(lottos) & set(win_nums))\n\n    answer= [result-cnt, result]\n    \n    return answer\n\nprint(solution([44, 1, 0, 0, 31, 25], [31, 10, 45, 1, 6, 19]))","repo_name":"surpmh/algorithms","sub_path":"Programmers/level1/77484.py","file_name":"77484.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24559451914","text":"# -*- coding: utf-8 -*-\n\"\"\"Heroku specific settings. These are used to deploy opencomparison to\nHeroku's platform.\n\"\"\"\n\n\nfrom os import environ\n\nfrom memcacheify import memcacheify\nfrom postgresify import postgresify\nfrom S3 import CallingFormat\n\nfrom settings.base import *\n\n\n########## DATABASES\nDATABASES = postgresify()\n\n\n########## CACHE\nCACHE_TIMEOUT = 60 * 60 * 24\nCACHES = memcacheify()\n\n\n########## WSGI SERVER\nINSTALLED_APPS += ['gunicorn']\n\n\n########## EMAIL\nDEFAULT_FROM_EMAIL = environ.get('DEFAULT_FROM_EMAIL',\n        'Django Packages <djangopackages-noreply@djangopackages.com>')\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\nEMAIL_HOST = environ.get('EMAIL_HOST', 'smtp.sendgrid.com')\nEMAIL_HOST_PASSWORD = os.environ['SENDGRID_PASSWORD']\nEMAIL_HOST_USER = os.environ['SENDGRID_USERNAME']\nEMAIL_PORT = environ.get('EMAIL_PORT', 587)\nEMAIL_SUBJECT_PREFIX = environ.get('EMAIL_SUBJECT_PREFIX', '[Django Packages] ')\nEMAIL_USE_TLS = True\nSERVER_EMAIL = EMAIL_HOST_USER\n\n\n########## SECRET\nSECRET_KEY = environ.get('SECRET_KEY', '')\n\n\n########## GITHUB\nGITHUB_API_SECRET = environ.get('GITHUB_API_SECRET')\nGITHUB_APP_ID = environ.get('GITHUB_APP_ID')\n\n\n########## SITE\nSITE_TITLE = environ.get('SITE_TITLE')\nFRAMEWORK_TITLE = environ.get('FRAMEWORK_TITLE')\n\n\n########## STORAGE\nINSTALLED_APPS += ['storages']\nDEFAULT_FILE_STORAGE = 'storages.backends.s3boto.S3BotoStorage'\nSTATICFILES_STORAGE = 'storages.backends.s3boto.S3BotoStorage'\n\nAWS_ACCESS_KEY_ID = environ.get('AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = environ.get('AWS_SECRET_ACCESS_KEY')\nAWS_STORAGE_BUCKET_NAME = environ.get('AWS_STORAGE_BUCKET_NAME')\n\nAWS_CALLING_FORMAT = CallingFormat.SUBDOMAIN\nAWS_HEADERS = {\n    'Expires': 'Thu, 15 Apr 2020 20:00:00 GMT',\n    'Cache-Control': 'max-age=86400',\n}\nAWS_QUERYSTRING_AUTH = False\n\nSTATIC_URL = 'https://s3.amazonaws.com/%s/' % AWS_STORAGE_BUCKET_NAME\nMEDIA_URL = STATIC_URL\n\n\n########### Permissions\nRESTRICT_PACKAGE_EDITORS = False\nRESTRICT_GRID_EDITORS = False\n\n########### Errors\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'handlers': {\n        'mail_admins': {\n            'level': 'ERROR',\n            'class': 'django.utils.log.AdminEmailHandler'\n        }\n    },\n    'loggers': {\n        'django': {\n            'handlers': ['mail_admins'],\n            'level': 'ERROR',\n            'propagate': True,\n        },\n    }\n}\n\nDJANGOLYTICS = {\n    \"CHOICES_MODELS\": ('searchv2.SearchV2', ),\n    \"CREATED_MODELS\": (),\n    \"MODIFIED_MODELS\": (),\n    \"TOKEN\": environ.get('DJANGOLYTICS_TOKEN')\n}\n\n########## DATABASE CONFIGURATION\n# Setting PGSQL_POOLING to True means:\n#   We use django_postgrespool to handle the database connection.\n#   What this means is we use SqlAlchemy to handle the pool to PGSQL on Heroku, meaning we don't have\n#   to reestablish connection to the database as often. Which means a faster app. The downside is there\n#   is some risk as it's still a new project.\n#\n# Setting PGSQL_POOLING to False means:\n#   We use the standard Django pgsql connection. The pooling isn't as good but we have more stability.\nPGSQL_POOLING = False\n\n\nif PGSQL_POOLING:\n    import dj_database_url\n\n    DATABASES = {'default': dj_database_url.config()}\n    DATABASES['default']['ENGINE'] = 'django_postgrespool'\n\n    SOUTH_DATABASE_ADAPTERS = {\n        'default': 'south.db.postgresql_psycopg2'\n    }\n\n    DATABASE_POOL_ARGS = {\n        'max_overflow': 10,\n        'pool_size': 5,\n        'recycle': 300\n    }\nelse:\n    from postgresify import postgresify\n\n    DATABASES = postgresify()\n########## END DATABASE CONFIGURATION","repo_name":"encorehu/opencomparison","sub_path":"settings/heroku.py","file_name":"heroku.py","file_ext":"py","file_size_in_byte":3601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"7377709068","text":"from http.server import *\nimport os\n\nrD='C:/Users/Madhu/Desktop/PSG/madhu/SEM 4/Computer networks lab/Assignment9_http/CN_http/'\n\nclass http_RequestHandler(BaseHTTPRequestHandler):\n\n    def do_GET(self):\n        try:\n            file = open(rD + self.path)\n            self.send_response(200)\n            self.send_header('Content-type', 'text-html')\n            self.end_headers()\n            html = file.read()\n            self.wfile.write(bytes(html, \"utf-8\"))\n            file.close()\n            return\n        except:\n            self.send_error(404, 'file not found')\n\n    def do_PUT(self):\n        x=self.path.split(',')\n        try:\n            file=open(rD +x[1],\"w\")\n            self.send_response(201)\n            self.send_header('Content-type', 'text-html')\n            self.end_headers()\n            file.write(x[0])\n            file.close()\n            return\n        except:\n            self.send_error(404, 'file not found')\n\n    def do_POST(self):\n        x = self.path.split(',')\n        try:\n            file = open(rD + x[1], \"a\")\n            self.send_response(201)\n            self.send_header('Content-type', 'text-html')\n            self.end_headers()\n            file.write(x[0])\n            file.close()\n            return\n        except:\n            self.send_error(404, 'file not found')\n\n    def do_DELETE(self):\n        if os.path.exists(rD+self.path):\n            os.remove(rD+self.path)\n            self.send_response(200)\n            self.send_header('Content-type', 'text-html')\n            self.end_headers()\n        else:\n            self.send_error(404,'file not found')\n        return\n\n\nif __name__=='__main__':\n    server_address = ('127.0.0.1', 9000)\n    httpServer = HTTPServer(server_address,http_RequestHandler)\n    httpServer.serve_forever()","repo_name":"joebeck20/College","sub_path":"SEM 4/Computer networks lab/Assignment9_http/HTTP Client Server_assignment/19PW13_HTTPserver.py","file_name":"19PW13_HTTPserver.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3019360256","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nЗадание 23.3a\n\nВ этом задании надо сделать так, чтобы экземпляры класса Topology\nбыли итерируемыми объектами.\nОснову класса Topology можно взять из любого задания 22.1x или задания 23.3.\n\nПосле создания экземпляра класса, экземпляр должен работать как итерируемый объект.\nНа каждой итерации должен возвращаться кортеж, который описывает одно соединение.\nПорядок вывода соединений может быть любым.\n\n\nПример работы класса:\n\nIn [1]: top = Topology(topology_example)\n\nIn [2]: for link in top:\n   ...:     print(link)\n   ...:\n(('R1', 'Eth0/0'), ('SW1', 'Eth0/1'))\n(('R2', 'Eth0/0'), ('SW1', 'Eth0/2'))\n(('R2', 'Eth0/1'), ('SW2', 'Eth0/11'))\n(('R3', 'Eth0/0'), ('SW1', 'Eth0/3'))\n(('R3', 'Eth0/1'), ('R4', 'Eth0/0'))\n(('R3', 'Eth0/2'), ('R5', 'Eth0/0'))\n\n\nПроверить работу класса.\n\"\"\"\n\nclass Topology:\n    def __init__(self, topology_dict):\n        self.topology = self._normalize(topology_dict)\n\n    def _normalize(self, topology_dict):\n        slovar = {}\n        for key1, value1 in topology_dict.items():\n            if not slovar.get(key1) and not slovar.get(value1):\n                slovar[key1] = (value1)\n        return slovar\n\n    def delete_link(self, one_pair, two_pair):\n        for key, value in list(self.topology.items()):\n            if  key == one_pair:\n                if value == two_pair:\n                    del self.topology[key]\n            elif  value == one_pair:\n                if key == two_pair:\n                    del self.topology[key]\n            else:\n                print(\"Такого соединения нет\")\n\n    def delete_node(self, node):\n        for key, value in list(self.topology.items()):\n            if key[0] == node or value[0] == node:\n                del self.topology[key]\n            else:\n                print(\"Такого устройства нет\")\n\n    def add_link(self, src_link, dst_link):\n        result = \"Add Link\"\n        temp_keys  = list(self.topology.keys())\n        temp_values = list(self.topology.values())\n        t1 = [item for item in zip(temp_keys, temp_values) if src_link in item\n              or dst_link in item]\n        if t1:\n            result = \"Cоединение с одним из портов существует\"\n            if t1[0] == (src_link, dst_link) or t1[0] == (dst_link, src_link):\n                result = \"Такое соединение существует\"\n        else:\n            self.topology.update({src_link:dst_link})\n        print(result)\n\n    def __add__(self, other):\n        return Topology({**self.topology, **other.topology})\n\n    def __iter__(self):\n        return iter(self.topology.items())\n\nif __name__ == \"__main__\":\n    topology_example = {\n        (\"R1\", \"Eth0/0\"): (\"SW1\", \"Eth0/1\"),\n        (\"R2\", \"Eth0/0\"): (\"SW1\", \"Eth0/2\"),\n        (\"R2\", \"Eth0/1\"): (\"SW2\", \"Eth0/11\"),\n        (\"R3\", \"Eth0/0\"): (\"SW1\", \"Eth0/3\"),\n        (\"R3\", \"Eth0/1\"): (\"R4\", \"Eth0/0\"),\n        (\"R3\", \"Eth0/2\"): (\"R5\", \"Eth0/0\"),\n        (\"SW1\", \"Eth0/1\"): (\"R1\", \"Eth0/0\"),\n        (\"SW1\", \"Eth0/2\"): (\"R2\", \"Eth0/0\"),\n        (\"SW1\", \"Eth0/3\"): (\"R3\", \"Eth0/0\"),\n    }\n\n\n    t1 = Topology(topology_example)\n    for link in t1:\n        print(link)\n","repo_name":"mishins9/python_4ne","sub_path":"exercises/23_oop_special_methods/task_23_3a.py","file_name":"task_23_3a.py","file_ext":"py","file_size_in_byte":3530,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23161999489","text":"# 1\n# import pickle\n# # Створюємо словник з данними\n# contact = [\n#     {'Alex': '+380997654513'},\n#     {'Andrew': '+380675690061'},\n#     {'Serhii': '+380661238812'}\n# ]\n# file_name = 'contact.bin'\n# with open('contact.bin', 'wb') as file:\n#     pickle.dump(contact, file)\n# перевірка\n# test= open('contact.bin', 'rb')\n# contact = pickle.load(test)\n# print(contact)\n\n\n# 2\nimport json\nimport json\n\na = {'key': 1, 'key2': True}\nb = {'key': 'Hello'}\nc = {}\nfor key, value in a.items():\n    if key in b:\n        c[key] = [value, b[key]]\n    else:\n        c[key] = value\n\nfor key, value in b.items():\n    if key not in c:\n        c[key] = value\n\nwith open('result.json', 'w') as file:\n    json.dump(c, file)\n","repo_name":"Serjkeey/homework-hillel","sub_path":"Homework_11/homework_11.py","file_name":"homework_11.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22302184102","text":"import numpy as np\n\ndef pla_alg(x,y_true,max_iter=1000,w_est=None):\n    '''\n    perceptron learning algorithm\n    input:\n    x = NxNs matrix, data variable\n    y_true = Ns matrix, ground truth labels \n    max_iter = maximum number of iteration\n    \n    Ns = number of samples\n    N = dimension of features\n    \n    output:\n    w_est = 1xN matrix, estimated weight\n    n_miscls = misclassified examples in each iteration\n    i = the number of iterations taken to converge,\n        i==max_iter indicates possible non-convergence\n    '''\n    if w_est is None:\n        w_est = np.random.rand(1,len(x)) #1x3\n\n    n_miscls = []\n\n    for i in range(max_iter):\n        y_est = (w_est.dot(x)>0)*1.0+(w_est.dot(x)<=0)*-1.0 #1x100\n        y_est = y_est[0] #100 length array from 1x100 matrix\n\n        mis_cls = (y_est*y_true)<0\n\n\n        y_sub = y_true[mis_cls]\n        x_sub = x[:,mis_cls]\n        n_sub = len(y_sub)\n        n_miscls.append(n_sub)\n        if n_sub>0:\n            ch = np.random.choice(n_sub,size=1)\n\n            w_est = w_est + y_sub[ch]*x_sub[:,ch].T\n\n        else:\n            break\n    \n    return w_est,n_miscls,i+1\n\ndef logreg_mle(x,y_true,learning_rate,max_iter=1000,w_est=None):\n    '''\n    Logistic Regression with MLE and GD algorithm\n    input:\n    x = NxNs matrix, data variable\n    y_true = Ns matrix, ground truth labels\n    learning_rate = gradient descent hyperparameter\n    max_iter = maximum number of iteration\n    \n    Ns = number of samples\n    N = dimension of features\n    \n    output:\n    w_est = 1xN matrix, estimated weight\n    n_miscls = misclassified examples in each iteration\n    i = the number of iterations taken to converge,\n        i==max_iter indicates possible non-convergence\n    '''\n    w_N = len(x)\n    \n    if w_est is None:\n        w_est = np.random.rand(1,w_N)\n    \n    n_miscls = []\n    \n    for i in range(max_iter):\n        grad_E = - np.mean(y_true*x / (1 + np.exp(y_true*w_est.dot(x))),axis=1)\n        \n        y_est = (w_est.dot(x)>0)*1.0+(w_est.dot(x)<=0)*-1.0 #1x100\n        y_est = y_est[0] #100 length array from 1x100 matrix\n\n        w_est = w_est - learning_rate * grad_E / np.sqrt(np.sum(grad_E*grad_E))\n        if (i+1)%100 == 0 :\n            learning_rate /= 2\n            \n        mis_cls = np.sum((y_est*y_true)<0)\n        n_miscls.append(mis_cls)\n        \n        if mis_cls == 0:\n            break\n    \n    return w_est,n_miscls,i\n    \n","repo_name":"tariqul-islam/BUET-ML-Tutorial","sub_path":"algs.py","file_name":"algs.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27712460047","text":"import requests\nfrom logger import App_Logger\n\n\nclass TMDB:\n\n    def __init__(self,log_file):\n        self.log_file = log_file\n        self.log = App_Logger()\n\n    def fetch_poster(self, movie_id):\n\n        \"\"\"\n        This method create a path for posters of the movies\n        :param movie_id: string\n        :return:\n        \"\"\"\n        try:\n            reponse = requests.get(\"https://api.themoviedb.org/3/movie/{}?api_key=f1857f1c73a189d66943c978c4964ea7&language=en-US\".format(movie_id))\n            data = reponse.json()\n            return \"https://image.tmdb.org/t/p/w185/\"+data['poster_path']\n        except Exception as e:\n            raise e","repo_name":"Akashdawari/Movie_Recommender","sub_path":"Tmdb_Api_Hitter.py","file_name":"Tmdb_Api_Hitter.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25655172258","text":"import asyncio\nimport json\nimport logging\nimport os\nimport queue\nimport sys\nimport threading\nimport time\n\nfrom nats.aio.client import Client as NATS\nfrom nats.aio.errors import NatsError\n\n_app_name = ''\n_appInfo = None\n_product_sn = ''\n_device_sn = ''\n\n# get Config\n_config_path = './etc/iotedge/config.json'\nwith open(_config_path, 'r') as load_f:\n    try:\n        load_dict = json.load(load_f)\n        print(str(load_dict))\n        # print('----config: {} -------'.format(load_dict))\n        _app_name = load_dict['appName']\n        _product_sn = load_dict['productSN']\n        _device_sn = load_dict['deviceSN']\n\n        if 'appInfo' in load_dict.keys():\n            _appInfo = load_dict['appInfo']\n\n    except Exception as e:\n        print('load config file error:{}'.format(e))\n        sys.exit(1)\n\nprint(\"application name:{}\".format(_app_name))\n\n\nclass _Logger(object):\n    def __init__(self, name):\n        self.url = os.environ.get(\n            'IOTEDGE_NATS_ADDRESS') or 'tcp://127.0.0.1:4222'\n        self.nc = NATS()\n        self.loop = asyncio.new_event_loop()\n        self.queue = queue.Queue()\n        self.name = name\n        self.logger = logging.getLogger()\n        format_str = logging.Formatter(\n            '%(asctime)s - %(levelname)s: %(message)s')\n        sh = logging.StreamHandler()\n        sh.setFormatter(format_str)\n        self.logger.addHandler(sh)\n\n    async def _publish(self):\n        try:\n            await self.nc.connect(servers=[self.url], loop=self.loop)\n            logging.debug('nats for logger connect success')\n        except Exception as e1:\n            logging.error(e1)\n            sys.exit(1)\n\n        while True:\n            try:\n                msg = self.queue.get()\n                bty = json.dumps(msg)\n                await self.nc.publish(subject='edge.log.upload',\n                                      payload=bty.encode('utf-8'))\n                await self.nc.flush()\n            except NatsError as e:\n                logging.error(e)\n            except Exception as e:\n                logging.error(e)\n\n    def start(self):\n        self.loop.run_until_complete(self._publish())\n\n    def debug(self, msg):\n        data = {\n            'module': self.name,\n            'level': 'debug',\n            'message': msg,\n            'timestamp': int(time.time()),\n        }\n        self.queue.put(data)\n        self.logger.debug(msg)\n\n    def info(self, msg):\n        data = {\n            'module': self.name,\n            'level': 'info',\n            'message': msg,\n            'timestamp': int(time.time()),\n        }\n        self.queue.put(data)\n        self.logger.info(msg)\n\n    def error(self, msg):\n        data = {\n            'module': self.name,\n            'level': 'error',\n            'message': msg,\n            'timestamp': int(time.time()),\n        }\n        self.queue.put(data)\n        self.logger.error(msg)\n\n    def warn(self, msg):\n        data = {\n            'module': self.name,\n            'level': 'warn',\n            'message': msg,\n            'timestamp': int(time.time()),\n        }\n        self.queue.put(data)\n        self.logger.warn(msg)\n\n    def critical(self, msg):\n        data = {\n            'module': self.name,\n            'level': 'critical',\n            'message': msg,\n            'timestamp': int(time.time()),\n        }\n        self.queue.put(data)\n        self.logger.critical(msg)\n\n    def setLevel(self, level):\n        self.logger.setLevel(level)\n\n\n_iotedge_logger = _Logger(\"app_\"+_app_name)\n\n\ndef _init_logger():\n    global _iotedge_logger\n    _iotedge_logger.start()\n    logging.debug('init logger success')\n\n\n_t_logger = threading.Thread(target=_init_logger)\n_t_logger.setDaemon(True)\n_t_logger.start()\n\n\ndef getLogger():\n    global _iotedge_logger\n    return _iotedge_logger\n","repo_name":"ucloud/iotstack-application-sdk-python","sub_path":"iotedgeapplicationlinksdk/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20607306560","text":"class Node:\n    def __init__(self,data):\n        self.data=data\n        self.next=None\nclass Linkedlist:\n    def __init__(self):\n        self.firstNode=None\n    def insert_end(self,data):\n        newNode=Node(data)\n        if self.firstNode is None:\n            self.firstNode=newNode\n        else:\n            tempNode=self.firstNode\n            while True:\n                if tempNode.next is None:\n                    break\n                else:\n                    tempNode=tempNode.next\n            tempNode.next=newNode\n    def delete_head(self):\n        tempNode=self.firstNode\n        self.firstNode=None\n        self.firstNode=tempNode.next\n    def delete_end(self):\n        tempNode=self.firstNode\n        while tempNode.next is not None:\n            privousNode=tempNode\n            tempNode=tempNode.next\n        privousNode.next=None\n\n    def printL(self):\n        if self.firstNode is None:\n            print(\"it is empty\")\n        else:\n            tempNode=self.firstNode\n            while True:\n                print(tempNode.data)\n                if tempNode.next is None:\n                    break\n                else:\n                    tempNode=tempNode.next\n\nlinklist=Linkedlist()\nlinklist.insert_end(78)  # push method\nlinklist.insert_end(79)\nlinklist.insert_end(76)\nlinklist.insert_end(71)\nlinklist.insert_end(73)\nlinklist.delete_head()\nlinklist.delete_end()     #pop method\nlinklist.printL()\n\n\n\n","repo_name":"Raushan-kumar88/linklist-","sub_path":"try.py","file_name":"try.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9975167686","text":"# coding: utf8\r\n\"\"\"\r\n    pylink.compat\r\n    -------------\r\n\r\n    Workarounds for compatibility with Python 2 and 3 in the same code base.\r\n\r\n    :copyright: Copyright 2012 Salem Harrache and contributors, see AUTHORS.\r\n    :license: BSD, see LICENSE for details.\r\n\r\n\"\"\"\r\n\r\nimport sys\r\n\r\n# -------\r\n# Pythons\r\n# -------\r\n\r\n# Syntax sugar.\r\n_ver = sys.version_info\r\n\r\n#: Python 2.x?\r\nis_py2 = (_ver[0] == 2)\r\n\r\n#: Python 3.x?\r\nis_py3 = (_ver[0] == 3)\r\n\r\n#: Python 3.0.x\r\nis_py30 = (is_py3 and _ver[1] == 0)\r\n\r\n#: Python 3.1.x\r\nis_py31 = (is_py3 and _ver[1] == 1)\r\n\r\n#: Python 3.2.x\r\nis_py32 = (is_py3 and _ver[1] == 2)\r\n\r\n#: Python 3.3.x\r\nis_py33 = (is_py3 and _ver[1] == 3)\r\n\r\n#: Python 3.4.x\r\nis_py34 = (is_py3 and _ver[1] == 4)\r\n\r\n#: Python 2.7.x\r\nis_py27 = (is_py2 and _ver[1] == 7)\r\n\r\n#: Python 2.6.x\r\nis_py26 = (is_py2 and _ver[1] == 6)\r\n\r\n# ---------\r\n# Specifics\r\n# ---------\r\n\r\nif is_py2:\r\n    if is_py26:\r\n        from logging import Handler\r\n\r\n        class NullHandler(Handler):\r\n            def emit(self, record):\r\n                pass\r\n    else:\r\n        from logging import NullHandler\r\n\r\n    bytes = str\r\n    str = unicode\r\n\r\n\r\nelif is_py3:\r\n    from logging import NullHandler\r\n    from io import StringIO\r\n\r\n    str = str\r\n    bytes = bytes\r\n    basestring = bytes\r\n\r\n\r\ndef format_unicode(s, encoding='utf-8'):\r\n    if isinstance(s, str):\r\n        return s\r\n    elif isinstance(s, basestring):\r\n        return s.decode(encoding)\r\n    return str(s)\r\n\r\n\r\ndef format_string(s, encoding='utf-8'):\r\n    if isinstance(s, str):\r\n        return s.encode(encoding)\r\n    if isinstance(s, basestring):\r\n        return s\r\n    return str(s)\r\n","repo_name":"LionelDarras/PyLink","sub_path":"pylink/compat.py","file_name":"compat.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"9335910655","text":"import logging\nimport multiprocessing as mp\nimport pathlib\nimport queue\nimport time\n\nimport pandas as pd\nimport pytest\nimport sqlalchemy\n\nfrom bluesearch.database.mining_cache import CreateMiningCache, Miner\n\n\nclass TestMiner:\n    @pytest.fixture\n    def miner_env(self, fake_sqlalchemy_engine, monkeypatch, model_entities):\n        # Re-use the \"en_core_web_sm\" model in the model_entities fixture\n        monkeypatch.setattr(\n            \"bluesearch.database.mining_cache.load_spacy_model\",\n            lambda model_path, device: model_entities,\n        )\n\n        task_queue: mp.Queue = mp.Queue()\n        can_finish = mp.Event()\n        miner = Miner(\n            database_url=fake_sqlalchemy_engine.url,\n            model_path=\"en_core_web_sm\",\n            target_table=\"mining_cache_temporary\",\n            task_queue=task_queue,\n            can_finish=can_finish,\n            device=\"cpu\",\n        )\n\n        return miner, task_queue, can_finish\n\n    def test_create_and_mine(self, fake_sqlalchemy_engine, monkeypatch, model_entities):\n        task_queue: mp.Queue = mp.Queue()\n        can_finish = mp.Event()\n        can_finish.set()\n\n        # Re-use the \"en_core_web_sm\" model in the model_entities fixture\n        monkeypatch.setattr(\n            \"bluesearch.database.mining_cache.load_spacy_model\",\n            lambda model_path, device: model_entities,\n        )\n\n        Miner.create_and_mine(\n            database_url=fake_sqlalchemy_engine.url,\n            model_path=\"en_core_web_sm\",\n            target_table=\"\",\n            task_queue=task_queue,\n            can_finish=can_finish,\n            device=\"cpu\",\n        )\n\n    def test_log_exception(self, miner_env, caplog):\n        miner, _, _ = miner_env\n        article_id = 5\n\n        try:\n            _ = 1 / 0\n        except ZeroDivisionError:\n            miner._log_exception(article_id=article_id)\n\n        assert len(caplog.record_tuples) == 1\n        logger_name, level, text = caplog.record_tuples[0]\n        assert logger_name == str(miner)\n        assert level == logging.ERROR\n        assert f\"Article ID: {article_id}\" in text\n        assert \"Traceback\" in text\n        assert \"ZeroDivisionError\" in text\n\n    def test_work_loop_and_mine(self, miner_env, monkeypatch):\n        miner, task_queue, can_finish = miner_env\n        article_id = 0\n\n        fake_result = {\n            \"article_id\": article_id,\n            \"paper_id\": \"my_paper\",\n            \"entity_type\": \"ORGAN\",\n        }\n\n        def run_pipeline(*args, **kwargs):\n            can_finish.set()\n            return pd.DataFrame([fake_result])\n\n        monkeypatch.setattr(\n            \"bluesearch.database.mining_cache.run_pipeline\", run_pipeline\n        )\n\n        # Work loop with queue with one article ID\n        task_queue.put(article_id)\n        miner.work_loop()\n\n        df_result = pd.read_sql(\n            f\"select * from {miner.target_table_name}\", con=miner.engine\n        )\n        assert len(df_result) == 1\n        assert df_result.iloc[0][\"article_id\"] == fake_result[\"article_id\"]\n        assert df_result.iloc[0][\"paper_id\"] == fake_result[\"paper_id\"]\n        assert df_result.iloc[0][\"entity_type\"] == fake_result[\"entity_type\"]\n        assert \"mining_model_version\" in df_result.columns\n\n        miner.engine.execute(f\"drop table {miner.target_table_name}\")\n\n    def test_generate_texts_with_metadata(self, miner_env, test_parameters):\n        miner, task_queue, can_finish = miner_env\n        article_id = 1\n\n        results = list(miner._generate_texts_with_metadata(article_ids=article_id))\n\n        assert len(results) == test_parameters[\"n_sections_per_article\"]\n\n        (text_1, meta_1), (text_2, meta_2) = results\n\n        assert text_1 == (\n            \"I am a sentence 0 in section 0 in article 1. I am a sentence 1 in \"\n            \"section 0 in article 1. I am a sentence 2 in section 0 in article 1.\"\n        )\n        assert meta_1 == {\n            \"article_id\": 1,\n            \"paragraph_pos_in_article\": 0,\n            \"paper_id\": \"1:section_0:0\",\n        }\n        assert text_2 == (\n            \"I am a sentence 0 in section 1 in article 1. I am a sentence 1 in \"\n            \"section 1 in article 1. I am a sentence 2 in section 1 in article 1.\"\n        )\n        assert meta_2 == {\n            \"article_id\": 1,\n            \"paragraph_pos_in_article\": 1,\n            \"paper_id\": \"1:section_1:1\",\n        }\n\n    def test_clean_up(self, miner_env, caplog):\n        miner, _, _ = miner_env\n        miner.clean_up()\n\n        for logger_name, level, _text in caplog.record_tuples:\n            assert logger_name == str(miner)\n            assert level == logging.INFO\n\n    def test_str(self, miner_env):\n        miner, _, _ = miner_env\n        assert str(miner) == f\"Miner[{mp.current_process().name}]\"\n\n\nclass TestCreateMiningCache:\n    @pytest.fixture\n    def cache_creator(self, fake_sqlalchemy_engine):\n        ee_models_paths = {\"type_1_public\": pathlib.Path(\"/my/model/path\")}\n        cache_creator_instance = CreateMiningCache(\n            database_engine=fake_sqlalchemy_engine,\n            ee_models_paths=ee_models_paths,\n            target_table_name=\"mining_cache_temporary\",\n            workers_per_model=2,\n        )\n\n        return cache_creator_instance\n\n    def test_delete_rows(self, cache_creator):\n        table_name = cache_creator.target_table\n        engine = cache_creator.engine\n        ee_models_paths = cache_creator.ee_models_paths\n\n        # Create a table with two rows, one with good model, one with bad\n        assert len(ee_models_paths) >= 1\n        valid_etype = list(ee_models_paths.keys())[0]\n        df = pd.DataFrame(\n            [\n                {\"entity_type\": valid_etype},\n                {\"entity_type\": valid_etype + \"_not_valid\"},\n            ]\n        )\n        with engine.begin() as con:\n            df.to_sql(table_name, con)\n\n        # Check table has two rows\n        df_table = pd.read_sql(f\"select * from {table_name}\", engine)\n        assert len(df_table) == 2\n\n        # Call `_delete_rows`, it show only delete the row with the good model\n        cache_creator._delete_rows()\n\n        # Check that only one row is left\n        df_table = pd.read_sql(f\"select * from {table_name}\", engine)\n        assert len(df_table) == 1\n\n        # Clean up by deleting the table just created\n        engine.execute(f\"drop table {table_name}\")\n\n    def test_schema_creation(self, cache_creator):\n        cache_creator._schema_creation()\n        df_new_table = pd.read_sql(\n            f\"select * from {cache_creator.target_table}\", con=cache_creator.engine\n        )\n\n        assert len(df_new_table) == 0\n        assert set(df_new_table.columns) == {\n            \"entity\",\n            \"entity_type\",\n            \"property\",\n            \"property_value\",\n            \"property_type\",\n            \"property_value_type\",\n            \"ontology_source\",\n            \"paper_id\",\n            \"start_char\",\n            \"end_char\",\n            \"article_id\",\n            \"paragraph_pos_in_article\",\n            \"mining_model_version\",\n            \"spacy_version\",\n        }\n\n        # Test calling with the table already existing.\n        cache_creator._schema_creation()\n\n        # Clean up by deleting the table just created\n        cache_creator.engine.execute(f\"drop table {cache_creator.target_table}\")\n\n    @staticmethod\n    def fake_wait_miner(stop_now):\n        while not stop_now.is_set():\n            time.sleep(0.01)\n\n    def test_create_tasks(self, cache_creator):\n        my_queue: mp.Queue = mp.Queue()\n        queue_name = \"my_queue\"\n        task_queues = {queue_name: my_queue}\n\n        stop_event = mp.Event()\n        worker_proc = mp.Process(target=self.fake_wait_miner, args=(stop_event,))\n        worker_proc.start()\n        workers_by_queue = {queue_name: [worker_proc]}\n\n        assert worker_proc.is_alive()\n        cache_creator.create_tasks(task_queues, workers_by_queue)\n\n        # Ugly workaround. This gives the background thread a bit of time\n        # to flush the buffer into the pipe. Otherwise the following assert\n        # might fail if none of the buffer has been transferred yet.\n        while len(my_queue._buffer) > 0:  # type: ignore\n            time.sleep(0.1)\n\n        assert not my_queue.empty()\n\n        stop_event.set()\n        worker_proc.join()\n\n        # Test adding tasks to a queue where all workers are dead\n        cache_creator.create_tasks(task_queues, workers_by_queue)\n\n    @staticmethod\n    def fake_queue_miner(task_queue=None, can_finish=None, **kwargs):\n        while not can_finish.is_set():\n            try:\n                _ = task_queue.get(timeout=1.0)\n            except queue.Empty:\n                continue\n\n    def test_do_mining(self, cache_creator, monkeypatch):\n        cache_creator._schema_creation()\n\n        monkeypatch.setattr(\n            \"bluesearch.database.mining_cache.Miner.create_and_mine\",\n            self.fake_queue_miner,\n        )\n        cache_creator.do_mining()\n        cache_creator.engine.execute(f\"drop table {cache_creator.target_table}\")\n        inspector = sqlalchemy.inspect(cache_creator.engine)\n        indexes = inspector.get_indexes(\"mining_cache\")\n        assert len(indexes) == 1\n\n    def test_construct(self, cache_creator, monkeypatch):\n        monkeypatch.setattr(\n            \"bluesearch.database.mining_cache.Miner.create_and_mine\",\n            self.fake_queue_miner,\n        )\n\n        cache_creator.construct()\n        cache_creator.engine.execute(f\"drop table {cache_creator.target_table}\")\n","repo_name":"BlueBrain/Search","sub_path":"tests/unit/database/test_mining_cache.py","file_name":"test_mining_cache.py","file_ext":"py","file_size_in_byte":9516,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"77"}
+{"seq_id":"43483303325","text":"from selenium import webdriver\r\nimport time\r\nfrom functools import partial\r\nimport sys\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.chrome.options import Options\r\n\r\ndef SignIn(driver):\r\n    driver.find_element_by_xpath('//*[@id=\"loginForm\"]/form/div[1]/input').send_keys('hark')\r\n    driver.find_element_by_xpath('//*[@id=\"loginForm\"]/form/div[2]/input').send_keys('Password009')\r\n    driver.find_element_by_xpath('//*[@id=\"loginForm\"]/form/button').click()\r\n\r\ndef Add(stock):\r\n    driver.find_element_by_xpath('//*[@id=\"inputStock\"]').send_keys(stock)\r\n    time.sleep(3)\r\n    driver.find_element_by_xpath('//*[@id=\"submitStock\"]').click()\r\n\r\ndef Delete(driver):\r\n    driver.find_element_by_xpath('/html/body/div/div[3]/div/div/div/table/tbody/tr/td[9]/button').click()\r\n\r\ndef package1(driver):\r\n    driver.find_element_by_xpath('/html/body/div/div[2]/div/div/main/div[2]/div[2]/div/div[2]/button').click()\r\n    time.sleep(3)\r\n    driver.find_element_by_xpath('//*[@id=\"deluxeConfirmation\"]/div/div/div[3]/form/button').click()\r\n\r\ndef package2(driver):\r\n    driver.find_element_by_xpath('/html/body/div/div[2]/div/div/main/div[2]/div[3]/div/div[2]/button').click()\r\n    time.sleep(3)\r\n    driver.find_element_by_xpath('//*[@id=\"ultimateConfirmation\"]/div/div/div[3]/form/button').click()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    options = Options()\r\n    options.add_experimental_option('excludeSwitches', ['enable-logging'])\r\n\r\n    driver = webdriver.Chrome(executable_path=r\"C:\\Users\\harki\\Downloads\\chromedriver_win32\\chromedriver.exe\",options=options)\r\n    \r\n    driver.get('http://127.0.0.1:8000/login/')\r\n    SignIn(driver)\r\n    time.sleep(3)\r\n\r\n    driver.get('http://127.0.0.1:8000/add_stock.html')\r\n    Add('mmm')\r\n    time.sleep(3)\r\n    Add('rf')\r\n\r\n    time.sleep(3)\r\n    Delete(driver)\r\n    time.sleep(3)\r\n\r\n    driver.get('http://127.0.0.1:8000/pricing/')\r\n    time.sleep(3)\r\n    package1(driver)\r\n    time.sleep(3)\r\n\r\n    driver.get('http://127.0.0.1:8000/add_stock.html')\r\n    Add('mmm')\r\n    time.sleep(3)\r\n    Add('all')\r\n    time.sleep(3)\r\n    Add('rf')\r\n    time.sleep(3)\r\n    Add('mo')\r\n    time.sleep(3)\r\n\r\n    driver.get('http://127.0.0.1:8000/pricing/')\r\n    time.sleep(3)\r\n    package2(driver)\r\n    time.sleep(3)\r\n    driver.get('http://127.0.0.1:8000/add_stock.html')\r\n    time.sleep(3)\r\n    Add('mo')\r\n    time.sleep(3)\r\n    Add('eog')\r\n    time.sleep(3)\r\n    Add('cci')","repo_name":"schapiewski/capstone","sub_path":"QATesting/packageAndPortTesting.py","file_name":"packageAndPortTesting.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"27324228489","text":"\"\"\"\nhttps://leetcode.com/problems/xor-queries-of-a-subarray/description/\n\"\"\"\n\nfrom typing import List\n\nclass Solution:\n    def xorQueries(self, arr: List[int], queries: List[List[int]]) -> List[int]:\n        prefix = [arr[0]]\n        for num in arr[1:]:\n            prefix.append(prefix[-1] ^ num)\n        \n        output = []\n        for left, right in queries: \n            if left == right:\n                output.append(arr[left])\n                continue\n\n            if left == 0:\n                output.append(prefix[right])\n            else:\n                output.append(prefix[right] ^ prefix[left - 1])\n            \n        return output","repo_name":"meraf00/Competitive-Programming","sub_path":"week_6/xor_queries_of_subarray.py","file_name":"xor_queries_of_subarray.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43085056781","text":"\n\ndef bayes_hockey():\n    # A. 60% of the kids play football, and 36% of the kids play ice hockey. 40% of the kids who play football\n    # also play ice hockey. What percent of those that play ice hockey also play football?\n\n    # 3 Known values\n\n    # P = probability of\n    # F = football\n    # I = ice_hockey\n\n    P_F = 0.6\n    P_I = 0.36\n    P_FI = 0.4\n\n    # Bayes: P(A|B) = (P(B|A) * P(A)) / P(B)\n    # Translated: P(I|F) = (P(F|I)*P(F))/P(I)\n    # Or: P(ice/foot) = (P(foot/ice)*P(foot))/P(ice)\n\n    P_IF = (P_FI * P_F) / P_I\n    print(\n        f\"\\nProbability of ice hockey players also playing football: {round(P_IF*100, 2)}%\")\n\n\ndef bayes_music_dance():\n\n    # B. 40% of the kids like music, and 24% of the kids like to dance. Given that 30% of those that like\n    # music also likes to dance, what percent of those that like to dance also likes music?\n\n    # 3 Known values\n\n    # P = probability of\n    # M = music\n    # D = dance\n\n    P_M = 0.4\n    P_D = 0.24\n    P_MD = 0.3\n\n    # Bayes: P(A|B) = P(B|A) = (P(B|A) * P(A)) / P(B)\n    # Translated: P(D|M) = (P(M|D)*P(M))/P(D)\n    # Or: P(dance/music) = (P(music/dance)*P(music))/P(dance)\n\n    P_DM = (P_MD * P_M) / P_D\n    print(f\"\\nProbability of dancers also liking music = {round(P_DM*100, 2)}%\")\n\n\ndef bayes_factory():\n    \"\"\"\n    C. In a factory, machine X produces 60% of the daily output and machine Y produces 40% of the daily\n    output.\n    2% of machine X’s output is defective, and 1.5% of machine Y’s output is defective.\n    One day, an item is inspected at random, and found to be defective. What is the probability that it\n    was produced by machine X?\n    \"\"\"\n    # 4 known values\n    # Probability of = P\n    # machine_x = A\n    # machine_y = B\n    # defect = D\n\n    P_A = 0.6\n    P_B = 0.4\n    P_DA = 0.02\n    P_DB = 0.015\n\n    # Bayes: P(A|B) = P(B|A) = (P(B|A) * P(A)) / P(B)\n    # Expanded and translated.\n    # P(A|D) = (P(D|A)*P(A)) / ((P(D|A) * P(A)) * (P(D|B) * P(B)))\n\n    P_AD = (P_DA * P_A) / ((P_DA * P_A) + (P_DB * P_B))\n    print(\n        f\"\\nProbability that the bulb was created at machineX = {round(P_AD*100, 2)}%\")\n\n\nif __name__ == \"__main__\":\n    bayes_hockey()\n    bayes_music_dance()\n    bayes_factory()\n","repo_name":"Mutestock/cphsoft","sub_path":"dsc/uge13/classification/src/logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18822615343","text":"from sklearn import cluster\nfrom sklearn.neighbors import kneighbors_graph\nimport numpy as np\nfrom collections import Counter\nfrom .kmeans import FuzzyKMeans\n\n\ndef label_by_cluster(X, cluster_method, n_clusters):\n    \"\"\"\n        cluster given data using the 'cluster_method'\n    \"\"\"\n    if cluster_method == \"FuzzyKMeans\":\n        kvargs = {\n            \"k\": n_clusters,\n            \"m\": 2,\n            \"tol\": 1e-4,\n            \"max_iter\": 1000,\n        }\n        attr = FuzzyKMeans\n    else:\n        if cluster_method == \"dbscan\":\n            kvargs = {\n                \"eps\": .3\n            }\n        elif cluster_method == \"SpectralClustering\":\n            kvargs = {\n                \"n_clusters\": n_clusters,\n                \"eigen_solver\": 'arpack',\n                \"affinity\": 'nearest_neighbors'\n            }\n        elif cluster_method == \"AgglomerativeClustering\":\n            connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)\n            connectivity = 0.5 * (connectivity + connectivity.T)\n            kvargs = {\n                \"linkage\": \"average\",\n                \"affinity\": \"cityblock\",\n                \"n_clusters\": n_clusters,\n                \"connectivity\": connectivity\n            }\n        else:\n            kvargs = {\n                \"n_clusters\": n_clusters\n            }\n        attr = getattr(cluster, cluster_method)\n    m = attr(**kvargs)\n    try:\n        print(X.shape)\n        m.fit(X)\n    except TypeError:\n        m.fit(X.toarray())\n    if hasattr(m, 'labels_'):\n        return m.labels_.astype(np.int)\n    else:\n        return m.predict(X)\n\n\ndef map_labels(Y, clusters, Y_test):\n    \"\"\"\n    Find relations between labels and Y by identifying the majority of the real labels of a cluster, and \n    convert Y_test to corresponding labels of the clusters in the same way\n    \"\"\"\n    # labels of each collection\n    collections = {}\n    # map from cluster to label\n    mapping = {}\n    # all distinct clusters\n    cluster_set = set([])\n    for i in range(len(clusters)):\n        if clusters[i] not in collections:\n            collections[clusters[i]] = []\n        collections[clusters[i]].append(Y[i])\n        cluster_set.add(clusters[i])\n    for c in cluster_set:\n        mapping[c] = Counter(collections[c]).most_common()[0][0]\n    # mapping from label to clusters\n    reverse_map = {}\n    for k, v in mapping.items():\n        if v in reverse_map:\n            print(\"[Warning] two clusters found for a single label\")\n            print(\">>>>\", mapping[v], \"and\", k, \"for\", v)\n        reverse_map[v] = k\n    for i in Y_test:\n        if i not in reverse_map:\n            print(\"[ERROR]label \", i, \"is not mapped to any cluster\")\n    # return cluster numbers for each label in test set\n    return [reverse_map[i] if i in reverse_map else clusters[0]\n            for i in Y_test]\n","repo_name":"lpimem/dscc","sub_path":"cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36346827365","text":"from models.labyrinth.simplify import *\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n    from models import Tile\n\n\ndef mesure_distance1(laby: 'Labyrinth'):\n    laby.tile_start.distance = 1\n    laby.lists.distances.add(laby.tile_start)\n    cpt = 0\n    while not laby.tile_arrival.distance:\n        cpt += 1\n        for tile in laby.path.tiles:\n            if tile.distance == cpt:\n                for voisin in tile.voisins:\n                    if not voisin.path or voisin.distance:\n                        continue\n                    voisin.distance = cpt + 1\n                    laby.lists.distances.add(voisin)\n\n\ndef mesure_distance(laby: 'Labyrinth'):\n    tiles_not_solved: set['Tile'] = laby.path.tiles.copy()\n    actual_distance = 1\n    laby.lists.distance_add(actual_distance, laby.tile_start)\n    tiles_not_solved.remove(laby.tile_start)\n    stop = False\n    while not stop:\n        for tile in laby.lists.distances[actual_distance]:\n            for voisin in tile.voisins:\n                if voisin in tiles_not_solved:\n                    laby.lists.distance_add(actual_distance + 1, voisin)\n                    tiles_not_solved.remove(voisin)\n                    if voisin is laby.tile_arrival:\n                        stop = True\n                        break\n        actual_distance += 1\n\n","repo_name":"MrStraw/py_lab","sub_path":"models/labyrinth/generate_steps/mesure_distance.py","file_name":"mesure_distance.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27790155248","text":"\"\"\"Feature and history detectors for CoNLL03 taken from:\n\n`Ando, R. K. and Zhang, T. (2005). A Framework for Learning Predictive\nStructures from Multiple Tasks and Unlabeled Data. J. Mach. Learn. Res., 6:1817-1853.`\n\nTODO: short description.\n\"\"\"\nWORD, POS, NP, LEMMA = 0, 1, 2, 3\n\n\ndef fd(sent, index, length):\n    \"\"\"Feature detector from ZJ03\n    \"\"\"\n    context = lambda idx, field: sent[index + idx][field] \\\n              if index+idx >= 0 and index + idx < length \\\n              else \"<s>\" if index+idx < 0 \\\n              else \"</s>\"\n\n    def bow(i, j):\n        bows = []\n        minidx = (index - i if index - i > 0 else 0)\n        maxidx = (index + i if index + i < length else length)\n        for w in sent[minidx:maxidx]:\n            bows.append(('bow%d-%d' % (i, j), w[WORD]))\n\n        return bows\n\n    ## tokens in a 5 token window w_{i-2}..w_{i+2}\n    w = context(0, WORD)\n    pre_w = context(-1, WORD)\n    pre_pre_w = context(-2, WORD)\n    post_w = context(1, WORD)\n    post_post_w = context(2, WORD)\n\n    ## token bigrams in a 5 token window\n    pre_pre_bigram = \"/\".join([pre_pre_w, pre_w])\n    pre_bigram = \"/\".join([pre_w, w])\n    post_bigram = \"/\".join([w, post_w])\n    post_post_bigram = \"/\".join([post_w, post_post_w])\n\n    ## length of w\n    wlen = len(w)\n\n    ## pos in a 5 token window\n    pos = context(0, POS)\n    pre_pos = context(-1, POS)\n    post_pos = context(1, POS)\n    pre_pre_pos = context(-2, POS)\n    post_post_pos = context(2, POS)\n\n    pre_pos_bigram = \"/\".join([pre_pos, pos])\n    post_pos_bigram = \"/\".join([pos, post_pos])\n    pre_pre_pos_bigram = \"/\".join([pre_pre_pos, pre_pos])\n    post_post_pos_bigram = \"/\".join([post_pos, post_post_pos])\n    pos_w = \"/\".join([w, pos])\n\n    ## Word shape features (5 token window)\n    istitle = w.istitle()\n    isdigit = w.isdigit()\n    isupper = w.isupper()\n    isalnum = w.isalnum()\n    hyphen = \"-\" in w[1:-1]\n    is2digit = isdigit and len(w)==2\n    is4digit = isdigit and len(w)==4\n\n    pre_istitle = pre_w.istitle()\n    pre_isdigit = pre_w.isdigit()\n    pre_isupper = pre_w.isupper()\n    pre_hyphen = \"-\" in pre_w[1:-1]\n    pre_isalnum = pre_w.isalnum()\n    pre_is2digit = pre_isdigit and len(pre_w)==2\n    pre_is4digit = pre_isdigit and len(pre_w)==4\n\n    pre_pre_istitle = pre_pre_w.istitle()\n    pre_pre_isdigit = pre_pre_w.isdigit()\n    pre_pre_isupper = pre_pre_w.isupper()\n    pre_pre_hyphen = \"-\" in pre_pre_w[1:-1]\n    pre_pre_isalnum = pre_pre_w.isalnum()\n    pre_pre_is2digit = pre_pre_isdigit and len(pre_pre_w)==2\n    pre_pre_is4digit = pre_pre_isdigit and len(pre_pre_w)==4\n\n    post_istitle = post_w.istitle()\n    post_isdigit = post_w.isdigit()\n    post_isupper = post_w.isupper()\n    post_hypen = \"-\" in post_w[1:-1]\n    post_isalnum = post_w.isalnum()\n    post_is2digit = post_isdigit and len(post_w)==2\n    post_is4digit = post_isdigit and len(post_w)==4\n\n    post_post_istitle = post_post_w.istitle()\n    post_post_isdigit = post_post_w.isdigit()\n    post_post_isupper = post_post_w.isupper()\n    post_post_hypen = \"-\" in post_post_w[1:-1]\n    post_post_isalnum = post_post_w.isalnum()\n    post_post_is2digit = post_post_isdigit and len(post_post_w)==2\n    post_post_is4digit = post_post_isdigit and len(post_post_w)==4\n\n    ## 2-4 suffixes in a 4 token window\n    w_suffix1 = w[-1:]\n    w_suffix2 = w[-2:]\n    w_suffix3 = w[-3:]\n    w_suffix4 = w[-4:]\n\n    pre_w_suffix1 = pre_w[-1:]\n    pre_w_suffix2 = pre_w[-2:]\n    pre_w_suffix3 = pre_w[-3:]\n    pre_w_suffix4 = pre_w[-4:]\n\n    post_w_suffix1 = post_w[-1:]\n    post_w_suffix2 = post_w[-2:]\n    post_w_suffix3 = post_w[-3:]\n    post_w_suffix4 = post_w[-4:]\n\n    pre_pre_w_suffix1 = pre_pre_w[-1:]\n    pre_pre_w_suffix2 = pre_pre_w[-2:]\n    pre_pre_w_suffix3 = pre_pre_w[-3:]\n    pre_pre_w_suffix4 = pre_pre_w[-4:]\n\n    post_post_w_suffix1 = post_post_w[-1:]\n    post_post_w_suffix2 = post_post_w[-2:]\n    post_post_w_suffix3 = post_post_w[-3:]\n    post_post_w_suffix4 = post_post_w[-4:]\n\n    ## 2-4 suffixes in a 4 token window\n    w_suffix1 = w[:1]\n    w_suffix2 = w[:2]\n    w_suffix3 = w[:3]\n    w_suffix4 = w[:4]\n\n    pre_w_suffix1 = pre_w[:1]\n    pre_w_suffix2 = pre_w[:2]\n    pre_w_suffix3 = pre_w[:3]\n    pre_w_suffix4 = pre_w[:4]\n\n    post_w_suffix1 = post_w[:1]\n    post_w_suffix2 = post_w[:2]\n    post_w_suffix3 = post_w[:3]\n    post_w_suffix4 = post_w[:4]\n\n    pre_pre_w_suffix1 = pre_pre_w[:1]\n    pre_pre_w_suffix2 = pre_pre_w[:2]\n    pre_pre_w_suffix3 = pre_pre_w[:3]\n    pre_pre_w_suffix4 = pre_pre_w[:4]\n\n    post_post_w_suffix1 = post_post_w[:1]\n    post_post_w_suffix2 = post_post_w[:2]\n    post_post_w_suffix3 = post_post_w[:3]\n    post_post_w_suffix4 = post_post_w[:4]\n\n    ## Noun phrase in a 3 token window\n    np = context(0,NP)\n    np_w = \"/\".join([np, w])\n    pre_np = context(-1, NP)\n    post_np = context(1, NP)\n\n    ## Extract features from local scope\n    features = locals()\n    del features[\"context\"]\n    del features['bow']\n    del features[\"sent\"]\n    del features[\"index\"]\n    del features[\"length\"]\n    features = features.items()\n\n    features.extend(bow(-4, -1))\n    features.extend(bow(1, 4))\n    \n    return features\n\n\ndef hd_az05(tags, sent, index, length, occurrences={}):\n    context = lambda idx, field: sent[index + idx][field] \\\n              if index+idx >= 0 and index + idx < length \\\n              else \"<s>\" if index+idx < 0 \\\n              else \"</s>\"\n\n    pre_tag = tags[index - 1] if index - 1 >= 0 else \"<s>\"\n    pre_pre_tag = tags[index - 2] if index - 2 >= 0 else \"<s>\"\n    pre_tag_w = \"/\".join([pre_tag, context(0, WORD)])\n    tag_bigram = \"/\".join([pre_pre_tag, pre_tag])\n    \n    history = [(\"pre_tag\", pre_tag), (\"pre_pre_tag\", pre_pre_tag),\n               (\"pre_tag_w\", pre_tag_w), (\"tag_bigram\", tag_bigram)]\n    return history\n\n\n","repo_name":"pprett/nut","sub_path":"nut/ner/features/az05.py","file_name":"az05.py","file_ext":"py","file_size_in_byte":5753,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"77"}
+{"seq_id":"71558929208","text":"# 用户输入\nname = input('Tell me something, and I will repeat it back to you: ')\nprint(\"Hello, \" + name + \"!\")\n\nprompt = \"If you tell us who you are, we can personalize the messages you see.\"\nprompt += \"\\nWhat is your first name? \"\nname = input(prompt)\nprint(\"\\nHello, \" + name + \"!\")\n\nage = input(\"How old are you? \")\nage = int(age)\nif age > 18:\n    print(\"恭喜你! 你已经是成年人了!\")\nelse:\n    print(\"你还是个孩子!\")\n\ncar = input(\"请问你要租赁什么样的车? \")\nprint(\"Let me see if I can find you a \" + car)\n\npeople = input(\"请问有多少人用餐? \")\npeople = int(people)\nif people > 8:\n    print(\"抱歉, 现在没有空桌子了.\")\nelse:\n    print(\"好的, 请来这坐.\")\n\nnumber = input('请输入一个数字: ')\nnumber = int(number)\nif number % 10 == 0:\n    print('你输入的数是10的倍数')\nelse:\n    print(\"你输入的不是10的倍数\")\n\n# while 循环\ncurrent_number = 1\nwhile current_number <= 5:\n    print(current_number, end=' ')\n    current_number += 1\nprint()\n\nunconfirmed_users = ['alice', 'brian', 'candace']\nconfirmed_users = []\nwhile unconfirmed_users:\n    current_user = unconfirmed_users.pop()\n    print(\"Verifying user: \" + current_user.title())\n    confirmed_users.append(current_user)\nprint(\"\\nThe following users hava been confirmed:\")\nfor confirmed_user in confirmed_users:\n    print(confirmed_user.title())\n\nresponses = {}\nwhile True:\n    name = input(\"Your name: \")\n    responses[name] = name\n    repeat = input(\"你是否继续?(yse/no) \")\n    if repeat == 'no':\n        break\n\nsandwich_orders = ['tuna', 'salmon', 'egg', 'beff', 'vegetables']\nfinished_sandwiches = []\nwhile sandwich_orders:\n    sandwich_order = sandwich_orders.pop()\n    finished_sandwiches.append(sandwich_order)\n    print(\"I made your \" + sandwich_order + \" sandwich\")\n\nprint(\"Pastrami is over!\")\nsandwich_orders_other = ['tuna', 'pastrami', 'salmon', 'pastrami', 'egg',\n                         'pastrami', 'beff', 'pastrami', 'vegetables']\nprint(\"sandwich_orders_other: \" + str(sandwich_orders_other))\nwhile \"pastrami\" in sandwich_orders_other:\n    sandwich_orders_other.remove(\"pastrami\")\nprint(\"now sandwich_orders_other\" + str(sandwich_orders_other))\n\ntravel = {}\nwhile True:\n    name = input(\"Place enter your name: \")\n    tourist = input(\"If you could cisit one place in the world, where would you go?\"\n                    \"if you haven't want to place, Place enter no!\")\n    if tourist == 'no':\n        break\n    else:\n        travel[name] = tourist\n","repo_name":"KokoroWJY/BokunoPy","sub_path":"Learn_basis_turtle/ClassMooc/WjyFour.py","file_name":"WjyFour.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"20243898247","text":"#!/usr/bin/python3\n\n\ndef safe_print_integer(value):\n    \"\"\"prints an integer with \"{:d}\".format()\n\n    Arguments:\n        value (int): the int to be printed\n\n    Returns:\n        if TypeErrors or ValueErrors occur - False, Otherwise - True\n    \"\"\"\n    try:\n        print(\"{:d}\".format(value))\n        return (True)\n    except (TypeError, ValueError):\n        return (False)\n","repo_name":"Onalo23/alx-higher_level_programming","sub_path":"0x05-python-exceptions/1-safe_print_integer.py","file_name":"1-safe_print_integer.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29787297397","text":"import argparse\nimport random\nimport numpy as np\nimport pandas as pd\nimport json\n\nfrom collections import defaultdict\nfrom tqdm import tqdm\n\nrandom.seed(0)\nnp.random.seed(0)\n\nBASE_TIME = 0.0\nMAX_TIME = 2572086.0\nDROP_OUT_MARK = 100\n\ndef get_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--dat_dir', default='./data/', type=str, help=\"Directory where 'mooc_actions.tsv' and 'mooc_action_labels.tsv' are located.\")\n    parser.add_argument('--out_dir', default='./', type=str, help=\"Directory where results will be saved.\")\n    parser.add_argument('--min_events', default=5, type=int, help=\"Minimum number of events needed to have a valid sequence for a user.\")\n    parser.add_argument('--max_events', default=200, type=int, help=\"Maximum number of events needed to have a valid sequence for a user.\")\n    parser.add_argument('--time_norm', default=60*60, type=int, help=\"Number of seconds per desired unit of time. Default is for 1 unit == 1 hour.\")\n    parser.add_argument('--valid_pct', default=0.1, type=float, help=\"Percentage of sequences to set aside for validation split.\")\n    parser.add_argument('--test_pct', default=0.15, type=float, help=\"Percentage of sequences to set aside for test split.\")\n    args = parser.parse_args()\n    args.dat_dir = args.dat_dir.rstrip(\"/\")\n    args.out_dir = args.out_dir.rstrip(\"/\")\n    return args\n\ndef collect_sequences(args):\n    drop_out_label_df = pd.read_csv(\"{}/mooc_action_labels.tsv\".format(args.dat_dir), sep='\\t')\n    drop_out_label = drop_out_label_df['LABEL'].to_dict()  # {actionid: 1 or 0 for whether dropout after the event}\n    first_line = True\n    sequences = defaultdict(list)\n    with open(\"{}/mooc_actions.tsv\".format(args.dat_dir)) as f:\n        for event in tqdm(f):\n            if first_line:\n                first_line = False\n                continue\n            action_id, user_id, mark_id, timestamp = event.strip().split(\"\\t\")\n            action_id, mark_id, timestamp = int(action_id), int(mark_id), float(timestamp)\n            if timestamp < BASE_TIME:\n                continue\n            # check events happen at the same time and shift accordingly\n            if sequences[user_id] and timestamp <= sequences[user_id][-1][0]:\n                timestamp = sequences[user_id][-1][0] + 0.5\n            sequences[user_id].append((timestamp, mark_id))\n            if drop_out_label[int(action_id)]:\n                sequences[user_id].append((timestamp, DROP_OUT_MARK))  # add a label to set T as the last event time\n    return sequences\n\n\ndef process_sequences(args, sequences):\n    print(\"   Num Sequences prior to Filtering:\", len(sequences))\n    for u in tqdm(list(sequences.keys())):\n        if args.min_events <= len(sequences[u]) <= args.max_events:\n            sequences[u] = list(zip(*sorted(sequences[u], key=lambda x: x[0])))  # Sort by timestamps and then split into two lists: one for timestmaps and one for marks\n            sequences[u][0] = [(t - BASE_TIME) / args.time_norm for t in sequences[u][0]]\n        else:\n            del sequences[u]\n    print(\"   Num Sequences after Filtering:\", len(sequences))\n    return sequences\n\n\ndef format_sequence(args, sequence, user_id):\n    return json.dumps({\n        \"user\": user_id,\n        \"T\": (MAX_TIME - BASE_TIME) / args.time_norm if sequence[1][-1] != 100 else sequence[0][-1],\n        \"times\": sequence[0] if sequence[1][-1] != 100 else sequence[0][:-1],\n        \"marks\": sequence[1] if sequence[1][-1] != 100 else sequence[1][:-1],\n    }) + \"\\n\"\n\n\n\nif __name__==\"__main__\":\n    args = get_args()\n    print(\"Gathering Event Sequences\")\n    sequences = collect_sequences(args)\n    print(\"Processing Event Sequences\")\n    sequences = process_sequences(args, sequences)\n    # print(sequences)\n\n\n    all_strs, train_strs, valid_strs, test_strs = [], [], [], []\n\n    sequences = list(sequences.items())\n    random.shuffle(sequences)\n\n    for i, (user_id, sequence) in tqdm(enumerate(sequences)):\n        seq_str = format_sequence(args, sequence, user_id)\n        all_strs.append(seq_str)\n        progress = (i + 1) / len(sequences)\n        if progress <= args.test_pct:\n            test_strs.append(seq_str)\n        elif progress <= args.test_pct + args.valid_pct:\n            valid_strs.append(seq_str)\n        else:\n            train_strs.append(seq_str)\n\n    dir_strs = [\n        (\"{}/all_sequences.jsonl\", all_strs),\n        (\"{}/train/train_mooc.jsonl\", train_strs),\n        (\"{}/valid/valid_mooc.jsonl\", valid_strs),\n        (\"{}/test/test_mooc.jsonl\", test_strs),\n    ]\n\n    for dir, strs in dir_strs:\n        with open(dir.format(args.out_dir), \"w\") as f:\n            f.writelines(strs)","repo_name":"ajboyd2/point_process_queries","sub_path":"data/mooc/parse_sequences.py","file_name":"parse_sequences.py","file_ext":"py","file_size_in_byte":4649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74213090168","text":"'''\r\nUnittests for StringChanges.py\r\nDecember 2020 Jakub Kazimierski\r\n'''\r\n\r\nimport unittest\r\nimport StringChanges\r\n\r\nclass test_StringChanges(unittest.TestCase):    \r\n    '''\r\n    Class with unittests for StringChanges.py\r\n    '''\r\n\r\n    # region Unittests\r\n    def test_ExpectedOutput(self):\r\n        '''\r\n        Checks if returned output is as expected.\r\n        '''\r\n        output = StringChanges.StringChanges(\"abcNdgM\")\r\n        self.assertEqual(output, \"abcgg\")\r\n\r\n    \r\n    def test_WrongInput(self):\r\n        '''\r\n        Checks if output is equal -1 for wrong input.\r\n        '''\r\n        output = StringChanges.StringChanges(12)\r\n        self.assertEqual(output, -1)\r\n\r\n    # endregion\r\n\r\nif __name__ == \"__main__\":\r\n    '''\r\n    Main method for test cases.\r\n    '''\r\n    unittest.main()","repo_name":"JakubKazimierski/PythonPortfolio","sub_path":"Coderbyte_algorithms/Easy/StringChanges/test_StringChanges.py","file_name":"test_StringChanges.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"38470907965","text":"import concurrent.futures\nfrom concurrent.futures import ThreadPoolExecutor\nimport multiprocessing\n\n\nclass ROI:\n    def __init__(self, wager, odd, roi_array, returns):\n        self.wager = wager\n        self.odd = odd\n        self.roi_array = roi_array\n        self.returns = returns\n\n    def __str__(self):\n        return \"wager: {0}, \\n\" \\\n               \"odd: {1}\\n\" \\\n               \"returns: {2},\\n\" \\\n               \"roi_array: {3}\".format(\n            self.wager, self.odd, self.returns, self.roi_array\n        )\n\n    def __repr__(self):\n        return self.__str__()\n\n\ndef is_eligible(arr, total, risk):\n    for i in range(0, total):\n        if arr[i] + risk[i] < 0:\n            return False\n\n    return True\n\n\ndef analyze(odds, wagers, no_of_buckets, total_wager, topx=10, risk=[]):\n    output = []\n    # print(\"Wagers: \", len(wagers))\n    # print(\"Odds: \", len(odds))\n    thread_group_size = multiprocessing.cpu_count()\n    if len(wagers) > thread_group_size:\n        step_size = len(wagers) // thread_group_size\n    else:\n        step_size = len(wagers)\n\n    if no_of_buckets != len(risk):\n        raise Exception(\n            \"Length of risk indicators: %d should be same as no_of_buckets: %d\" % (len(risk), no_of_buckets))\n\n    with ThreadPoolExecutor() as executor:\n        future_to_num = {\n            executor.submit(zip_wagers_to_odds, odds, wagers, no_of_buckets, total_wager, j, j + step_size, risk): j for\n            j in\n            range(0, len(wagers), step_size)}\n        #        print(\"Number of Jobs submitted: \", len(future_to_num))\n        count = 0\n        for future in concurrent.futures.as_completed(future_to_num):\n            num = future_to_num[future]\n            try:\n                result = future.result()\n                count = count + 1\n                if len(result) > 0:\n                    sorted_result = sorted(result, key=lambda deal: min(deal.roi_array), reverse=True)[:topx]\n                    output += sorted_result\n                    # print(\"Output Received: \", count, len(sorted_result))\n                    # print(\"#\", end=\"\")\n                # else:\n                #     print(\".\", end=\"\")\n            except Exception as exc:\n                print('%r generated an exception: %s' % (num, exc))\n    print(\"\")\n    return output\n\n\ndef zip_wagers_to_odds(odds, wagers, no_of_buckets, total_wager, a, b, risk=[]):\n    output = []\n    # print(*odds, sep=\"\\n\\n\")\n    for i in range(a, b):\n        if i < len(wagers):\n            wager = wagers[i]\n            for odd in odds:\n                prod = []\n                returns = []\n                for j in range(0, len(odd)):\n                    return_value = float(wager[j] * float(odd[j].f_odd))\n                    returns += [return_value]\n                    roi_in_percentage = ((return_value - total_wager) / total_wager) * 100\n                    prod += [roi_in_percentage]\n\n                if is_eligible(prod, no_of_buckets, risk):\n                    # print ((wager, prod)#)\n                    output += [ROI(wager, odd, prod, returns)]\n    return output\n\n\ndef get_wagers(min_wager, max_wager, no_of_buckets):\n    if no_of_buckets >= 2:\n        return generate_wagers(min_wager, max_wager, no_of_buckets, [])\n    else:\n        return []\n\n\ndef generate_wagers(min_wager, max_wager, no_of_buckets, current):\n    output = []\n    for i in range(1, max_wager):\n        if no_of_buckets == 1:\n            current_sum = sum(current) + i\n            if current_sum >= min_wager and current_sum == max_wager:\n                output.append(current + [i])\n        else:\n            output += generate_wagers(min_wager, max_wager, no_of_buckets - 1, current + [i])\n\n    return output\n","repo_name":"Tyagi-Gaurav/python-practice","sub_path":"betp/src/analyze_with_wagers.py","file_name":"analyze_with_wagers.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22292521621","text":"import jax.numpy as jnp\n\nfrom flowjax.masks import block_diag_mask, block_tril_mask, rank_based_mask\n\n\ndef test_rank_based_mask():\n    in_ranks = jnp.arange(2)\n    out_ranks = jnp.array([0, 1, 1, 2])\n\n    expected_mask = jnp.array([[0, 0], [1, 0], [1, 0], [1, 1]], dtype=jnp.int32)\n\n    mask = rank_based_mask(in_ranks, out_ranks)\n    assert jnp.all(expected_mask == mask)\n\n    in_ranks = jnp.array([0, 0, 1, 1])\n    out_ranks = jnp.array([0, 1])\n\n    expected_mask = jnp.array([[0, 0, 0, 0], [1, 1, 0, 0]], dtype=jnp.int32)\n    mask = rank_based_mask(in_ranks, out_ranks)\n    assert jnp.all(expected_mask == mask)\n\n\ndef test_block_tril_mask():\n    args = [(1, 2), 3]\n    expected = jnp.array([[0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0]])\n    result = block_tril_mask(*args)\n    assert jnp.all(expected == result)\n\n\ndef test_block_diag_mask():\n    args = [(1, 2), 3]\n    expected = jnp.array([[1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1]])\n    result = block_diag_mask(*args)\n    assert jnp.all(expected == result)\n","repo_name":"danielward27/flowjax","sub_path":"tests/test_masks.py","file_name":"test_masks.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"77"}
+{"seq_id":"40004934269","text":"# -*- coding: utf-8 -*-\nfrom .models import Configures\nfrom rest_framework import serializers\nfrom interfaces.models import Interfaces\n\nclass InterfaceAnotherSerializer(serializers.ModelSerializer):\n\tproject = serializers.StringRelatedField(label='项目名称', help_text='项目名称')\n\tpid = serializers.IntegerField(write_only=True, label='项目id', help_text='项目id')\n\tiid = serializers.IntegerField(write_only=True, label='接口id', help_text='接口id')\n\n\tclass Meta:\n\t\tmodel = Interfaces\n\t\tfields = ('name', 'project', 'pid', 'iid')\n\n\t\textra_kwargs = {\n\t\t\t'name': {\n\t\t\t\t'read_only': True,\n\t\t\t},\n\t\t}\n\n\t# 需要校验接口id和项目id是否匹配\n\tdef validate(self, attrs):\n\t\t\"\"\"\n\t\t校验项目ID是否与接口ID一致\n\t\t:param attrs:\n\t\t:return:\n\t\t\"\"\"\n\t\tif not Interfaces.objects.filter(id=attrs['iid'], project_id=attrs['pid']).exists():\n\t\t\traise serializers.ValidationError('项目和接口信息不对应！')\n\t\treturn attrs\n\nclass ConfiguresSerializer(serializers.ModelSerializer):\n\t\"\"\"\n\t配置序列化器\n\t\"\"\"\n\tinterface = InterfaceAnotherSerializer(help_text='所属项目和接口')\n\n\tclass Meta:\n\t\tmodel = Configures\n\t\tfields = ('id', 'name', 'author', 'request', 'interface')\n\t\textra_kwargs = {\n\t\t\t'request': {\n\t\t\t\t'write_only': True\n\t\t\t}\n\t\t}\n\n\tdef create(self, validated_data):\n\t\tinterface_dict = validated_data.pop('interface')\n\t\tvalidated_data['interface_id'] = interface_dict['iid']\n\t\treturn Configures.objects.create(**validated_data)\n\n\tdef update(self, instance, validated_data):\n\t\tif 'interface' in validated_data:\n\t\t\tinterface_dict = validated_data.pop('interface')\n\t\t\tvalidated_data['interface_id'] = interface_dict['iid']\n\t\treturn super().update(instance, validated_data)\n","repo_name":"xiaoqi2019/DjangoDev","sub_path":"apps/configures/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40658243690","text":"from flask import Flask, url_for, render_template, request\nimport os\nimport random\nimport requests\nfrom operator import xor\nfrom urllib.parse import urlencode\nfrom urllib.request import Request, urlopen\nfrom operator import xor\nimport ssl\n# from wireless import Wireless\nimport json\nimport requests\n\n\nimport time\nimport serial\nimport numpy as np\nser = serial.Serial(              \n               port='/dev/ttyUSB1',\n               baudrate = 19200,\n               parity=serial.PARITY_NONE,\n               stopbits=serial.STOPBITS_ONE,\n               bytesize=serial.EIGHTBITS,\n               timeout=1\n           )\n\napp = Flask(__name__)\n\nMAC = 'b8:27:eb:04:fb:62'\nheaders = {\n    \"content-type\": \"application/json\"\n}\ndef PUF(c):\n    ser.flush()\n    c1 = int(c)\n    c1 = np.uint16(c1)\n    print(\"writing challenge to serial output\")\n    print(c1)\n    ser.write(c1.tobytes())\n    ser.flush()\n    dummy = np.uint8(5)\n    print(\"sending dummy to serial output\")\n    print(dummy)\n    ser.write(dummy)\n    ser.flush()\n    # time.sleep(0.4)\n    response =ser.readline()\n    print(\"response received\")\n    print(response.decode(\"utf-8\") )\n    response =ser.readline()\n    print(\"response received\")\n    print(response.decode(\"utf-8\") )\n    response =ser.readline()\n    print(\"response received\")\n    print(response.decode(\"utf-8\") )\n    return str(response.decode(\"utf-8\"))\n\n@app.route(\"/\")\ndef homepage():\n    return render_template('client_connect.html')\n\n@app.route('/connect', methods=[\"GET\", \"POST\"])\ndef connect():\n    if request.method == 'POST':\n        context = ssl._create_unverified_context()\n        post_fields = {'MAC':MAC}\n        \n        request1 = Request('http://172.16.12.37:5000/exchange1', urlencode(post_fields).encode())\n        jsony = urlopen(request1,context=context).read()\n        \n        jsony = jsony.decode(\"utf-8\") \n        jsony = json.loads(jsony)\n        C1_ = jsony['C1_']\n        C2_ = jsony['C2_']\n        R2_ = jsony['R2_']\n        R3_ = jsony['R3_']\n        C3  = jsony['C3']\n        R3 = PUF(C3)\n        print(\"R3\")\n        print(R3)\n        nonce = int(R3) ^ int(R3_)\n        print(\"nonce\")\n        print(nonce)\n        C2 = int(C2_) ^ int(nonce)\n        print(\"C2\")\n        print(C2)\n        R2 = int(R2_) ^ int(nonce)\n        print(\"R2\")\n        print(R2)\n        print(PUF(C2))\n        #if R2 != PUF(C2):\n        #    return \"Router is not Authentic\"\n        C1 = nonce ^ int(C1_)\n        R1 = PUF(str(C1))\n        R1_ = nonce ^ int(R1)\n        print(\"C1\")\n        print(C1)\n        print(\"R1\")\n        print(\"R1_\")\n        print(R1)\n        print(R1_)\n        R1new = PUF(int(C1) ^ int(C3))\n        R2new = PUF(int(C2) ^ int(C3))\n        R3new = PUF(nonce ^ int(C3))\n        R1new_ = nonce ^ int(R1new)\n        R2new_ = nonce ^ int(R2new)\n        R3new_ = nonce ^ int(R3new)\n        print(\"R1new\")\n        print(R1new)\n        print(R1new_)\n        print(\"R2new\")\n        print(R2new)\n        print(R2new_)\n        print(\"R3new\")\n        print(R3new)\n        print(R3new_)\n        Hclient = int(R1new)| int(R2new)| int(R3new)| int(R1)\n        post_fields = {'MAC':MAC,'R1new_':str(R1new_),'R2new_':str(R2new_),'R3new_':str(R3new_),'Hclient':str(Hclient),'R1_':str(R1_)}\n        print(post_fields)\n        request2 = Request('http://172.16.12.37:5000/exchange2', data=urlencode(post_fields).encode())\n        response2 = urlopen(request2).read()\n        return response2          \n    return \"not a post request\"\n                \n        \n        \n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=8000)\n","repo_name":"shreyasaha1997/ISED-Conference","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29306661699","text":"#Cajero Automático\nimport sys\n\na=input(\"Ingrese usuario: \")\nb=input(\"Ingrese clave: \")\nc=0\ns=1000000\nwhile b!=\"1803\":\n    c+=1\n    if c==3:\n        print(\"tarjeta bloqueada\")\n        sys.exit(1)\n    else:\n        print(\"clave incorrecta\")\n        b=input(\"Ingrese clave: \")\nm=int(input(\"Monto a retirar: \"))\nif m>100000:\n    print(\"Monto no permitido\")\nelse:\n    cajero=s-m\n    cuenta=100000-m\n    print(\"saldo cuenta=\",cuenta)\n    print(\"saldo cajero=\",cajero)\n    g=m//20000\n    f=m%20000\n    h=f//10000\n    j=f%10000\n    k=j//5000\n    print(\"billetes 20000=\",g)\n    print(\"billetes 10000=\",h)\n    print(\"billetes 5000=\",k)","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej11/hito1_ej11_85e628b9a69e69bf2ed9360f397dde90.py","file_name":"hito1_ej11_85e628b9a69e69bf2ed9360f397dde90.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45910622136","text":"import streamlit as st\r\nimport pandas as pd\r\nfrom sklearn import datasets\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom st_aggrid import AgGrid, GridUpdateMode, JsCode\r\nfrom st_aggrid.grid_options_builder import GridOptionsBuilder\r\n\r\nst.write(\"\"\"\r\n# Simple Iris Flower Prediction App\r\n\r\nThis app predicts the **Iris flower** type!\r\n\"\"\")\r\n\r\nst.sidebar.header('User Input Parameters')\r\n\r\n@st.experimental_memo\r\ndef convert_df(df):\r\n   return df.to_csv(index=False).encode('utf-8')\r\n\r\n\r\ndef user_input_features():\r\n    sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)\r\n    sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)\r\n    petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)\r\n    petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2)\r\n    data = {'sepal_length': sepal_length,\r\n            'sepal_width': sepal_width,\r\n            'petal_length': petal_length,\r\n            'petal_width': petal_width}\r\n    features = pd.DataFrame(data, index=[0])\r\n    return features\r\n\r\ndf = user_input_features()\r\n\r\nst.subheader('User Input parameters')\r\n\r\ngd = GridOptionsBuilder.from_dataframe(df)\r\ngd.configure_pagination(enabled=True)\r\ngd.configure_default_column(editable=True, groupable=True)\r\ngd.configure_selection(selection_mode=\"multiple\")\r\ngd.configure_column(\"Option\", editable=True, cellEditor=\"agSelectCellEditor\", cellEditorParams={\"values\": [\"One\",\"Two\"] })\r\n\r\ngridoptions = gd.build()\r\ngrid_table = AgGrid(\r\n    df,\r\n    gridOptions=gridoptions,\r\n    update_mode=GridUpdateMode.SELECTION_CHANGED,\r\n    theme=\"material\",\r\n)\r\noptions = [st.selectbox(\r\n    'How would you like to be contacted?',\r\n    ('Email', 'Home phone', 'Mobile phone'))]\r\ndf['Options'] = options\r\nedited_df = st.experimental_data_editor(df, num_rows=\"dynamic\")\r\n\r\n\r\n\r\nst.download_button(\r\n   \"Press to Download\",\r\n   convert_df(grid_table['data']),\r\n   \"file.csv\",\r\n   \"text/csv\",\r\n   key='download-csv'\r\n)\r\n\r\niris = datasets.load_iris()\r\nX = iris.data\r\nY = iris.target\r\n\r\nclf = RandomForestClassifier()\r\nclf.fit(X, Y)\r\n\r\nprediction = clf.predict(df)\r\nprediction_proba = clf.predict_proba(df)\r\n\r\nst.subheader('Class labels and their corresponding index number')\r\nst.write(iris.target_names)\r\n\r\nst.subheader('Prediction')\r\nst.write(iris.target_names[prediction])\r\n#st.write(prediction)\r\n\r\nst.subheader('Prediction Probability')\r\nst.write(prediction_proba)\r\n","repo_name":"KYKong97/AirAsiaTraining","sub_path":"iris-ml-app.py","file_name":"iris-ml-app.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22851223862","text":"import numpy as np\nimport tensorflow as tf\nimport utils_cora\n\ntraining_epochs = 200\nbatch_size = 8\nembed_dim = 128\nclass_dim = 7\nlearning_rate = tf.train.exponential_decay(\n    learning_rate=0.005, global_step=10000, decay_steps=100, decay_rate=0.98, staircase=True)\n\nFLAGS = tf.flags.FLAGS\ntf.flags.DEFINE_string(\n    'method', 'DeepWalk', 'Method selected from DeepWalk, LINE and node2vec, default node2vec.')\n\ndata = utils_cora.Dataset(FLAGS.method)\nif FLAGS.method == 'LINE':\n    embed_dim *= 2\nX = tf.placeholder(tf.float32, [None, embed_dim])\ny = tf.placeholder(tf.int32, [None, ])\nlabels = tf.one_hot(y, class_dim, axis=1)\nW = tf.Variable(tf.zeros([embed_dim, class_dim]))\nb = tf.Variable(tf.zeros([class_dim]))\nlogits = tf.matmul(X, W) + b\nloss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)\noptimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n    step = 0\n    for epoch in range(training_epochs):\n        avg_cost = 0\n        train_len = len(data.y)\n        permutation = np.random.permutation(train_len)\n        train_X = data.X[permutation]\n        train_y = data.y[permutation]\n        data_idx = 0\n        while data_idx < train_len - 1:\n            X_batch = train_X[data_idx: np.clip(data_idx + batch_size, 0, train_len - 1)]\n            y_batch = train_y[data_idx: np.clip(data_idx + batch_size, 0, train_len - 1)]\n            data_idx += batch_size\n            batch_loss, _ = sess.run([loss, optimizer], feed_dict={X: X_batch, y: y_batch})\n            if step == 0:\n                print('Step %5d: initial loss = %.5f' % (step, batch_loss))\n            step += 1\n            if step % 10 == 0:\n                print('\\rStep %5d: loss = %.5f' % (step, batch_loss), end='', flush=True)\n\n    print(\"\\nOptimization Finished.\")\n\n    # Test model\n    correct_pre = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))\n    accuracy = tf.reduce_mean(tf.cast(correct_pre, tf.float32))\n    print(\"Accuracy:\", accuracy.eval({X: data.test_X, y: data.test_y}))\n","repo_name":"yikaiw/NRL-implement","sub_path":"LRclassifier.py","file_name":"LRclassifier.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"77"}
+{"seq_id":"24810520259","text":"from dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Optional, List, Sequence, Any\nimport numpy as np\nfrom tabulate import tabulate\n\nfrom pyqumo.matrix import str_array\nfrom pyqumo.random import CountableDistribution, Distribution\nfrom pyqumo.sim.helpers import build_statistics, FiniteFifoQueue, \\\n    InfiniteFifoQueue, Server, Queue, TimeSizeRecords, Statistics\n\n\n@dataclass\nclass Packet:\n    \"\"\"\n    Packet representation for G/G/1/N model.\n\n    Stores timestamps: when the packet arrived (created_at), started\n    serving (service_started_at) and finished serving (departed_at).\n\n    Also stores flags, indicating whether the packet was dropped or was\n    completely served. Packets arrived in the end of the modeling, may not\n    be served, as well as dropped packets.\n    \"\"\"\n    created_at: float\n    service_started_at: Optional[float] = None\n    departed_at: Optional[float] = None\n    dropped: bool = False\n    served: bool = False\n\n\nclass Records:\n    \"\"\"\n    Records for G/G/1/N statistical analysis.\n    \"\"\"\n    def __init__(self):\n        self._packets: List[Packet] = []\n        self._system_size = TimeSizeRecords()\n\n    def add_packet(self, packet: Packet):\n        self._packets.append(packet)\n\n    @property\n    def system_size(self):\n        return self._system_size\n\n    @property\n    def packets(self):\n        return self._packets\n\n\n@dataclass\nclass Params:\n    \"\"\"\n    Model parameters: arrival and service processes, queue capacity and limits.\n    \"\"\"\n    arrival: Any\n    service: Any\n    queue_capacity: int\n    max_packets: int = 1000000\n    max_time: float = np.inf\n\n\n@dataclass\nclass Results:\n    \"\"\"\n    Results returned from G/G/1/N model simulation.\n    \"\"\"\n    system_size: Optional[CountableDistribution] = None\n    queue_size: Optional[CountableDistribution] = None\n    busy: Optional[CountableDistribution] = None\n    loss_prob: float = 0.0\n    departures: Optional[Statistics] = None\n    response_time: Optional[Statistics] = None\n    wait_time: Optional[Statistics] = None\n    real_time: Optional[float] = 0.0\n\n    @property\n    def utilization(self) -> float:\n        \"\"\"\n        Get utilization coefficient, that is `Busy = 1` probability.\n        \"\"\"\n        return self.busy.pmf(1)\n\n    def tabulate(self) -> str:\n        \"\"\"\n        Build a pretty formatted table with all key properties.\n        \"\"\"\n        system_size_pmf = [\n            self.system_size.pmf(x)\n            for x in range(self.system_size.truncated_at + 1)]\n        queue_size_pmf = [\n            self.queue_size.pmf(x)\n            for x in range(self.queue_size.truncated_at + 1)]\n\n        items = [\n            ('System size PMF', str_array(system_size_pmf)),\n            ('System size average', self.system_size.mean),\n            ('System size std.dev.', self.system_size.std),\n            ('Queue size PMF', str_array(queue_size_pmf)),\n            ('Queue size average', self.queue_size.mean),\n            ('Queue size std.dev.', self.queue_size.std),\n            ('Utilization', self.utilization),\n            ('Loss probability', self.loss_prob),\n            ('Departures, average', self.departures.avg),\n            ('Departures, std.dev.', self.departures.std),\n            ('Response time, average', self.response_time.avg),\n            ('Response time, std.dev.', self.response_time.std),\n            ('Wait time, average', self.wait_time.avg),\n            ('Wait time, std.dev.', self.wait_time.std),\n            (\"Execution time, ms.\", self.real_time),\n        ]\n        return tabulate(items, headers=('Param', 'Value'))\n\n\nclass Event(Enum):\n    STOP = 0\n    ARRIVAL = 1\n    SERVICE_END = 2\n\n\nclass System:\n    \"\"\"\n    System state representation.\n\n    This object takes care of the queue, current time, next arrival and\n    service end time, server status and any other kind of dynamic information,\n    except for internal state of arrival or service processes.\n    \"\"\"\n    def __init__(self, params: Params):\n        \"\"\"\n        Constructor.\n\n        Parameters\n        ----------\n        params : Params\n            Model parameters\n        \"\"\"\n        if params.queue_capacity < np.inf:\n            self._queue = FiniteFifoQueue(params.queue_capacity)\n        else:\n            self._queue = InfiniteFifoQueue()\n\n        self._time: float = 0.0\n        self._service_end: Optional[float] = None\n        self._next_arrival: Optional[float] = 0.0\n        self._server: Server[Packet] = Server()\n        self._stopped: bool = False\n\n    @property\n    def server(self) -> Server[Packet]:\n        return self._server\n\n    @property\n    def queue(self) -> Queue[Packet]:\n        return self._queue\n\n    @property\n    def time(self) -> float:\n        return self._time\n\n    @property\n    def stopped(self):\n        return self._stopped\n\n    @property\n    def size(self) -> int:\n        \"\"\"\n        Get system size, that is queue size plus one (busy) or zero (empty).\n        \"\"\"\n        return self.server.size + self.queue.size\n\n    def schedule(self, event: Event, interval: float) -> None:\n        \"\"\"\n        Schedule next event.\n\n        Parameters\n        ----------\n        event : Event\n        interval : float\n            Non-negative interval, after which the event will be fired.\n        \"\"\"\n        if interval < 0:\n            raise ValueError(f\"expected non-negative interval, but \"\n                             f\"{interval} found\")\n        if event == Event.ARRIVAL:\n            self._next_arrival = self._time + interval\n        elif event == Event.SERVICE_END:\n            self._service_end = self._time + interval\n        else:\n            raise ValueError(f\"unexpected event {event}\")\n\n    def stop(self) -> None:\n        self._stopped = True\n\n    def next_event(self) -> Event:\n        \"\"\"\n        Get next event type and move time to it.\n\n        Returns\n        -------\n        event : Event\n        \"\"\"\n        ts = self._service_end\n        ta = self._next_arrival\n\n        if ts is None and ta is None:\n            return Event.STOP\n\n        if ts is not None and (ta is None or ta > ts):\n            self._time = ts\n            self._service_end = None\n            return Event.SERVICE_END\n\n        # If we are here, TS is None, or TS is not None and NOT (ta > ts):\n        # this means, that arrival happens\n        self._time = self._next_arrival\n        self._next_arrival = None\n        return Event.ARRIVAL\n\n\ndef simulate(\n        arrival: Distribution,\n        service: Distribution,\n        queue_capacity: int = np.inf,\n        max_time: float = np.inf,\n        max_packets: int = 1000000\n) -> Results:\n    \"\"\"\n    Run simulation model of G/G/1/N system.\n\n    Simulation can be stopped in two ways: by reaching maximum simulation time,\n    or by reaching the maximum number of generated packets. By default,\n    simulation is limited with the maximum number of packets only (1 million).\n\n    Queue is expected to have finite capacity.\n\n    Arrival and service time processes can be of any kind, including Poisson\n    or MAP. To use a PH or normal distribution, a GenericIndependentProcess\n    model with the corresponding distribution may be used.\n\n    Parameters\n    ----------\n    arrival : RandomProcess\n        Arrival random process.\n    service : RandomProcess\n        Service time random process.\n    queue_capacity : int\n        Queue capacity.\n    max_time : float, optional\n        Maximum simulation time (default: infinity).\n    max_packets\n        Maximum number of simulated packets (default: 1'000'000)\n\n    Returns\n    -------\n    results : Results\n        Simulation results.\n    \"\"\"\n    params = Params(\n        arrival=arrival.rnd, service=service.rnd, queue_capacity=queue_capacity,\n        max_packets=max_packets, max_time=max_time)\n    system = System(params)\n    records = Records()\n\n    # Initialize model:\n    records.system_size.add(0.0, system.size)\n    system.schedule(Event.ARRIVAL, params.arrival.eval())\n\n    # Run simulation:\n    max_time = params.max_time\n    while not system.stopped:\n        event = system.next_event()\n\n        # Check whether event is scheduled too late, and we need to stop:\n        if system.time > max_time:\n            system.stop()\n            continue\n\n        # Process event\n        # print(f\"{system.time:.3f} [{event.name:5s}] \"\n        #       f\"server: {system.server.size}, \"\n        #       f\"queue: {system.queue.size}\")\n        if event == Event.ARRIVAL:\n            _handle_arrival(system, params, records)\n        elif event == Event.SERVICE_END:\n            _handle_service_end(system, params, records)\n        elif event == Event.STOP:\n            system.stop()\n\n    return _build_results(records)\n\n\ndef _handle_arrival(system: System, params: Params, records: Records):\n    \"\"\"\n    Handle new packet arrival event.\n\n    First of all, a new packet is created. Then we check whether the\n    system is empty. If it is, this new packet starts serving immediately.\n    Otherwise, it is added to the queue.\n\n    If the queue was full, the packet is dropped. To mark this, we set\n    `dropped` flag in the packet to `True`.\n\n    In the end we schedule the next arrival. We also check whether the\n    we have already generated enough packets. If so, `system.stopped` flag\n    is set to `True`, so on the next main loop iteration the simulation\n    will be stopped.\n\n    Parameters\n    ----------\n    system : System\n    params : Params\n    records : Records\n    \"\"\"\n    num_packets_built = len(records.packets)\n\n    # If too many packets were generated, ask to stop:\n    if num_packets_built >= params.max_packets:\n        system.stop()\n\n    time_now = system.time\n    packet = Packet(created_at=time_now)\n    records.add_packet(packet)\n\n    # If server is ready, start serving. Otherwise, push the packet into\n    # the queue. If the queue was full, mark the packet is being dropped\n    # for further analysis.\n    server = system.server\n    if server.ready:\n        # start serving immediately\n        server.serve(packet)\n        system.schedule(Event.SERVICE_END, params.service.eval())\n        packet.service_started_at = time_now\n        records.system_size.add(time_now, system.size)\n\n    elif system.queue.push(packet):\n        # packet was queued\n        records.system_size.add(time_now, system.size)\n\n    else:\n        # mark packet as being dropped\n        packet.dropped = True\n\n    # Schedule next arrival:\n    system.schedule(Event.ARRIVAL, params.arrival.eval())\n\n\ndef _handle_service_end(system: System, params: Params, records: Records):\n    \"\"\"\n    Handle end of the packet service.\n\n    If the queue is empty, the server becomes idle. Otherwise, it starts\n    serving the next packet from the queue.\n\n    The packet that left the server is marked as `served = True`.\n\n    Parameters\n    ----------\n    system : System\n    params : Params\n    records : Records\n    \"\"\"\n    time_now = system.time\n    server = system.server\n    queue = system.queue\n\n    packet = server.pop()\n    packet.served = True\n    packet.departed_at = time_now\n\n    # Start serving next packet, if exists:\n    packet = queue.pop()\n    if packet is not None:\n        server.serve(packet)\n        packet.service_started_at = time_now\n        system.schedule(Event.SERVICE_END, params.service.eval())\n\n    # Anyway, system size has changed - record it!\n    records.system_size.add(time_now, system.size)\n\n\ndef _get_departure_intervals(packets_list: Sequence[Packet]) -> List[float]:\n    \"\"\"\n    Build departures intervals sequence.\n\n    Parameters\n    ----------\n    packets_list : sequence of Packet\n\n    Returns\n    -------\n    intervals : sequence of float\n    \"\"\"\n    prev_time = 0.0\n    intervals = []\n    for packet in packets_list:\n        if packet.served:\n            intervals.append(packet.departed_at - prev_time)\n            prev_time = packet.departed_at\n    return intervals\n\n\ndef _build_results(records: Records) -> Results:\n    \"\"\"\n    Create results from the records.\n\n    Parameters\n    ----------\n    records : Records\n    \"\"\"\n    ret = Results()\n\n    #\n    # 1) Build system size, queue size and busy (server size)\n    #    distributions. To do this, we need PMFs. Queue size\n    #    PMF and busy PMF can be computed from system size PMF.\n    #\n    system_size_pmf = list(records.system_size.pmf)\n    num_states = len(system_size_pmf)\n    p0 = system_size_pmf[0]\n    p1 = system_size_pmf[1] if num_states > 1 else 0.0\n\n    queue_size_pmf = [p0 + p1] + system_size_pmf[2:]\n    server_size_pmf = [p0, sum(system_size_pmf[1:])]\n\n    ret.system_size = CountableDistribution(system_size_pmf)\n    ret.queue_size = CountableDistribution(queue_size_pmf)\n    ret.busy = CountableDistribution(server_size_pmf)\n\n    #\n    # 2) For future estimations, we need packets and some filters.\n    #    Group all of them here.\n    #\n    all_packets = records.packets\n    served_packets = [packet for packet in all_packets if packet.served]\n    dropped_packets = [packet for packet in all_packets if packet.dropped]\n\n    #\n    # 3) Build scalar statistics.\n    #\n    ret.loss_prob = len(dropped_packets) / len(all_packets)\n\n    #\n    # 4) Build various intervals statistics: departures, waiting times,\n    #    response times.\n    #\n    departure_intervals = _get_departure_intervals(served_packets)\n    ret.departures = build_statistics(np.asarray(departure_intervals))\n    ret.response_time = build_statistics([\n        pkt.departed_at - pkt.created_at for pkt in served_packets\n    ])\n    ret.wait_time = build_statistics([\n        pkt.service_started_at - pkt.created_at for pkt in served_packets\n    ])\n\n    return ret\n","repo_name":"ipu69/pyqumo_old","sub_path":"pyqumo/sim/gg1.py","file_name":"gg1.py","file_ext":"py","file_size_in_byte":13565,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"5641921958","text":"import pygame\n \nclass Rocket:\n    \"\"\"A class to manage the rocket.\"\"\"\n \n    def __init__(self, r_game):\n        \"\"\"Initialize the rocket and set its starting position.\"\"\"\n        self.screen = r_game.screen\n        self.settings = r_game.settings\n        self.screen_rect = r_game.screen.get_rect()\n\n        # Load the rocket image and get its rect.\n        #   Rocket image from: https://opengameart.org/content/rocket\n        #   License: https://creativecommons.org/licenses/by/3.0/\n        #   Image was resized.\n        self.image = pygame.image.load('images/rocket_small.png')\n        self.rect = self.image.get_rect()\n\n        # Start each new rocket at the center of the screen.\n        self.rect.center = self.screen_rect.center\n\n        # Store a decimal value for the rocket's horizontal\n        #   and vertical positions.\n        self.x = float(self.rect.x)\n        self.y = float(self.rect.y)\n\n        # Movement flags.\n        self.moving_right, self.moving_left = False, False\n        self.moving_up, self.moving_down = False, False\n\n    def update(self):\n        \"\"\"Update the rocket's position based on movement flags.\"\"\"\n        if self.moving_right and self.rect.right < self.screen_rect.right:\n            self.x += self.settings.rocket_speed\n        if self.moving_left and self.rect.left > 0:\n            self.x -= self.settings.rocket_speed\n        if self.moving_up and self.rect.top > 0:\n            self.y -= self.settings.rocket_speed\n        if self.moving_down and self.rect.bottom <= self.screen_rect.bottom:\n            self.y += self.settings.rocket_speed\n\n        # Update rect object from position attributes.\n        self.rect.x = self.x\n        self.rect.y = self.y\n\n    def blitme(self):\n        \"\"\"Draw the rocket at its current location.\"\"\"\n        self.screen.blit(self.image, self.rect)","repo_name":"ehmatthes/pcc_3e","sub_path":"solution_files/chapter_12/ex_12_4_rocket/rocket.py","file_name":"rocket.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":571,"dataset":"github-code","pt":"77"}
+{"seq_id":"9253631675","text":"import io\n\nimport pytest\nfrom exif import Image as ExifImage\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom rest_framework.exceptions import ValidationError as DRFValidationError\nimport boto3\n\nfrom veems.media import models\nfrom veems.moderation import services as moderation_services\n\npytestmark = pytest.mark.django_db\n\n\n@pytest.fixture\ndef video_rendition(video):\n    return models.VideoRendition.objects.create(\n        video=video,\n        profile='mpeg4_360p',\n        ext='webm',\n        file_size=1,\n    )\n\n\ndef test_transcode_job(transcode_job):\n    assert str(transcode_job) == (\n        f'<TranscodeJob {transcode_job.id} mpeg4_360p '\n        f'created video={transcode_job.video_id}>'\n    )\n\n\ndef test_upload_file_upload_to(upload):\n    result = models._upload_file_upload_to(\n        instance=upload, filename='blah.mp4'\n    )\n\n    assert result == f'uploads/{upload.id}.mp4'\n\n\ndef test_video_custom_thumbnail_image(video_factory, uploaded_img_with_exif):\n    img_file_obj, _ = uploaded_img_with_exif\n\n    video = video_factory(custom_thumbnail_image=img_file_obj)\n\n    assert video.custom_thumbnail_image\n    # Check all exif data was removed\n    exif_image = ExifImage(video.custom_thumbnail_image)\n    assert exif_image.list_all() == []\n\n\ndef test_video_rendition_thumbnail_upload_to(\n    video_rendition, simple_uploaded_img_file\n):\n    video_rendition_thumbnail = models.VideoRenditionThumbnail.objects.create(\n        video_rendition=video_rendition,\n        time_offset_secs=1,\n        height=10,\n        width=10,\n        ext='.jpg',\n        file=simple_uploaded_img_file,\n    )\n\n    result = models._video_rendition_thumbnail_upload_to(\n        instance=video_rendition_thumbnail, filename='something.jpg'\n    )\n\n    assert result == (\n        f'videos/{video_rendition_thumbnail.video_rendition.video_id}/'\n        f'renditions/{video_rendition_thumbnail.video_rendition.id}/'\n        f'thumbnails/{video_rendition_thumbnail.id}.jpg'\n    )\n\n\nclass TestUpload:\n    def test_set_file_using_uploaded_file(self, channel):\n        upload = models.Upload.objects.create(\n            presigned_upload_urls=[\n                'https://s3api.localhost/?part_num=1',\n                'https://s3api.localhost/?part_num=2',\n                'https://s3api.localhost/?part_num=3',\n            ],\n            media_type='video',\n            channel=channel,\n        )\n        file_ = SimpleUploadedFile(\n            'video.mp4',\n            b'data',\n        )\n\n        upload.file = file_\n        upload.save()\n        upload.refresh_from_db()\n\n        assert upload.file.name == f'uploads/{upload.id}.mp4'\n        assert upload.file.url.startswith('http')\n        assert 'AccessKeyId' in upload.file.url\n\n    def test_file_uploaded_outside_the_applocation(self, settings, channel):\n        # This tests the flow where the file is uploaded to the storage\n        # bucket completely outside of the application itself\n        # (on the client side using pre-signed-url upload process\n        upload = models.Upload.objects.create(\n            presigned_upload_urls=[\n                'https://s3api.localhost/?part_num=1',\n                'https://s3api.localhost/?part_num=2',\n                'https://s3api.localhost/?part_num=3',\n            ],\n            media_type='video',\n            channel=channel,\n        )\n\n        uploaded_filename = f'{upload.id}/video.mp4'\n\n        # Upload the file completely outside of Django\n        s3 = boto3.client('s3', endpoint_url=settings.AWS_S3_ENDPOINT_URL)\n        s3.upload_fileobj(\n            io.BytesIO(b'data'),\n            models.STORAGE_BACKEND.bucket_name,\n            uploaded_filename,\n        )\n\n        # Set the Django file field to point to that file path\n        upload.file.name = uploaded_filename\n        upload.save()\n\n        # Check file can be accessed as if it was uploaded within Django\n        upload.refresh_from_db()\n        assert upload.file.name == uploaded_filename\n        assert upload.file.url.startswith('http')\n        assert 'AccessKeyId' in upload.file.url, upload.file.url\n        assert upload.id in upload.file.url\n\n\nclass TestVideo:\n    def test_validates_number_of_tags(\n        self,\n        video_factory,\n        channel,\n    ):\n        with pytest.raises(DRFValidationError):\n            video_factory(\n                channel=channel,\n                # Max is 5\n                tags=['1', '2', '3', '4', '5', '6'],\n            )\n\n    def test_is_removed_returns_true_if_was_removed(\n        self, channel, video_factory\n    ):\n        video = video_factory(channel=channel)\n        moderation_services.mark_video_as_removed(\n            video_id=video.id,\n            reason='doxxing',\n        )\n\n        assert video.is_removed is True\n\n    def test_is_removed_returns_false_if_was_never_removed(\n        self, channel, video_factory\n    ):\n        video = video_factory(channel=channel)\n\n        assert video.is_removed is False\n","repo_name":"VeemsHQ/veems","sub_path":"tests/media/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":4951,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"77"}
+{"seq_id":"21501075201","text":"from arg_help_formatter import *\nfrom srcnn_utils import get_pad_count\n\ndef convert_positive_int(value):\n    try:\n        value = int(value)\n        assert (value > 0)\n    except Exception:\n        raise argparse.ArgumentTypeError(\"Positive integer is expected but got: {}\".format(value))\n    return value\n\ndef convert_positive_float(value):\n    try:\n        value = float(value)\n        assert (value > 0)\n    except Exception:\n        raise argparse.ArgumentTypeError(\"Positive float is expected but got: {}\".format(value))\n    return value\n\ndef group_arg_list_size_check(args):\n\n    len_layer = args.convlayers\n    len_ks = len(args.kernelsizes)\n    len_kc = len(args.kernelcounts)\n    len_rp = len(args.relupositions)\n    len_lr = len(args.learnrates)\n    \n    try:\n        assert len_layer == len_ks, \"Layer count ({}) does not match with number of elements in the kernel sizes list ({})\".format(len_layer, len_ks)\n        assert len_layer == len_kc, \"Layer count ({}) does not match with number of elements in the kernel counts list ({})\".format(len_layer, len_kc)\n        assert len_layer >= len_rp, \"Layer count ({}) is smaller than the number of relu positions given ({})\".format(len_layer, len_rp)\n        assert len_layer == len_lr, \"Layer count ({}) does not match with learning rates ({})\".format(len_layer, len_lr)\n\n        # Checking each kernel size for padding\n        for ks in args.kernelsizes:\n            padding = get_pad_count(ks)\n            assert padding.is_integer(), \"Invalid kernel size ({}) for proper calculation of padding ({})\".format(ks, padding)\n\n        # Sort ReLU Positions\n        relu_positions = args.relupositions = sorted(args.relupositions)\n\n        # Detect duplicate ReLU layer positions if any\n        for i in range(len(relu_positions)-1):\n            assert relu_positions[i] not in relu_positions[i+1:], \"Duplicate positions given for ReLU layers\"\n\n        # Adjusting ReLU Layer positions according to Convolutional Layers\n        # Throws error if invalid ReLU position given \n        for i in range(len_rp):\n            assert relu_positions[i] != len_layer, \"ReLU layer ({}) cannot be the last layer of the neural network\".format(relu_positions[i])\n            assert relu_positions[i] < len_layer, \"Invalid position for ReLU layer ({})\".format(relu_positions[i])\n            relu_positions[i] += i\n\n    except AssertionError as e:\n        print(e)\n        exit(1)\n\n# Handles cmd args\ndef arg_handler():\n    parser = argparse.ArgumentParser(description='Image Super Resolution with PyTorch: [Train]', \n                                     formatter_class=ArgHelpFormatter, \n                                     add_help=False)\n    # Optional flags\n    parser.add_argument(\"-h\", \"--help\", help=\"Help message\", action=\"store_true\")\n    parser.add_argument(\"-ng\", \"--nogpu\", help=\"Use GPU\", default=False, action=\"store_true\")\n\n    # Required flags\n    enable_exec = (\"-h\" not in sys.argv)\n    group = parser.add_argument_group(title='required arguments')\n\n    group.add_argument(\"-sf\", \"--scalefactor\",\n                       help=\"Specify scale factor for upsampling (default: 2)\",\n                       type=convert_positive_int,\n                       metavar=\"FACTOR\",\n                       default=2)\n    \n    group.add_argument(\"-bs\", \"--batchsize\",  \n                       help=\"Specify batch size (default: 1)\", \n                       type=convert_positive_int, \n                       metavar=\"BATCH\",\n                       default=1)\n    \n    group.add_argument(\"-cl\", \"--convlayers\",  \n                       help=\"Specify number of convolutional layers (default: 3)\", \n                       type=convert_positive_int, \n                       metavar=\"CONV\", \n                       default=3)\n    \n    group.add_argument(\"-ks\", \"--kernelsizes\", \n                       nargs='+',\n                       type=convert_positive_int,\n                       help=\"Specify kernel sizes for each convolutional layer (default: 9 1 5...)\",\n                       metavar=\"KSIZES\", \n                       default=[9, 1, 5])\n    \n    group.add_argument(\"-kc\", \"--kernelcounts\", \n                       nargs='+',\n                       type=convert_positive_int,\n                       help=\"Specify number of kernels for each convolutional layer (default: 64 32 1...)\", \n                       metavar=\"KCOUNTS\", \n                       default=[64, 32, 1])\n    \n    group.add_argument(\"-rp\", \"--relupositions\", \n                       nargs='+',\n                       type=convert_positive_int,  \n                       help=\"Specify after which convolutional layer ReLU takes place (default: 1 2 3...)\", \n                       metavar=\"POS\", \n                       default=[1, 2])\n    \n    group.add_argument(\"-lr\", \"--learnrates\", \n                       nargs='+',\n                       type=convert_positive_float,\n                       help=\"Specify learning rate (default: 0.0001 0.0001 0.00001...)\", \n                       metavar=\"LR\",\n                       default=[1e-04, 1e-04, 1e-05])\n\n    group.add_argument(\"-of\", \"--outputfolder\", \n                       type=str,\n                       help=\"Specify output folder name (default: '')\", \n                       metavar=\"FOLDER\",\n                       default=\"\")\n\n    group.add_argument(\"-c\", \"--checkpoint\", \n                       type=str,\n                       help=\"Specify checkpoint path (default: '')\", \n                       metavar=\"PATH\",\n                       default=\"\")\n\n    args = parser.parse_args()\n\n    # Print help if -h is used\n    if args.help:\n        parser.print_help()\n        return\n\n    group_arg_list_size_check(args)\n    return args","repo_name":"shdw2x/srcnn","sub_path":"arg_helper_train.py","file_name":"arg_helper_train.py","file_ext":"py","file_size_in_byte":5699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8085808482","text":"from models import cifar10vgg as cifar10\nfrom models import cifar100vgg as cifar100\n\nimport numpy as np\nfrom utils import uncertainty_tools as utils\n\nMODELS ={\"cifar100\": cifar100.Cifar100Vgg, \"cifar10\": cifar10.Cifar10Vgg}\n\ndef aes_sr(model_name, model_cls,k=10, train=True, first_epoch=100, last_epoch=249):\n\n    nets = list(np.linspace(first_epoch, last_epoch, k))\n    nets = [int(round(a)) for a in nets]\n\n\n    model = model_cls(train=train, filename=model_name)\n    results = []\n    for net in nets:\n        results.append(model.predict_val_checkpoint(checkpoint=net))\n    baseline_pred = results[-1]\n\n    aes_pred = np.mean(np.array(results),0)\n\n    f_pred = np.argmax(baseline_pred, 1)\n    confidence = aes_pred[np.arange(aes_pred.shape[0]), f_pred]\n\n    errors = np.argmax(baseline_pred, 1) != np.argmax(model.y_test, 1)\n\n    curve,aurc,eaurc = utils.RC_curve(errors,confidence)\n    baseline_curve, baseline_aurc, baseline_eaurc = utils.RC_curve(errors,np.max(baseline_pred,1))\n    return eaurc, baseline_eaurc\n\n\ndef aes_MC(model_name, model_cls, k=10, train=False, first_epoch=100, last_epoch=249, mc_iterations=100):\n    nets = list(np.linspace(first_epoch, last_epoch, k))\n    nets = [int(round(a)) for a in nets]\n    model = model_cls(train=train, filename=model_name)\n    mc_uncertainty = []\n    baseline_pred = (model.predict_val_checkpoint(checkpoint=nets[-1]))\n    f_pred = np.argmax(baseline_pred, 1)\n\n    for net in nets:\n        mc_uncertainty.append(utils.MC_dropout(model, checkpoint=net, mc_iterations=mc_iterations))\n\n\n    aes_pred = np.mean(np.array(mc_uncertainty), 0)\n\n    confidence = 1/aes_pred[np.arange(aes_pred.shape[0]), f_pred]\n\n    errors = f_pred != np.argmax(model.y_test, 1)\n\n\n    curve,aurc,eaurc = utils.RC_curve(errors,confidence)\n\n    confidence_baseline = 1/mc_uncertainty[-1][np.arange(baseline_pred.shape[0]), f_pred]\n    baseline_curve, baseline_aurc, baseline_eaurc = utils.RC_curve(errors,confidence_baseline)\n    return eaurc, baseline_eaurc\n\n\n\ndef aes_NN_dist(model_name, model_cls, k=10, train=False, first_epoch=100, last_epoch=249):\n    nets = list(np.linspace(first_epoch, last_epoch, k))\n    nets = [int(round(a)) for a in nets]\n    model = model_cls(train=train, filename=model_name)\n    NN_uncertainty = []\n    baseline_pred = (model.predict_val_checkpoint(checkpoint=nets[-1]))\n    f_pred = np.argmax(baseline_pred, 1)\n\n    for net in nets:\n        activations_train, activation_test = model.predict_act_checkpoints(checkpoint=net)\n        uncertainty = utils.nn_uncertainty(activations_train,activation_test, model.y_train, f_pred, num_classes=30)\n        NN_uncertainty.append(uncertainty)\n\n\n    confidence = np.mean(np.array(NN_uncertainty), 0)\n\n    errors = f_pred != np.argmax(model.y_test, 1)\n\n\n    curve,aurc,eaurc = utils.RC_curve(errors,confidence)\n    baseline_curve, baseline_aurc, baseline_eaurc = utils.RC_curve(errors,NN_uncertainty[-1])\n    return eaurc, baseline_eaurc\n\n\nif __name__ == '__main__':\n    results = {}\n    # Results for Cifar10 :\n    #this line is only for training the model on first run\n    aes_sr(\"uncertainty_cifar10\", MODELS[\"cifar10\"], k=1, train=True)\n\n    for k in [10]:\n        if k not in results.keys():\n            results[k] = {}\n        results[k][\"SR\"] = aes_sr(\"uncertainty_cifar10\", MODELS[\"cifar10\"], k=k, train=False)\n        results[k][\"MC\"] = aes_MC(\"uncertainty_cifar10\", MODELS[\"cifar10\"], k=k, train=False, mc_iterations=100)\n        print(results)\n        results[k][\"NN-dist\"] = aes_NN_dist(\"uncertainty_cifar10\", MODELS[\"cifar10\"], k=k, train=False)\n\n    print(results)\n\n    # this line is only for training the model on first run\n    aes_sr(\"uncertainty_cifar100__0\", MODELS[\"cifar100\"], k=1, train=True)\n\n    results = {}\n    # Results for Cifar100 :\n    for k in [10]:\n        if k not in results.keys():\n            results[k]={}\n        results[k][\"SR\"] = aes_sr(\"uncertainty_cifar100__0\", MODELS[\"cifar100\"],k=k, train=False)\n        results[k][\"MC\"] = aes_MC(\"uncertainty_cifar100__0\", MODELS[\"cifar100\"],k=k, train=False, mc_iterations=100)\n        print(results)\n        results[k][\"NN-dist\"] = aes_NN_dist(\"uncertainty_cifar100__0\", MODELS[\"cifar100\"],k=k, train=False)\n\n    print(results)\n","repo_name":"geifmany/uncertainty_ICLR","sub_path":"aes.py","file_name":"aes.py","file_ext":"py","file_size_in_byte":4213,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"1152123596","text":"import sys\nsys.stdin = open(\"input.txt\", 'r')\n\ninput = sys.stdin.readline\n\nT = int(input())\nfor _ in range(T):\n    N = int(input())\n    p = [0] * N\n    for i in range(N):\n        p[i] = int(input())\n    t = sum(p)\n    p = sorted(enumerate(p), key=lambda x:x[1], reverse=True)\n    #print(p)\n    if p[0][1] == p[1][1]:\n        print('no winner')\n        continue\n    if p[0][1] > t//2:\n        print(f'majority winner {p[0][0]+1}')\n    else:\n        print(f'minority winner {p[0][0]+1}')","repo_name":"hjyoon/baekjoon-answers","sub_path":"_11000/11637.py","file_name":"11637.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1235244660","text":"import os, sys\nfrom wit import Wit\nfrom gnewsclient import gnewsclient\n\naccess_token = os.environ[\"WIT_ACCESS_TOKEN\"]\n \nclient = Wit(access_token = access_token)\n\n# available news categories\nnews_categories = [('sports', 'sports news'), ('political', 'political news'), ('business', 'business news'), \n\t\t\t\t   ('top stories', 'top stories news'), ('world', 'world news'), ('national', 'national news'), \n\t\t\t\t\t('technology', 'technology news'), ('entertainment', 'entertainment news')]\n\n# a help message\nHELP_MSG = \"\"\"\nHey! I am NewsBot. \nI can provide you news from all around the world in different languages, on different topics! \nTry any of these categories. :)\n\"\"\"\n\n\ndef wit_response(message_text):\n    resp = client.message(message_text)\n    # entity = None\n    # value = None\n    # try:\n    #     entity = list(resp['entities'])[0]\n    #     value = resp['entities'][entity][0]['value']\n    # except:\n    #     pass\n    # return (entity,value)\n    categories = {'newstype':None, 'location':None}\n    \n    entities = list(resp['entities'])\n    for entity in entities:\n        categories[entity] = resp['entities'][entity][0]['value']\n    \n    return categories\n\ndef get_news_elements(categories):\n    news_client = gnewsclient()\n    news_client.query = ''\n\n    if categories['newstype'] != None:\n        news_client.query += categories['newstype'] + ' '\n\n    if categories['location'] != None:\n        news_client.query += categories['location'] \n\n    news_items = news_client.get_news()\n\n    elements = []\n\n    for item in news_items:\n        element = {\n                    'title' : item['title'],\n                    'buttons' : [{\n                                'type': 'web_url',\n                                'title': \"Read more\",\n                                'url': item['link']\n                    }],\n                    'image_url': item['img']\n        }\n        elements.append(element)\n\n    return elements   \n#print(get_news_elements(wit_response('I want sport news from india')))","repo_name":"anuschka/fb-messenger-bot-python","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"21682642059","text":"import os\nimport eol_scons\nimport platform\n\nenv = Environment(tools=['default','nidas'])\n\np1 = env.Program('ncprint','ncprint.cc',\n    LIBS=[\"netcdf_c++\",\"netcdf\",\"hdf5\",\"hdf5_hl\",\"nidas_util\"],\n    CXXFLAGS=['-g','-Wall'])\n#     CXXFLAGS=['-g','-pg','-Wall'],LINKFLAGS=\"-pg\")\n\n\nenv['ISFF'] = os.environ['ISFF']\n\nif platform.machine() == \"x86_64\":\n    env['BINDIR'] = \"bin64\"\nelse:\n    env['BINDIR'] = \"bin\"\n\nenv.Install('$ISFF/$BINDIR',p1)\n\nenv.Alias('install', [ '$ISFF/$BINDIR' ])\n","repo_name":"ncareol/isfs","sub_path":"src/nc_utils/SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43693502571","text":"from ftw.builder import Builder\nfrom ftw.builder import create\nfrom ftw.workspace.testing import FTW_WORKSPACE_FUNCTIONAL_TESTING\nfrom plone.app.testing import login\nfrom plone.app.testing import logout\nfrom plone.app.testing import setRoles\nfrom plone.app.testing import TEST_USER_ID\nfrom plone.testing.z2 import Browser\nfrom Products.CMFCore.utils import getToolByName\nfrom unittest2 import TestCase\nfrom zExceptions import Unauthorized\nimport transaction\n\n\nclass TestWorkspaceWorkflow(TestCase):\n\n    layer = FTW_WORKSPACE_FUNCTIONAL_TESTING\n\n    def setUp(self):\n        self.browser = Browser(self.layer['app'])\n        self.portal = self.layer['portal']\n        setRoles(self.portal, TEST_USER_ID, ['Manager'])\n\n        self.portal.portal_types['Folder'].allowed_content_types = (\n            'Workspace', )\n\n        self.folder = self._setup_projects_folder_with_placeful_workflow()\n\n    def _setup_projects_folder_with_placeful_workflow(self):\n        folder = create(Builder('folder'))\n\n        policy_id = 'workspace_policy'\n\n        folder.manage_addProduct['CMFPlacefulWorkflow']\\\n            .manage_addWorkflowPolicyConfig()\n        config = getToolByName(\n            folder,\n            'portal_placeful_workflow').getWorkflowPolicyConfig(folder)\n\n        config.setPolicyIn(policy=policy_id)\n        config.setPolicyBelow(policy=policy_id, update_security=True)\n        transaction.commit()\n\n        return folder\n\n    def test_anonymous_user_cannot_access_folder(self):\n        logout()\n\n        with self.assertRaises(Unauthorized):\n            self.portal.restrictedTraverse(self.folder.getId())\n\n    def test_authenticated_user_can_view_folder(self):\n        user = create(Builder('user'))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.assertTrue(\n            self.portal.restrictedTraverse(self.folder.getId()),\n            'A authenticated user should have access to the folder')\n\n    def test_authenticated_user_CANNOT_view_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Authenticated'))\n\n        logout()\n        login(self.portal, user.getId())\n\n        with self.assertRaises(Unauthorized):\n            self.folder.restrictedTraverse(workspace.getId())\n\n    def test_reader_CAN_view_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Reader', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.assertTrue(\n            self.folder.restrictedTraverse(workspace.getId()),\n            'A reader should have access to the workspace')\n\n    def test_contributor_CAN_add_content_in_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Contributor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.assertTrue(\n            create(Builder('TabbedViewFolder').within(workspace)),\n            'Contributor should be able to add a tabbedviewfolder')\n\n        self.assertTrue(\n            create(Builder('file').within(workspace)),\n            'Contributor should be able to add a tabbedviewfolder')\n\n    def test_contributor_CAN_delete_his_content_in_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Contributor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        item = create(Builder('TabbedViewFolder').within(workspace))\n        id_ = item.getId()\n\n        workspace.manage_delObjects([id_])\n\n        self.assertNotIn(id_, workspace.objectIds())\n\n    def test_contributor_CANNOT_delete_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Contributor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        with self.assertRaises(Unauthorized):\n            self.folder.manage_delObjects([workspace.getId()])\n\n    def test_admin_CAN_manage_permissions(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user')\n                      .with_roles('Editor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.assertTrue(workspace.restrictedTraverse('@@sharing'),\n                        'Admin shold habe access to the sharing view')\n\n    def test_others_CANNOT_manage_permissions(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Contributor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        with self.assertRaises(Unauthorized):\n            workspace.restrictedTraverse('@@sharing')\n\n    def test_admin_CAN_delete_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        workspace_id = workspace.getId()\n        user = create(Builder('user')\n                      .with_roles('Editor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.folder.manage_delObjects([workspace.getId()])\n        self.assertNotIn(workspace_id, self.folder.objectIds())\n\n    def test_site_admin_CAN_delete_his_workspace(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        workspace_id = workspace.getId()\n        user = create(Builder('user')\n                      .with_roles('Editor'))\n\n        logout()\n        login(self.portal, user.getId())\n\n        self.folder.manage_delObjects([workspace_id])\n\n        self.assertNotIn(workspace_id, self.folder.objectIds())\n\n    def test_reader_CANNOT_edit_content(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Reader', on=workspace))\n        item = create(Builder('TabbedViewFolder').within(workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        with self.assertRaises(Unauthorized):\n            item.restrictedTraverse('edit')\n\n    def test_contributor_CAN_edit_his_content(self):\n        workspace = create(Builder('workspace').within(self.folder))\n        user = create(Builder('user').with_roles('Contributor', on=workspace))\n\n        logout()\n        login(self.portal, user.getId())\n\n        item = create(Builder('TabbedViewFolder').within(workspace))\n        self.assertTrue(item.restrictedTraverse('edit'),\n                        'Contributor should be able to edit his content')\n","repo_name":"4teamwork/ftw.workspace","sub_path":"ftw/workspace/tests/test_workspace_example_workflow.py","file_name":"test_workspace_example_workflow.py","file_ext":"py","file_size_in_byte":6633,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"31732818377","text":"\n# Priority Queue = data structure that keeps data stored by it's weight/importance.\n# Binary Heap will allow us both enqueue and dequeue items in O(log n)\n# which is great for implementing a Priority Queue.\n# We will want to insert according to priority and remove according to priority.\n# \nclass PriorityQueue:\n\n    def __init__(self):\n        # index 0 is not used to allow integer division, so index 1 is the start\n        self.heap_list = [{'key': None, 'value': None}]  \n        self.size = 0\n\n    def insert(self, key, value):\n        # append element at the end of the list, increment list size,\n        # and percolate the element up to its correct position\n        self.heap_list.append({'key': key, 'value': value})\n        self.size += 1\n        self.percolate_up(self.size)\n\n    def percolate_up(self, i):\n        while i // 2 > 0:\n            # if less than parent, swap values\n            if self.heap_list[i]['key'] > self.heap_list[i // 2]['key']:\n                tmp = self.heap_list[i // 2]\n                self.heap_list[i // 2] = self.heap_list[i]\n                self.heap_list[i] = tmp\n            # move up to parent position\n            i //= 2\n\n    def delete_max(self):\n        # set the last to be first deleting the first\n        # delete last and decrement the size\n        # percolutate the root element down to its proper place\n        min_val = self.heap_list[1]\n        self.heap_list[1] = self.heap_list[self.size]\n        self.heap_list.pop()\n        self.size -= 1\n        self.percolate_down(1)  # index 1 is the start\n        return min_val\n\n    def percolate_down(self, i):\n        while (i * 2) <= self.size:\n            min_child_index = self.min_child(i)\n            # if parent is greater than the min child, percolate down\n            if self.heap_list[i]['key'] < self.heap_list[min_child_index]['key']:\n                tmp = self.heap_list[i]\n                self.heap_list[i] = self.heap_list[min_child_index]\n                self.heap_list[min_child_index] = tmp\n            i = min_child_index\n\n    def min_child(self, i):\n        # if the right child index > size of heap_list, return left child index\n        if i * 2 + 1 > self.size:\n            return i * 2\n        else:\n            # if left child < right child, return left child index\n            if self.heap_list[i * 2]['key'] > self.heap_list[i * 2 + 1]['key']:\n                return i * 2\n            else:\n                # return right child index\n                return i * 2 + 1\n\n\nif __name__ == '__main__':\n\n    pq = PriorityQueue()\n    objects = [{'key':5, 'value':'five'}, {'key':9, 'value':'nine'}, {'key':11, 'value':'eleven'}, \\\n            {'key': 14, 'value': 'fourteen'}, {'key': 18, 'value': 'eighteen'}, {'key': 19, 'value': 'nineteen'}, \\\n            {'key': 21, 'value': 'twenty'}, {'key': 33, 'value': 'thrity three'}, {'key': 17, 'value': 'seventeen'}, \\\n            {'key':27, 'value':'twenty seven'}]\n\n    for obj in objects:\n        pq.insert(obj['key'], obj['value'])\n\n    i = 0\n    keys = []\n    for obj in pq.heap_list:\n        if i > 0:\n            keys.append(obj['key'])\n        i += 1\n\n    # check keys/weights are in same order\n    # visual check https://visualgo.net/en/heap\n    assert keys == [33,27,19,17,21,9,18,5,14,11]\n\n    max_obj = pq.delete_max()\n    print(max_obj)\n\n    i = 0\n    keys = []\n    for obj in pq.heap_list:\n        if i > 0:\n            keys.append(obj['key'])\n        i += 1\n\n    assert keys == [27,21,19,17,11,9,18,5,14]","repo_name":"T-travis/data_structures_and_algorithms","sub_path":"sorting/binary_heap_and_priority_queue/custon_priority_queue.py","file_name":"custon_priority_queue.py","file_ext":"py","file_size_in_byte":3487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7816561048","text":"import socket\n\nip='127.0.0.1'\nport=5000\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server:\n    server.bind((ip, port))\n    server.listen()\n    connection, address = server.accept()\n\n    with connection:\n        print('Connected :: ' , address)\n        msg = ''\n        while True:\n            data = connection.recv(1024)\n\n            if data:\n                msg += data.decode('utf-8')\n                \n                if data.decode('utf-8') == '\\r\\n' or data.decode('utf-8').endswith('\\r\\n'):\n                    print(msg)\n                    connection.send(bytes('msg :: ' + msg, 'utf-8'))\n\n                    if msg == 'quit\\r\\n':\n                        connection.shutdown(1)\n                        connection.close()\n                        break\n                    msg = ''\n\n            \n\n","repo_name":"juanjoaquino05/python-echo-server","sub_path":"echo_server.py","file_name":"echo_server.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17462271587","text":"import os\nfrom PIL import Image\nfrom torchvision.transforms import Compose, RandomRotation, RandomHorizontalFlip, ColorJitter\nfrom multiprocessing import Pool, cpu_count\nfrom shutil import copy2\nfrom tqdm import tqdm\n\n# Define the augmentation transformations\naugmentations = Compose([\n    RandomRotation(30),\n    RandomHorizontalFlip(),\n    ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n])\n\n# Worker function for multiprocessing\ndef augment_worker(args):\n    image_subset, augmented_dir = args\n    current_class = image_subset[0][1]  # Get the class of the first image in the subset\n    for img_path, label_name in image_subset:\n        # Load the image\n        image = Image.open(img_path).convert('RGB')\n        \n        # Copy the original image to the Augmented_Images directory\n        original_img_path = os.path.join(augmented_dir, label_name, os.path.basename(img_path))\n        if not os.path.exists(os.path.dirname(original_img_path)):\n            os.makedirs(os.path.dirname(original_img_path))\n        copy2(img_path, original_img_path)  # <-- Copy the original image\n        \n        # Apply augmentations\n        augmented_image = augmentations(image)\n        \n        # Save the augmented image with a prefix or suffix to differentiate from the original\n        augmented_img_path = os.path.join(augmented_dir, label_name, \"aug_\" + os.path.basename(img_path))\n        augmented_image.save(augmented_img_path)\n\n    return current_class\n\ndef augment_dataset(root_dir):\n    classes = os.listdir(root_dir)\n    image_paths = []\n\n    for cls in classes:\n        for image_name in os.listdir(os.path.join(root_dir, cls)):\n            image_paths.append((os.path.join(root_dir, cls, image_name), cls))\n\n    # Directory to save augmented images\n    global augmented_dir\n    augmented_dir = 'Augmented_Images'\n    if not os.path.exists(augmented_dir):\n        os.makedirs(augmented_dir)\n\n    # Split the images into subsets for multiprocessing\n    num_processes = cpu_count()\n    chunk_size = len(image_paths) // num_processes\n    image_subsets = [image_paths[i:i + chunk_size] for i in range(0, len(image_paths), chunk_size)]\n\n    # Use a Pool of workers to parallelize the augmentation\n    args_for_workers = [(subset, augmented_dir) for subset in image_subsets]\n    with Pool(num_processes) as pool:\n        with tqdm(total=num_processes, desc=\"Processing subsets\") as pbar:\n            for current_class in pool.imap_unordered(augment_worker, args_for_workers):\n                pbar.set_description(f\"Processed class: {current_class}\")  # Update the description\n                pbar.update(1)\n\n\nif __name__ == \"__main__\":\n    augment_dataset('Images')\n","repo_name":"letalboy/MultiLabelCNNClassifier","sub_path":"Data/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18824856113","text":"# ! change LOCAL to False before submitting !\n# set LOCAL to True for local testing\n\nLOCAL = True\n\nif LOCAL:\n    class Node:\n        def __init__(self, value, next_item=None):\n            self.value = value\n            self.next_item = next_item\n\n        def __repr__(self):\n            return f'{self.value}'\n\n\ndef solution(node, idx):\n    \"\"\"Удаляет из связного списка 1 элемент по индексу idx.\"\"\"\n\n    if idx == 0:\n        return node.next_item\n\n    i = 1\n    temp = node\n\n    while idx > i:\n        temp = temp.next_item\n        i += 1\n\n    temp.next_item = temp.next_item.next_item\n    return node\n\n\n\ndef test():\n    node3 = Node(\"node3\", None)\n    node2 = Node(\"node2\", node3)\n    node1 = Node(\"node1\", node2)\n    node0 = Node(\"node0\", node1)\n    new_head = solution(node0, 1)\n    assert new_head is node0\n    assert new_head.next_item is node2\n    assert new_head.next_item.next_item is node3\n    assert new_head.next_item.next_item.next_item is None\n    # result is node0 -> node2 -> node3\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"LpilinAlexandr/algorithms","sub_path":"sprint 2/contest/Нелюбимое дело.py","file_name":"Нелюбимое дело.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3568278968","text":"from metric_collector.metric_collector import MetricCollector\nfrom flask import Flask\nfrom prometheus_client import core, exposition\nimport pygogo as gogo\n\napplication = Flask(__name__)\n# logging setup\nkwargs = {}\nformatter = gogo.formatters.structured_formatter\nlogger = gogo.Gogo('struct', low_formatter=formatter).get_logger(**kwargs)\n\n\n@application.route('/')\ndef hello():\n    return \"Welcome to the Handbrake Job Exporter.  The metrics can be found on /metrics\"\n\n\n@application.route('/metrics')\ndef metrics():\n    registry = core.CollectorRegistry(auto_describe=False)\n    registry.register(MetricCollector(logger=logger))\n    return exposition.generate_latest(registry)\n\n\nif __name__ == '__main__':\n    application.run(host=\"0.0.0.0\", port=8080)\n","repo_name":"chrisjohnson00/handbrake-job-metrics","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"25073013266","text":"import multiprocessing\n\ndef comer(fruta):\n    print(f\"Comi {fruta}.\")\n\nif __name__ == \"__main__\":\n    frutas = [ f\"(id {a}) {b}\" for a, b in zip(range(20),\n        [\n        \"Maçã\", \"Banana\", \"Laranja\", \"Morango\", \"Uva\",\n        \"Pera\", \"Abacaxi\", \"Manga\", \"Melancia\", \"Melão\",\n        \"Kiwi\", \"Cereja\", \"Limão\", \"Coco\", \"Framboesa\",\n        \"Amora\", \"Pêssego\", \"Ameixa\", \"Figo\", \"Romã\"\n    ])]\n\n    with multiprocessing.Pool(3) as p:\n        p.map(comer, frutas)\n\n    print(\"Fim\")\n","repo_name":"lavratti/python-thread-and-multipleprocessing-exemples","sub_path":"exemple_multiprocessing_4.py","file_name":"exemple_multiprocessing_4.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"is","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42146340935","text":"def convert_text(inner_text: str):\n    \"\"\"\n    Транслитерирует текст из русских символов в текст из английских символов\n    :param inner_text: str\n    :return: transliterated_text\n    \"\"\"\n\n    lower_case_letters = {\n        \"а\": \"a\",\n        \"б\": \"b\",\n        \"в\": \"v\",\n        \"г\": \"g\",\n        \"д\": \"d\",\n        \"е\": \"e\",\n        \"ё\": \"e\",\n        \"ж\": \"zh\",\n        \"з\": \"z\",\n        \"и\": \"i\",\n        \"й\": \"y\",\n        \"к\": \"k\",\n        \"л\": \"l\",\n        \"м\": \"m\",\n        \"н\": \"n\",\n        \"о\": \"o\",\n        \"п\": \"p\",\n        \"р\": \"r\",\n        \"с\": \"s\",\n        \"т\": \"t\",\n        \"у\": \"u\",\n        \"ф\": \"f\",\n        \"х\": \"h\",\n        \"ц\": \"ts\",\n        \"ч\": \"ch\",\n        \"ш\": \"sh\",\n        \"щ\": \"sch\",\n        \"ъ\": \"\",\n        \"ы\": \"y\",\n        \"ь\": \"\",\n        \"э\": \"e\",\n        \"ю\": \"yu\",\n        \"я\": \"ya\",\n        \" \": \"_\",\n    }\n\n    transliterated_text = \"\"\n    for index, char in enumerate(str(inner_text).lower()):\n        if char in lower_case_letters:\n            transliterated_text += lower_case_letters[char]\n        else:\n            transliterated_text += char\n\n    return transliterated_text\n\n\ndef image_path(instance, filename):\n    return f\"products/{convert_text(instance.name)}/{filename}\"\n","repo_name":"DonikHronic/StorageBot","sub_path":"bot_storage/utils/get_folder_path.py","file_name":"get_folder_path.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2571399940","text":"# -*- coding: utf-8 -*-\n\"\"\"Train the TabNet or reduced TabNet model on various datasets.\"\"\"\nimport os\nfrom absl import app\nimport numpy as np\nimport tensorflow.compat.v1 as tf\n\nfrom datetime import datetime\nfrom config.covertype import *\nfrom model import tabnet, tabnet_reduced\nfrom util import data_helper, logging\n\nlogger = logging.create_logger()\n\n# Run Tensorflow on GPU 0\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\n\ndef sort_col_names(feature_cols):\n    column_names = sorted(feature_cols)\n    logger.info(\"Ordered column names, corresponding to the indexing in Tensorboard visualization\")\n    for fi in range(len(column_names)):\n        logger.info(str(fi) + \" : \" + column_names[fi])\n\n\ndef main(unused_argv):\n    # column order\n    feature_columns = (\n        INT_COLUMNS + ENCODED_CATEGORICAL_COLUMNS + BOOL_COLUMNS + STR_COLUMNS + FLOAT_COLUMNS\n    )\n    all_columns = feature_columns + [LABEL_COLUMN]\n\n    # Fix random seeds\n    tf.set_random_seed(SEED)\n    np.random.seed(SEED)\n\n    input_columns = data_helper.get_columns(\n        INT_COLUMNS, ENCODED_CATEGORICAL_COLUMNS, BOOL_COLUMNS, FLOAT_COLUMNS, STR_COLUMNS\n    )\n\n    # Define the TabNet model\n    tabnet_model = (\n        (\n            tabnet_reduced.TabNetReduced(\n                columns=input_columns,\n                num_features=NUM_FEATURES,\n                feature_dim=FEATURE_DIM,\n                output_dim=OUTPUT_DIM,\n                num_decision_steps=NUM_DECISION_STEPS,\n                relaxation_factor=RELAXATION_FACTOR,\n                batch_momentum=BATCH_MOMENTUM,\n                virtual_batch_size=VIRTUAL_BATCH_SIZE,\n                num_classes=NUM_CLASSES,\n            )\n        )\n        if REDUCED\n        else (\n            tabnet.TabNet(\n                columns=input_columns,\n                num_features=NUM_FEATURES,\n                feature_dim=FEATURE_DIM,\n                output_dim=OUTPUT_DIM,\n                num_decision_steps=NUM_DECISION_STEPS,\n                relaxation_factor=RELAXATION_FACTOR,\n                batch_momentum=BATCH_MOMENTUM,\n                virtual_batch_size=VIRTUAL_BATCH_SIZE,\n                num_classes=NUM_CLASSES,\n            )\n        )\n    )\n\n    sort_col_names(feature_columns)\n\n    # Input sampling\n    train_batch = data_helper.input_fn(\n        TRAIN_FILE,\n        INT_COLUMNS,\n        BOOL_COLUMNS,\n        FLOAT_COLUMNS,\n        STR_COLUMNS,\n        LABEL_COLUMN,\n        num_epochs=MAX_STEPS,\n        shuffle=True,\n        batch_size=BATCH_SIZE,\n    )\n    test_batch = data_helper.input_fn(\n        TEST_FILE,\n        INT_COLUMNS,\n        BOOL_COLUMNS,\n        FLOAT_COLUMNS,\n        STR_COLUMNS,\n        LABEL_COLUMN,\n        num_epochs=MAX_STEPS,\n        shuffle=False,\n        batch_size=N_TEST_SAMPLES,\n    )\n\n    train_iter = train_batch.make_initializable_iterator()\n    test_iter = test_batch.make_initializable_iterator()\n\n    feature_train_batch, label_train_batch = train_iter.get_next()\n    feature_test_batch, label_test_batch = test_iter.get_next()\n\n    # Define the model and losses\n    encoded_train_batch, total_entropy = tabnet_model.encoder(\n        feature_train_batch, reuse=False, is_training=True\n    )\n\n    logits_orig_batch, _ = tabnet_model.classify(encoded_train_batch, reuse=False)\n\n    softmax_orig_key_op = tf.reduce_mean(\n        tf.nn.sparse_softmax_cross_entropy_with_logits(\n            logits=logits_orig_batch, labels=label_train_batch\n        )\n    )\n\n    train_loss_op = softmax_orig_key_op + SPARSITY_LOSS_WEIGHT * total_entropy\n    tf.summary.scalar(\"Total loss\", train_loss_op)\n\n    # Optimization step\n    global_step = tf.train.get_or_create_global_step()\n    learning_rate = tf.train.exponential_decay(\n        INIT_LEARNING_RATE, global_step=global_step, decay_steps=DECAY_EVERY, decay_rate=DECAY_RATE\n    )\n    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n    with tf.control_dependencies(update_ops):\n        gvs = optimizer.compute_gradients(train_loss_op)\n        capped_gvs = [\n            (tf.clip_by_value(grad, -GRADIENT_THRESH, GRADIENT_THRESH), var) for grad, var in gvs\n        ]\n        train_op = optimizer.apply_gradients(capped_gvs, global_step=global_step)\n\n    # Model evaluation\n    # Test performance\n    encoded_test_batch, _ = tabnet_model.encoder(feature_test_batch, reuse=True, is_training=False)\n\n    _, prediction_test = tabnet_model.classify(encoded_test_batch, reuse=True)\n\n    predicted_labels = tf.cast(tf.argmax(prediction_test, 1), dtype=tf.int32)\n    test_eq_op = tf.equal(predicted_labels, label_test_batch)\n    test_acc_op = tf.reduce_mean(tf.cast(test_eq_op, dtype=tf.float32))\n    tf.summary.scalar(\"Test accuracy\", test_acc_op)\n\n    # Training setup\n    current_time = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n    model_name = MODEL_NAME + f\"_{current_time}\"\n    init = tf.initialize_all_variables()\n    init_local = tf.local_variables_initializer()\n    init_table = tf.tables_initializer(name=\"Initialize_all_tables\")\n    saver = tf.train.Saver()\n    summaries = tf.summary.merge_all()\n\n    with tf.Session() as sess:\n        summary_writer = tf.summary.FileWriter(\"./tflog/\" + model_name, sess.graph)\n\n        sess.run(init)\n        sess.run(init_local)\n        sess.run(init_table)\n        sess.run(train_iter.initializer)\n        sess.run(test_iter.initializer)\n\n        for step in range(1, MAX_STEPS + 1):\n            if step % DISPLAY_STEP == 0:\n                _, train_loss, merged_summary = sess.run([train_op, train_loss_op, summaries])\n                summary_writer.add_summary(merged_summary, step)\n                logger.info(\n                    \"Step \" + str(step) + \", Training Loss = \" + \"{:.4f}\".format(train_loss)\n                )\n            else:\n                _ = sess.run(train_op)\n\n            if step % TEST_STEP == 0:\n                feed_arr = [vars()[\"summaries\"], vars()[\"test_acc_op\"]]\n\n                test_arr = sess.run(feed_arr)\n                merged_summary = test_arr[0]\n                test_acc = test_arr[1]\n\n                logger.info(\"Step \" + str(step) + \", Test Accuracy = \" + \"{:.4f}\".format(test_acc))\n                summary_writer.add_summary(merged_summary, step)\n\n            if step % SAVE_STEP == 0:\n                saver.save(sess, \"./checkpoints/\" + model_name + \".ckpt\")\n\n\nif __name__ == \"__main__\":\n    app.run(main)\n","repo_name":"ptuls/tabnet-modified","sub_path":"train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":6431,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"77"}
+{"seq_id":"40250727588","text":"\n\"\"\"Functions to visualize human poses\"\"\"\n\nimport matplotlib.pyplot as plt\nimport data_utils\nimport numpy as np\nimport h5py\nimport os\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef show3Dpose(channels, ax, lcolor=\"#3498db\", rcolor=\"#e74c3c\", add_labels=False): # blue, orange\n  \"\"\"\n  Visualize the ground truth 3d skeleton\n\n  Args\n    channels: 96x1 vector. The pose to plot.\n    ax: matplotlib 3d axis to draw on\n    lcolor: color for left part of the body\n    rcolor: color for right part of the body\n    add_labels: whether to add coordinate labels\n  Returns\n    Nothing. Draws on ax.\n  \"\"\"\n\n  assert channels.size == len(data_utils.H36M_NAMES)*3, \"channels should have 96 entries, it has %d instead\" % channels.size\n  vals = np.reshape( channels, (len(data_utils.H36M_NAMES), -1) )\n\n  I   = np.array([1,2,3,1,7,8,1, 13,14,15,14,18,19,14,26,27])-1 # start points\n  J   = np.array([2,3,4,7,8,9,13,14,15,16,18,19,20,26,27,28])-1 # end points\n  LR  = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)\n\n  # Make connection matrix\n  for i in np.arange( len(I) ):\n    x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)]\n    # ax.plot(x, y, z, lw=3, marker = 'o', markersize = 5, c=lcolor if LR[i] else rcolor, markeredgecolor = lcolor)\n    ax.plot(x, y, z, lw=2, c=lcolor if LR[i] else rcolor)\n\n  RADIUS = 750 # space around the subject\n  xroot, yroot, zroot = vals[0,0], vals[0,1], vals[0,2]\n  ax.set_xlim3d([-RADIUS+xroot, RADIUS+xroot])\n  ax.set_zlim3d([-RADIUS+zroot, RADIUS+zroot])\n  ax.set_ylim3d([-RADIUS+yroot, RADIUS+yroot])\n\n  # ax.set_xlim3d([np.min(vals[:, 0]), np.max(vals[:, 0])])\n  # ax.set_zlim3d([np.min(vals[:, 2]), np.max(vals[:, 2])])\n  # ax.set_ylim3d([np.min(vals[:, 1]), np.max(vals[:, 1])])\n\n  if add_labels:\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.set_zlabel(\"z\")\n\n  # Get rid of the ticks and tick labels\n\n  ax.set_xticks([])\n  ax.set_yticks([])\n  ax.set_zticks([])\n\n  ax.get_xaxis().set_ticklabels([])\n  ax.get_yaxis().set_ticklabels([])\n  ax.set_zticklabels([])\n  ax.set_aspect('equal')\n\n  # Get rid of the panes (actually, make them white)\n  white = (1.0, 1.0, 1.0, 0.0)\n  ax.w_xaxis.set_pane_color(white)\n  # ax.w_zaxis.set_pane_color(white)\n  ax.w_yaxis.set_pane_color(white)\n  # # Keep z pane\n  #\n  # # Get rid of the lines in 3d\n  ax.w_xaxis.line.set_color(white)\n  ax.w_yaxis.line.set_color(white)\n  ax.w_zaxis.line.set_color(white)\n\n\n  ax.view_init(azim=129, elev=10)\n\n\n\ndef show2Dpose(channels, ax, lcolor=\"#3498db\", rcolor=\"#e74c3c\", add_labels=False):\n  \"\"\"\n  Visualize a 2d skeleton with 32 joints\n\n  Args\n    channels: 64x1 vector. The pose to plot.\n    ax: matplotlib axis to draw on\n    lcolor: color for left part of the body\n    rcolor: color for right part of the body\n    add_labels: whether to add coordinate labels\n  Returns\n    Nothing. Draws on ax.\n  \"\"\"\n\n  assert channels.size == len(data_utils.H36M_NAMES)*2, \"channels should have 64 entries, it has %d instead\" % channels.size\n  vals = np.reshape( channels, (len(data_utils.H36M_NAMES), -1) )\n\n  I  = np.array([1,2,3,1,7,8,1, 13,14,14,18,19,14,26,27])-1 # start points\n  J  = np.array([2,3,4,7,8,9,13,14,16,18,19,20,26,27,28])-1 # end points\n  LR = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)\n\n  # Make connection matrix\n  for i in np.arange( len(I) ):\n    x, y = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(2)]\n    ax.plot(x, y, lw=2, c=lcolor if LR[i] else rcolor)\n\n  # Get rid of the ticks\n  ax.set_xticks([])\n  ax.set_yticks([])\n\n  # Get rid of tick labels\n  ax.get_xaxis().set_ticklabels([])\n  ax.get_yaxis().set_ticklabels([])\n\n  RADIUS = 300 # space around the subject\n  xroot, yroot = vals[0,0], vals[0,1]\n  ax.set_xlim([-RADIUS+xroot, RADIUS+xroot])\n  ax.set_ylim([-RADIUS+yroot, RADIUS+yroot])\n  if add_labels:\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"z\")\n\n  ax.set_aspect('equal')\n\n\n\n\ndef show2Dpose_mdm(channels, ax, lcolor=\"#3498db\", rcolor=\"#e74c3c\", add_labels=False):\n  \"\"\"\n  Visualize 2d reprojections of all 3d pose hypotheses in one fig in order to show the similarity between them\n\n  Args\n    channels: 64 * 5, 2d reprojections of all 3d pose hypotheses\n    ax: matplotlib axis to draw on\n    lcolor: color for left part of the body\n    rcolor: color for right part of the body. Note that we do not really use lcolor and rcolor in this function. \n\t        In stead, we define a color for each hypotheses to show the overlap between them.\n    add_labels: whether to add coordinate labels\n  Returns\n    Nothing. Draws on ax.\n  \"\"\"\n\n\n\n\n  I  = np.array([1,2,3,1,7,8,1, 13,14,14,18,19,14,26,27])-1 # start points\n  J  = np.array([2,3,4,7,8,9,13,14,16,18,19,20,26,27,28])-1 # end points\n  LR = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)\n  colors = ['#FF8000', '#4169E1', '#308014', '#000080', '#FF83FA'] # color used for 2d reprejection from each 3d pose hypotheses \n  for m in range(channels.shape[-1]):\n    vals = np.reshape(channels[:,m], [len(data_utils.H36M_NAMES), -1])\n    for i in np.arange( len(I) ):\n      x, y = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(2)]\n      ax.plot(x, y, lw=2, c=colors[m])\n\n  # Get rid of the ticks\n  ax.set_xticks([])\n  ax.set_yticks([])\n\n  # Get rid of tick labels\n  ax.get_xaxis().set_ticklabels([])\n  ax.get_yaxis().set_ticklabels([])\n\n  RADIUS = 300 # space around the subject\n  xroot, yroot = vals[0,0], vals[0,1]\n  ax.set_xlim([-RADIUS+xroot, RADIUS+xroot])\n  ax.set_ylim([-RADIUS+yroot, RADIUS+yroot])\n  if add_labels:\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"z\")\n\n  ax.set_aspect('equal')\n","repo_name":"chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network","sub_path":"src/viz.py","file_name":"viz.py","file_ext":"py","file_size_in_byte":5566,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"77"}
+{"seq_id":"34659048802","text":"# *******************************************************************\n# Trang Nguyen trang_nguyen@csu.fullerton.edu CWID:802816165\n# Insert your name here\n# \n# CPSC 471 - Section 2\n# *******************************************************************\n\nimport sys\nimport subprocess\nimport socket\n\n#default server name\nserverName= \"localhost\"\n\n# max buffer size in server in bytes \nbufferSize=4096\n\n# *******************************************************************\n#\t\t\t\t\t\t\tFUNCTIONS\n# We need to write these functions:\n# a) Receives the specified number of bytes from a specified socket\n# b) Gets size of a file in bytes\n# c) Upload a file to server\n# d) Download a file from server\n# e) List file from the server\n# f) quit (disconnect/exit)\n# *******************************************************************\n\n\ndef recvAll(sock, numBytes):\n\n\t# The buffer\n\trecvBuff = \"\"\n\t\n\t# The temporary buffer\n\ttmpBuff = \"\"\n\t\n\t# Keep receiving till all is received\n\twhile len(recvBuff) < numBytes:\n\t\t\n\t\t# Attempt to receive bytes\n\t\ttmpBuff =  sock.recv(numBytes)\n\t\t\n\t\t# The other side has closed the socket\n\t\tif not tmpBuff:\n\t\t\tbreak\n\t\t\n\t\t# Add the received bytes to the buffer\n\t\trecvBuff += tmpBuff\n\t\n\treturn recvBuff\n\ndef createSocket(portNumb):\n\timport socket\n\t#create an INET, STREAMing socket\n\tclientSocket=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\n\t#bind the socket to a port\n\tclientSocket.connect((serverName,portNumb))\n\tprint(\"Connected to port #:\",portNumb)\n\n\treturn clientSocket\n\n\t\ndef sizeOfFile (fileName):\n\timport os\n\treturn os.path.getSize(fileName)\n\t\ndef uploadFileToServer(fileName):\n\n\t#generate an emphemeral port\n\ttempSocket=createSocket(portNumb)\n\n\t#open file \n\ttry:\n\t\tfile_object=open(fileName,'r')\n\texcept OSError:\n\t\tprint (\"can not open file %s to read\", fileName)\n\t\ttempSocket.close()\n\n\tfileSize=sizeOfFile(fileName)\n\n\twhile True:\n\t\t# Read  data\n\t\tfileData = file_object.read(fileName)\n\t\n\t\t# Make sure we did not hit EOF\n\t\tif fileData:\n\t\t\n\t\t\t\n\t\t\t# Get the size of the data read\n\t\t\t# and convert it to string\n\t\t\tdataSizeStr = str(len(fileData))\n\t\t\n\t\t\t# Prepend 0's to the size string\n\t\t\t# until the size is 10 bytes\n\t\t\twhile len(dataSizeStr) < 10:\n\t\t\t\tdataSizeStr = \"0\" + dataSizeStr\n\t\n\t\n\t\t\t# Prepend the size of the data to the\n\t\t\t# file data.\n\t\t\tfileData = dataSizeStr + fileData\t\n\t\t\n\t\t\t# The number of bytes sent\n\t\t\tnumSent = 0\n\t\t\n\t\t\t# Send the data!\n\t\t\twhile len(fileData) > numSent:\n\t\t\t\tnumSent += tempSocket.send(fileData[numSent:])\n\t\n\t\t# The file has been read. We are done\n\t\telse:\n\t\t\tbreak\n\n\t\tprint (\"Sent \", numSent, \" bytes.\")\n\t\n\t# Close the socket and the file\n\tfile_object.close()\n\ttempSocket.close()\n\t\n\t\n\n#def downloadFileFromServer(socket,fileName):\n\n# *******************************************************************\n#\t\t\t\t\t\t\tMAIN PROGRAM\n# *******************************************************************\n\n\n#if client command line has 3 args. for ex: python client.py localhost 1234\n\nif len(sys.argv) < 3:\n\tprint (\"python \" + sys.argv[0]+\"<server_machine>\"+\"<server_port>\")\n\nserverName=sys.argv[1]\nserverPort=int(sys.argv[2])\n\nclientSocket= createSocket(serverPort)\n\n\n","repo_name":"trangnguyen88/471FinalProject","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33953391789","text":"val = 36000000\ntop = int(val / 10)\npackets = [0 for _ in range(val)]\nmax_packets = 0\nstop = False\n\nfor i in range(1, top + 1):\n    for j in range(i, top + 1, i):\n        # if j not in packets.keys():\n        #     packets[j] = 0\n\n        packets[j] += i\n\n        if packets[j] > max_packets:\n            max_packets = packets[j]\n            print(max_packets)\n\n        if packets[j] >= top:\n            stop = True\n            print('---')\n            print(packets[j])\n            print(j)\n\n    if stop:\n        exit()\n\n# 3326400 fail\n","repo_name":"smaaland/python-sandbox","sub_path":"adventofcode/2015/day20.py","file_name":"day20.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35891004282","text":"import os\nimport inject\n\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import QtCore\n\nfrom . import SettingsTitle\nfrom . import WidgetSettings\nfrom . import PictureButton\n\nfrom PyQt5 import QtGui\n\n\nclass WidgetSettingsStorage(WidgetSettings):\n    locationAction = QtCore.pyqtSignal(object)\n\n    @inject.params(config='config')\n    def __init__(self, config):\n        super(WidgetSettingsStorage, self).__init__()\n\n        self.layout = QtWidgets.QVBoxLayout()\n        self.layout.setAlignment(Qt.AlignLeft)\n\n        self.layout.addWidget(SettingsTitle('Shortcuts'))\n\n        self.layout.addWidget(QtWidgets.QLabel('Ctl+N - create new note'))\n        self.layout.addWidget(QtWidgets.QLabel('Ctl+R - create new group'))\n        self.layout.addWidget(QtWidgets.QLabel('Ctl+I - import note from a file'))\n        self.layout.addWidget(QtWidgets.QLabel('Ctl+F - start fulltext search'))\n\n        spacer = QtWidgets.QWidget()\n        spacer.setSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        self.layout.addWidget(spacer)\n\n        location = config.get('storage.location')\n        location = location.replace(os.path.expanduser('~'), '~')\n\n        self.layout.addWidget(SettingsTitle('Storage'))\n\n        self.location = PictureButton(' {}'.format(location))\n        self.location.clicked.connect(self.locationAction.emit)\n        self.location.setIcon(QtGui.QIcon(\"icons/save\"))\n        self.location.setToolTip(\"Clone selected preview\")\n        self.location.setFlat(True)\n        self.layout.addWidget(self.location)\n\n        self.setLayout(self.layout)\n\n    def quit(self):\n        self.thread.exit()\n","repo_name":"AlexWoroschilow/AOD-Notes","sub_path":"src/modules/storage_filesystem/gui/settings/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2445483777","text":"#1\n\nmy_int = \"7020010060810340230200\"\nnew_my_int = \"0\"\nresult = my_int.count(new_my_int)\nprint(result)\n\n#############################\n\n#2\n\nmy_int = \"7020010060810340230200\"\ncount = 0\nfor e in str(my_int)[::-1]:\n    if e =='0':\n       count += 1\n    else:\n           break\nprint(count)\n\n#################################\n\n#3a\n\nmy_list_1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nmy_list_2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nmy_result = my_list_2[::2] + my_list_2[1::2]\nprint(my_result)\n\n#####################################\n\n#3б\n\nmy_list_1, my_list_2 = [1, 3, 2, 4,5 ] , [10, 20, 15, 25, 22]\nmy_result = [my_list_1[i] for i in range(len(my_list_1)) if my_list_1[i] % 2 == 0] + [my_list_2[i] for i in range(len(my_list_2)) if my_list_2[i] % 2 == 1]\nprint(my_result)\n\n#######################################\n\n#4\n\nmy_list = [1,2,3,4]\nnew_list = []\nnew_list = my_list[1:]\nvalue = my_list[0]\nnew_list.append(value)\nprint(new_list)\n\n\n#############################\n\n# 5\n\nmy_list = [1,2,3,4]\nmy_list += [my_list.pop(0)]\nprint(my_list)\n\n#########################\n\n#6\n\nstr = '43 34 56'\ndef to_int(text, default=0):\n    try:\n        return int(text)\n    except ValueError:\n        return default\n\ntext_numbers = ['43 34 56']\nprint(sum(number for word in text_numbers for number in map(to_int, word.split())))\n\nEdited #6\n\nstr = '43 34 56'\nnew_str = ['43 34 56']\nnew_str = [sum(map(int, s.split())) for s in new_str]\nprint(new_str)\n\n###############################\n\n#7\n\nimport re\nmy_str ='Vladyslav'\nmy_str += '_' * (len(my_str) % 2)\nprint(re.findall('.{%s}' % 2, my_str))\n\nEdited #7\n\nmy_str ='Vladyslav'\nnew_my_str = (list(map(''.join, zip(*[iter(my_str + '_')]*2))))\nprint(new_my_str)\n###############################\n\n#8\n\nmy_str = \"My_long str\"\nl_limit = 'o'\nr_limit = 't'\nindex_l_limit = my_str.index(l_limit)\nindex_r_limit = my_str.index(r_limit)\nsub_str = (my_str[index_l_limit + 1:index_r_limit])\nprint(sub_str)\n\n#9\n\nmy_str = \"My long string\"\nl_limit = \"o\"\nr_limit = \"g\"\ni1 = my_str.find(l_limit)\ni2 = my_str.rfind(r_limit)\nsub_str = my_str[i1+1 : i2]\nprint(sub_str)\n\n#10\n\nmy_list = [2, 4, 1, 5, 3, 9, 0, 7]\ncount = 0\nfor e in range (1, len(my_list)-2):\n    if my_list[e-1]<my_list[e] and my_list[e]>my_list[e+1]:\n       count += 1\nprint(count)","repo_name":"vladzabluda/IntroPyton_14_01_Vlad","sub_path":"Homework #5.py","file_name":"Homework #5.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"375031016","text":"from argparse import ArgumentParser\nimport logging\nimport traceback\n\nimport gi  # nopep8\ngi.require_version('GLib', '2.0')  # nopep8\nfrom gi.repository import GLib  # nopep8\n\nfrom rr.config.app_config_loader import AppConfigLoader\nfrom rr.smart_cctv import SmartCCTV\n\n\ndef error(e):\n    if error.verbose:\n        logging.error(traceback.format_exc())\n    logging.error(e)\n\n    if not error.verbose:\n        logging.error(\"Execute with '-v' to get the full trace\")\n\n\ndef parse_args():\n\n    parser = ArgumentParser(description='AI CCTV system for smart cities')\n    parser.add_argument('-f', dest='config_file', default='config.yaml',\n                        help='configuration file used by the system.')\n    parser.add_argument(\n        '-v',\n        dest='verbose',\n        default=False,\n        action='store_true',\n        help='Print full stack trace of errors. Useful for debugging.')\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_args()\n\n    error.verbose = args.verbose\n\n    config = AppConfigLoader()\n\n    try:\n        config_dict = config.load(args.config_file)\n        server = SmartCCTV(config_dict)\n    except Exception as e:\n        error(e)\n        exit(-1)\n\n    try:\n        server.start()\n        print(\"Starting Smart CCTV server, press ctrl+c to interrupt...\")\n        GLib.MainLoop().run()\n    except KeyboardInterrupt:\n        print(\"Cleaning up.\")\n        server.stop()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"RidgeRun/ti-edge-ai-demos","sub_path":"smartcity.py","file_name":"smartcity.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3468848496","text":"import colorsys\nimport itertools\nfrom typing import Iterable\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch\nfrom sklearn.metrics import confusion_matrix\n\nfrom thunder_ml.modules import ThunderModule\nfrom thunder_ml.routines import InferenceMode\n\n\ndef convert_distribution_to_predictions(batch: torch.Tensor):\n    if len(batch.shape) > 2:\n        raise ValueError(\"Distributions have at most 2 dimensions\")\n\n    if len(batch.shape) == 1 and torch.is_floating_point(batch):\n        batch = batch.reshape(1, -1)\n\n    predictions = torch.argmax(batch, dim=-1)\n\n    return predictions\n\n\nclass ConfusionMatrixCallback(pl.Callback):\n    def __init__(self, classes=None, cmap=plt.cm.Blues):\n        # check if we have proper labels or just numbers for classes\n        if isinstance(classes, int):\n            self.class_indices = np.arange(0, classes)\n            self.class_names = self.class_indices\n        elif isinstance(classes, Iterable):\n            self.class_indices = np.arange(0, len(list(classes)))\n            self.class_names = classes\n        else:\n            self.class_indices = None\n            self.class_names = None\n\n        if isinstance(cmap, str):\n            cmap = matplotlib.colormaps[cmap]\n        elif not isinstance(cmap, matplotlib.cm.colors.Colormap):\n            raise TypeError(\"`cmap` must be of type `str` or `Colormap`\")\n\n        self.cmap = cmap\n\n    # do after each validation\n    def on_validation_epoch_end(\n        self, trainer: \"pl.Trainer\", pl_module: \"ThunderModule\"\n    ):\n        if not isinstance(pl_module, ThunderModule):\n            raise ValueError(\"Module needs to be a ThunderModule!\")\n\n        # thanks to our ThunderModule, we have a history of inferences\n        outputs = pl_module.outputs.get(InferenceMode.VALIDATION, [])\n        ytrue = None\n        ypred = None\n\n        if len(outputs) == 0:\n            raise RuntimeError(\"No inferences logged.\")\n\n        # make a long list of predictions and ground truths\n        for (inputs, y_targets), y_distributions in outputs:\n            y_predictions = convert_distribution_to_predictions(y_distributions)\n\n            if ypred is None:\n                ypred = y_predictions\n            else:\n                ypred = torch.cat([ypred, y_predictions], 0)\n\n            if ytrue is None:\n                ytrue = y_targets\n            else:\n                ytrue = torch.cat([ytrue, y_targets], 0)\n\n        if self.class_indices is None or len(self.class_indices) == 0:\n            class_indices = list(set(np.unique(ytrue)).union(set(np.unique(ypred))))\n        else:\n            class_indices = self.class_indices\n\n        if self.class_names is None or len(self.class_names) == 0:\n            class_names = class_indices\n        else:\n            class_names = self.class_names\n\n        # let sklearn make the matrix and than we normalize it between zero and one\n        cf_matrix = confusion_matrix(\n            ytrue, ypred, labels=class_indices, normalize=\"true\"\n        ).astype(float)\n\n        figure = conf_matrix2figure(cf_matrix, class_names, self.cmap)\n\n        # use thunder modules integration to tensorboard to add the figure directly\n        pl_module.add_figure(\"conf_matrix\", figure)\n\n        # clean up\n        plt.close(figure)\n\n\ndef conf_matrix2figure(cf_matrix, class_names, cmap):\n    # create a figure in matplot\n    figure = plt.figure(figsize=(8, 8))\n    plt.imshow(cf_matrix, interpolation=\"nearest\", cmap=cmap, vmin=0, vmax=1)\n    plt.title(\"Confusion matrix\")\n\n    # add a scale\n    plt.colorbar()\n\n    # put proper labels to the matrix\n    tick_marks = np.arange(len(class_names))\n    plt.xticks(tick_marks, class_names, rotation=45)\n    plt.yticks(tick_marks, class_names)\n\n    # Use white text if squares are dark; otherwise black.\n    threshold = cf_matrix.max() / 2.0\n\n    # add percentages in each cell\n    for i, j in itertools.product(range(cf_matrix.shape[0]), range(cf_matrix.shape[1])):\n        red, green, blue, _ = cmap(cf_matrix[i, j])\n        _, lightness, _ = colorsys.rgb_to_hls(red, green, blue)\n\n        color = \"white\" if lightness < threshold else \"black\"\n        plt.text(\n            j,\n            i,\n            f\"{cf_matrix[i, j]:.2%}\",\n            horizontalalignment=\"center\",\n            verticalalignment=\"center\",\n            color=color,\n        )\n\n    # finishing touches\n    plt.ylabel(\"True label\")\n    plt.xlabel(\"Predicted label\")\n    plt.tight_layout()\n\n    return figure\n","repo_name":"Moraxno/thunder-ml","sub_path":"thunder_ml/callbacks/conf_matrix.py","file_name":"conf_matrix.py","file_ext":"py","file_size_in_byte":4505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15039956125","text":"import cv2\nimport numpy as np\nfrom vhh_cmc.Configuration import Configuration\n\n\nclass PreProcessing(object):\n    \"\"\"\n    This class is used to pre-process frames.\n    \"\"\"\n\n    def __init__(self, config_instance: Configuration):\n        \"\"\"\n        Constructor\n\n        :param config_file: [required] path to configuration file (e.g. PATH_TO/config.yaml)\n                            must be with extension \".yaml\"\n        \"\"\"\n        print(\"create instance of pre-processing ... \")\n        self.config_instance = config_instance\n\n    def applyTransformOnImg(self, image: np.ndarray) -> np.ndarray:\n        \"\"\"\n        This method is used to apply the configured pre-processing methods on a numpy frame.\n\n        :param image: This parameter must hold a valid numpy image (WxHxC).\n        :return: This methods returns the preprocessed numpy image.\n        \"\"\"\n        image_trans = image\n\n        # convert to grayscale image\n        if(int(self.config_instance.flag_convert2Gray) == 1):\n            image_trans = self.convertRGB2Gray(image_trans)\n\n        # crop image\n        if (int(self.config_instance.center_crop_flag) == 1):\n            image_trans = self.crop(image_trans, (image_trans.shape[0], image_trans.shape[0]))\n            #image_trans = self.crop(image_trans, (700, 512))\n\n        # resize image\n        if(self.config_instance.flag_downscale == 1):\n            dim = self.config_instance.resize_dim\n            image_trans = self.resize(image_trans, dim)\n\n        return image_trans\n\n    def convertRGB2Gray(self, img: np.ndarray):\n        \"\"\"\n        This method is used to convert a RBG numpy image to a grayscale image.\n\n        :param img: This parameter must hold a valid numpy image.\n        :return: This method returns a grayscale image (WxHx1).\n        \"\"\"\n        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        #img_gray = np.expand_dims(img_gray, axis=-1)\n        return img_gray\n\n    def crop(self, img: np.ndarray, dim: tuple):\n        \"\"\"\n        This method is used to crop a specified region of interest from a given image.\n\n        :param img: This parameter must hold a valid numpy image.\n        :param dim: This parameter must hold a valid tuple including the crop dimensions.\n        :return: This method returns the cropped image.\n        \"\"\"\n        crop_w, crop_h = dim\n\n        crop_h_1 = 0\n        crop_h_2 = 0\n        crop_w_1 = 0\n        crop_w_2 = 0\n\n        img_h = img.shape[0]\n        img_w = img.shape[1]\n\n        crop_w_1 = int(img_w / 2) - int(crop_w / 2)\n        if (crop_w_1 < 0):\n            crop_w_1 = 0\n\n        crop_w_2 = int(img_w / 2) + int(crop_w / 2)\n        if (crop_w_2 >= img_w):\n            crop_w_2 = img_w\n\n        crop_h_1 = int(img_h / 2) - int(crop_h / 2)\n        if (crop_h_1 < 0):\n            crop_h_1 = 0\n\n        crop_h_2 = int(img_h / 2) + int(crop_h / 2)\n        if (crop_h_2 >= img_h):\n            crop_h_2 = img_h\n\n        img_crop = img[crop_h_1:crop_h_2, crop_w_1:crop_w_2]\n        return img_crop\n\n    def resize(self, img: np.ndarray, dim: tuple):\n        \"\"\"\n        This method is used to resize a image.\n\n        :param img: This parameter must hold a valid numpy image.\n        :param dim: This parameter must hold a valid tuple including the resize dimensions.\n        :return: This method returns the resized image.\n        \"\"\"\n        img_resized = cv2.resize(img, dim)\n        if(int(self.config_instance.flag_convert2Gray) == 1):\n            img_resized = np.expand_dims(img_resized, axis=-1)\n        return img_resized\n\n\n","repo_name":"dahe-cvl/vhh_cmc","sub_path":"vhh_cmc/PreProcessing.py","file_name":"PreProcessing.py","file_ext":"py","file_size_in_byte":3524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72813531448","text":"import pandas as pd\nimport requests\nimport json\n\ndef yahoo_opt_clean(x, type):\n    x = pd.io.json.json_normalize(x['optionChain']['result'][0]['options'][0][type])\n    x = x[['lastPrice', 'expiration', 'strike', 'inTheMoney']]\n    if type == 'calls':\n        x['type'] = 'C'\n    elif type == 'puts':\n        x['type'] = 'P'\n    else:\n        raise ValueError('Unknown option type')\n    return x\n\n\ndef get_options():\n    url = 'https://query2.finance.yahoo.com/v7/finance/options/SPY'\n    content = requests.get(url).text\n    content = json.loads(content)\n    current_price = content['optionChain']['result'][0]['quote']['regularMarketPrice']\n    current_date = content['optionChain']['result'][0]['quote']['regularMarketTime']\n    dates = content['optionChain']['result'][0]['expirationDates']\n    options = yahoo_opt_clean(content, 'calls')\n    df = yahoo_opt_clean(content, 'puts')\n    options = options.append(df, ignore_index=True)\n    for i in range(1, len(dates)):\n        content = requests.get(url + '?date=' + str(dates[i])).text\n        content = json.loads(content)\n        num_strikes = len(content['optionChain']['result'][0]['strikes'])\n        if num_strikes > 1:\n            df = yahoo_opt_clean(content, 'calls')\n            options = options.append(df, ignore_index=True)\n            df = yahoo_opt_clean(content, 'puts')\n            options = options.append(df, ignore_index=True)\n        else:\n            break\n    return options, current_price, current_date\n\nif __name__ == '__main__':\n    options, current_price, date = get_options()\n    pathname = 'saved_data/options_' + str(date) + '.csv'\n    options.to_csv(pathname, index=False)\n","repo_name":"ikmckenz/vol_surface","sub_path":"src/yahoo_options.py","file_name":"yahoo_options.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70283899129","text":"import streamlit as st\nfrom send_email import send_email\n\nst.header(\"Contact Me\")\n\nst.write(\"If you have any questions or want to collaborate, feel free to reach out to me.\")\n\nwith st.form(key=\"email_form\"):\n    user_email = st.text_input(\"Your Email Address\")\n    raw_message = st.text_area(\"Your Message\")\n    message = f\"\"\"\\\n    Subject: New email from {user_email}\n    \n    From: {user_email}\n    {raw_message}\n    \"\"\"\n    submit_button = st.form_submit_button(label=\"Send\")\n    if submit_button:\n        send_email(message)\n        st.info(\"Thank you for your message!\")\n        st.balloons()\n\n","repo_name":"martinwinther/python-portfolio","sub_path":"pages/Contact_Me.py","file_name":"Contact_Me.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27945150428","text":"#!/usr/bin/env python\n\n__author__ = 'bfc'\n\nimport argparse\n\ndef say_hello(message):\n    '''Say hello and print a message.\n    '''\n\n    print('Hello there! I am here to tell you ' +str(message))\n\n\n# main\n\ndef _add_parser_args(parser):\n    parser.add_argument(\n        \"-m\",\n        \"--message\",\n        required=True,\n        help=\"A message to print for the user.\",\n        dest=\"message\",\n    )\n    \ndef _main(args=None):\n    '''Read and print messages.\n    '''\n\n    if not args:\n        parser = argparse.ArgumentParser()\n        _add_parser_args(parser)\n        args = parser.parse_args()\n\n    # load options\n    msg = 'the default message'\n\n    if args.message:\n        msg = args.message\n\n    # print the message\n\n    say_hello(msg)\n","repo_name":"bfc5288/hellopypi","sub_path":"hellopypi_bfc/client/testfunc.py","file_name":"testfunc.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40253608578","text":"'''\nhttps://leetcode-cn.com/problems/most-stones-removed-with-same-row-or-column\n\n在二维平面上，我们将石头放置在一些整数坐标点上。每个坐标点上最多只能有一块石头。\n\n现在，move 操作将会移除与网格上的某一块石头共享一列或一行的一块石头。\n\n我们最多能执行多少次 move 操作？\n \n示例 1：\n输入：stones = [[0,0],[0,1],[1,0],[1,2],[2,1],[2,2]]\n输出：5\n\n示例 2：\n输入：stones = [[0,0],[0,2],[1,1],[2,0],[2,2]]\n输出：3\n\n示例 3：\n输入：stones = [[0,0]]\n输出：0\n\n \n提示：\n\n1 <= stones.length <= 1000\n0 <= stones[i][j] < 10000\n'''\n\nclass Solution:\n    def removeStones(self, stones):\n        \"\"\"\n        :type stones: List[List[int]]\n        :rtype: int\n        \"\"\"\n        \n\n\nif __name__ == '__main__':\n    s = Solution()\n    # ret = s.\n    print(s)\n","repo_name":"chanfengsr/AllPrivateProject","sub_path":"Python/LeetCodeTraining/题库/0947 移除最多的同行或同列石头(Most Stones Removed with Same Row or Column).py","file_name":"0947 移除最多的同行或同列石头(Most Stones Removed with Same Row or Column).py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"32999453294","text":"import calendar\nimport csv\nimport datetime\nfrom datetime import date\nfrom io import StringIO\n\nimport xlrd\nfrom beancount.core import data\nfrom beancount.core.data import Note, Transaction\n\nfrom . import (DictReaderStrip, get_account_by_guess,\n               get_income_account_by_guess)\nfrom .base import Base\nfrom .deduplicate import Deduplicate\n\nAccount余额宝 = 'Assets:Company:Alipay:MonetaryFund'\nincomes = ['余额自动转入', '收益', '单次转入']\n\n\nclass YuEBao(Base):\n\n    def __init__(self, filename, byte_content, entries, option_map):\n        if not filename.endswith('xls'):\n            raise RuntimeError('Not YuEBao!')\n        data = xlrd.open_workbook(filename)\n        table = data.sheets()[0]\n        rows_value = table.row_values(0)\n        if rows_value[0] != '余额宝收支明细查询':\n            raise RuntimeError('Not YuEBao!')\n        self.book = data\n        self.table = table\n        self.deduplicate = Deduplicate(entries, option_map)\n\n    def parse(self):\n        table = self.table\n        rows = table.nrows\n        for i in range(5, rows - 4):\n            row = table.row_values(i)\n            time = datetime.datetime(\n                *xlrd.xldate_as_tuple(table.cell_value(rowx=i, colx=0), self.book.datemode))\n            print(\"Importing {} price = {} balance = {}\".format(\n                time, row[2], row[3]))\n            meta = {}\n            amount = float(row[1])\n\n            entry = Transaction(\n                meta,\n                date(time.year, time.month, time.day),\n                '*',\n                '余额宝',\n                '余额宝',\n                data.EMPTY_SET,\n                data.EMPTY_SET, []\n            )\n\n            if not row[2] in incomes:\n                amount = -amount\n\n            if not self.deduplicate.find_duplicate(entry, amount, None, Account余额宝):\n                print(\n                    \"Unknown transaction for {}, check if Alipay transaction exists.\".format(time))\n\n        self.deduplicate.apply_beans()\n        return []\n","repo_name":"zsxsoft/my-beancount-scripts","sub_path":"modules/imports/yuebao.py","file_name":"yuebao.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","stars":328,"dataset":"github-code","pt":"77"}
+{"seq_id":"26762174558","text":"query_lookup = {\n    1: \"A\",\n    2: \"AAAA\",\n    3: \"ANY\",\n    4: \"SRV\",\n    5: \"SOA\",\n    6: \"PTR\",\n    7: \"TXT\",\n    8: \"NAPTR\",\n    9: \"MX\",\n    10: \"DS\",\n    11: \"RRSIG\",\n    12: \"DNSKEY\",\n    13: \"NS\",\n    14: \"OTHER\",\n    15: \"SVCB\",\n    16: \"HTTPS\"\n}\nquery_lookup_r = {v: k for k, v in query_lookup.items()}\n\nstatus_help = {\n    '🛑Blocked': 'BLOCK',\n    '🟢Allowed': 'ALLOW'\n}\n\nstatus_lookup = {\n    'BLOCK': [1, 4, 5, 6, 7, 8, 9, 10, 11],\n    'ALLOW': [2, 3, 12, 13, 14]\n}\n","repo_name":"haykh/pihole-dash","sub_path":"utils/defs.py","file_name":"defs.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10658489507","text":"import grpc\nfrom concurrent import futures\nimport chunk_pb2, chunk_pb2_grpc\nimport time\n\n\nclass ClassificationServer(chunk_pb2_grpc.ClassificationServerServicer):\n    def __init__(self):\n        class Servicer(chunk_pb2_grpc.ClassificationServerServicer):\n            def inquire(self, chunks, context):\n                buffer = b''\n                for chunk in chunks:\n                    buffer += chunk.buffer\n\n                time.sleep(3)\n\n                msg = 'S^PANTS^^^test.jpg'\n                return chunk_pb2.Response(message=msg)\n\n        self.server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))\n        chunk_pb2_grpc.add_ClassificationServerServicer_to_server(Servicer(), self.server)\n\n    def start(self, port):\n        self.server.add_insecure_port(f'[::]:{port}')\n        self.server.start()\n        self.server.wait_for_termination()\n","repo_name":"StephenILee/grpc_example","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12132819326","text":"\nfrom __future__ import print_function\nimport numpy as np\n\nfrom keras.models import Sequential\nfrom keras.layers.core import (Dense, Dropout,\n                               Activation, Flatten)\nfrom keras.layers.embeddings import Embedding\nfrom keras.layers.convolutional import Convolution1D, MaxPooling1D\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nimport theano\nimport cPickle\nfrom keras.regularizers import l2, l1, l1l2\nfrom convert_review import build_design_matrix\nimport keras.backend as K\nimport modelParameters\nimport os\nimport datetime\nfrom activations import DistanceMetric\n\nDEVSPLIT = 14\nUSEWORDS = True\n\nif USEWORDS:\n\tVocabSize = modelParameters.VocabSize_w\n\tmaxReviewLen = modelParameters.MaxLen_w\n\tskipTop = modelParameters.skip_top\nelse:\n\tVocabSize = modelParameters.VocabSize_c\n\tmaxReviewLen = modelParameters.MaxLen_c\n\tskipTop = 0\n\ndef contrastiveLoss(Xl,Xr,y):\n\treturn y*K.l2_normalize(Xl,Xr) + (1-y)*K.max(10,10-K.l2_normalize(Xl,Xr))\n\n\nbatch_size = 80\n\nnum_filters1 = 1300\nfilter_length1 = 2\nstride_len1 = 1\npool_len1 = 2\n\nnum_filters2 = 800\nfilter_length2 = 3\nstride_len2 = 1\npool_len2 = 2\n\nnum_filters3 = 500\nfilter_length3 = 4\nstride_len3 = 1\npool_len3 = 2\n\n\nnum_filters4 = 300\nfilter_length4 = 5\nstride_len4 = 1\npool_len4 = 2\n\n\nembedding_dims = 200\n\nhidden_dims = 100\nnum_epochs = 5\n\ndef train_siamese_model():\n\n\tprint( 'Loading data...' )\n\n\t((X_train, y_train), (X_test, y_test)) = build_design_matrix( VocabSize,\n\t                                                              use_words = USEWORDS,\n\t                                                              skip_top = skipTop,\n\t                                                              dev_split = DEVSPLIT )\n\n\tprint( len( X_train ), 'train sequences' )\n\tprint( len( X_test ), 'test sequences' )\n\n\tprint( 'X_train shape:', X_train.shape )\n\tprint( 'X_test shape:', X_test.shape )\n\n\tprint( 'Build model...' )\n\n\n\t#LEFT and RIGHT branches of siamese network\n\tmodelL = Sequential( )\n\tmodelR = Sequential( )\n\n\n\t# we start off with an efficient embedding layer which maps\n\t# our vocab indices into embedding_dims dimensions\n\tmodelL.add( Embedding( VocabSize, embedding_dims, input_length = maxReviewLen ) )\n\tmodelR.add( Embedding( VocabSize, embedding_dims, input_length = maxReviewLen ) )\n\n\tmodelL.add( Dropout( 0.10 ) )\n\tmodelR.add( Dropout( 0.10 ) )\n\t###init changed from uniform to glorot_norm\n\tmodelL.add( Convolution1D( nb_filter = num_filters1,\n\t                          filter_length = filter_length1,\n\t                          border_mode = 'valid',\n\t                          activation = 'relu',\n\t                          subsample_length = stride_len1,\n\t                          init = 'uniform'\n\t                          ) )\n\n\tmodelR.add( Convolution1D( nb_filter = num_filters1,\n\t                           filter_length = filter_length1,\n\t                           border_mode = 'valid',\n\t                           activation = 'relu',\n\t                           subsample_length = stride_len1,\n\t                           init = 'uniform'\n\t                           ) )\n\n\n\t# input_length=maxReviewLen, input_dim=VocabSize\n\n\tmodelL.add( Dropout( 0.25 ) )\n\tmodelR.add( Dropout( 0.25 ) )\n\n\n\tmodelL.add( MaxPooling1D( pool_length = 2 ) )\n\tmodelR.add( MaxPooling1D( pool_length = 2 ) )\n\n\n\n\n\tmodelL.add( Convolution1D( nb_filter = num_filters2,\n\t                          filter_length = filter_length2,\n\t                          border_mode = 'valid',\n\t                          activation = 'relu',\n\t                          subsample_length = stride_len2,\n\t                          init = 'uniform'\n\n\t                          ) )\n\n\tmodelR.add( Convolution1D( nb_filter = num_filters2,\n\t                           filter_length = filter_length2,\n\t                           border_mode = 'valid',\n\t                           activation = 'relu',\n\t                           subsample_length = stride_len2,\n\t                           init = 'uniform'\n\n\t                           ) )\n\n\n\n\tmodelL.add( Dropout( 0.40 ) )\n\tmodelR.add( Dropout( 0.40 ) )\n\n\t# we use standard max pooling (halving the output of the previous layer):\n\tmodelL.add( MaxPooling1D( pool_length = 2 ) )\n\tmodelR.add( MaxPooling1D( pool_length = 2 ) )\n\n\n\n\n\tmodelL.add( Convolution1D( nb_filter = num_filters3,\n\t                          filter_length = filter_length3,\n\t                          border_mode = 'valid',\n\t                          activation = 'relu',\n\t                          subsample_length = stride_len3,\n\t                          init = 'uniform'\n\n\t                          ) )\n\n\tmodelR.add( Convolution1D( nb_filter = num_filters3,\n\t                           filter_length = filter_length3,\n\t                           border_mode = 'valid',\n\t                           activation = 'relu',\n\t                           subsample_length = stride_len3,\n\t                           init = 'uniform'\n\n\t                           ) )\n\n\n\n\n\n\n\n\tmodelL.add( Dropout( 0.30 ) )\n\tmodelR.add( Dropout( 0.30 ) )\n\n\n\n\n\tmodelL.add( MaxPooling1D( pool_length = 2 ) )\n\n\tmodelR.add( MaxPooling1D( pool_length = 2 ) )\n\n\n\n\n\tmodelL.add( Convolution1D( nb_filter = num_filters4,\n\t                          filter_length = filter_length4,\n\t                          border_mode = 'valid',\n\t                          activation = 'relu',\n\t                          subsample_length = stride_len4,\n\t                          init = 'uniform'\n\n\t                          ) )\n\n\tmodelR.add( Convolution1D( nb_filter = num_filters4,\n\t                           filter_length = filter_length4,\n\t                           border_mode = 'valid',\n\t                           activation = 'relu',\n\t                           subsample_length = stride_len4,\n\t                           init = 'uniform'\n\n\t                           ) )\n\n\n\n\tmodelL.add( Dropout( 0.25 ) )\n\tmodelR.add( Dropout( 0.25 ) )\n\n\tmodelL.add( MaxPooling1D( pool_length = pool_len4 ) )\n\tmodelR.add( MaxPooling1D( pool_length = pool_len4 ) )\n\n\n\n\n\n\n\t# We flatten the output of the conv layer,\n\t# so that we can add a vanilla dense layer:\n\tmodelL.add( Flatten( ) )\n\tmodelR.add( Flatten( ) )\n\n\t# We add a vanilla hidden layer:\n\tmodelL.add( Dense( hidden_dims ) )\n\tmodelL.add( Activation( 'relu' ) )\n\n\tmodelR.add( Dense( hidden_dims ) )\n\tmodelR.add( Activation( 'relu' ) )\n\n\n\n\tmerged_vector = merge([modelL, modelR], mode='concat', concat_axis=-1)\n\n\n\tGw=Dense(1, activation=DistanceMetric)(merged_vector)\n\n\n\tmodel.compile( loss = contrastiveLoss,\n\t               optimizer = 'rmsprop',\n\t               )\n","repo_name":"scparedes/sentiment-manifolds","sub_path":"siamese_network.py","file_name":"siamese_network.py","file_ext":"py","file_size_in_byte":6545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27820681484","text":"import copy\nimport logging\nimport os\nimport re\nimport tornado.httpclient\nimport unittest.mock\n\nfrom test_muniments import test_helpers\nfrom test_muniments.test_common import MetaTest\nfrom test_muniments.test_fixtures import Fixture\nfrom test_muniments.test_integration import BaseIntegrationTest\n\nlogger = logging.getLogger(__name__)\n\n\nclass CouchDbFixture(Fixture):\n    def setup(self):\n        request = copy.deepcopy(self.storage['write']['request'])\n        request['url'] = request['url'].rsplit('/', 1)[0] + '/'\n\n        self.fetch_request({\n            'url': request['url'],\n            'method': 'DELETE',\n        })\n\n        self.fetch_request(request)\n\n    def check_response(self, response):\n        self.context.assertEqual(response['code'], self.result.code)\n\n        if 'reason' in response:\n            self.context.assertEqual(response['reason'], self.result.reason)\n\n        expected_headers = response.get('headers', {})\n\n        expected_headers.setdefault('Content-Length', unittest.mock.ANY)\n        expected_headers.setdefault('Date', unittest.mock.ANY)\n        expected_headers.setdefault('Etag', unittest.mock.ANY)\n        expected_headers.setdefault('Server', unittest.mock.ANY)\n\n        self.context.assertEqual(expected_headers, self.result.headers)\n\n        self.context.maxDiff, _ = None, self.context.maxDiff\n        self.context.assertEqual(re.sub(r',\"_rev\":\"[^\"]+\"', '', self.result.body.decode('utf-8')).strip(), response.get('body', b'').decode('utf-8'))\n        self.context.maxDiff = _\n\n    def fetch_request(self, request):\n        kwargs = copy.deepcopy(request)\n        url = 'http://' + self.context.host + ':5984' + kwargs.pop('url')\n\n        logger.debug('url: %s', url)\n        logger.debug('kwargs: %s', kwargs)\n\n        request = tornado.httpclient.HTTPRequest(url, **kwargs)\n\n        return tornado.httpclient.HTTPClient().fetch(request, raise_error = False)\n\n\nclass CouchDbReadFixture(CouchDbFixture):\n    @property\n    def description(self):\n        return '{0.uuid.hex}—external.couchdb—{0.storage[read][request][method]:6} {0.url}'.format(self, self.context.real_module)\n\n    def setup(self):\n        self.fetch_request(self.storage['write']['request'])\n\n    def run(self):\n        self.result = self.fetch_request(self.storage['read']['request'])\n\n    def check(self):\n        self.check_response(self.storage['read']['response'])\n\n\nclass CouchDbWriteFixture(CouchDbFixture):\n    @property\n    def description(self):\n        return '{0.uuid.hex}—external.couchdb—{0.storage[write][request][method]:6} {0.url}'.format(self, self.context.real_module)\n\n    def run(self):\n        self.result = self.fetch_request(self.storage['write']['request'])\n\n    def check(self):\n        self.check_response(self.storage['write']['response'])\n\ntest_helpers.import_directory(__name__, os.path.dirname(__file__), sort_key = lambda _: 1 if _.rsplit('.', 1)[-1].startswith('list_') else 0)\n\n\nclass BaseCouchDbIntegrationTest(BaseIntegrationTest, tornado.testing.AsyncTestCase):\n    mocks_mask = set(( 'tornado.httpclient', )).union(BaseIntegrationTest.mocks_mask)\n    mocks = set().union(BaseIntegrationTest.mocks)\n\n    docker_compose_services = set(( 'couchdb', )).union(BaseIntegrationTest.docker_compose_services)\n\n    def setUp(self):\n        super().setUp()\n\n        couchdb_host = 'http://{0.host}:5984'.format(self)\n\n        os.environ['COUCHDB_URL'] = couchdb_host\n\n        def _():\n            del os.environ['COUCHDB_URL']\n\n        self.addCleanup(_)\n\n        logger.info('waiting for couchdb resolution')\n\n        while True:\n            try:\n                request = tornado.httpclient.HTTPRequest(couchdb_host)\n\n                response = tornado.httpclient.HTTPClient().fetch(request, raise_error = False)\n                logger.debug('response: %s', response)\n\n                if response.code in range(200, 300):\n                    break\n            except tornado.httpclient.HTTPError:\n                pass\n\n\nclass CouchDbIntegrationTest(BaseCouchDbIntegrationTest, metaclass = MetaTest):\n    mocks_mask = set().union(BaseCouchDbIntegrationTest.mocks_mask)\n    mocks = set().union(BaseCouchDbIntegrationTest.mocks)\n\n    docker_compose_services = set().union(BaseCouchDbIntegrationTest.docker_compose_services)\n\n    fixture_classes = (\n        CouchDbFixture,\n    )\n","repo_name":"alunduil/muniments","sub_path":"test_muniments/test_integration/test_external/test_couchdb/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4326,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7205261190","text":"import os\nfrom dataIntegrator.TensorFlowService import TensorFlowService\nfrom dataIntegrator.TuShareService.TuShareServiceChinaStockIndex import TuShareChinaStockIndexService\n\ndef getEnv():\n    print(\"PYTHONPATH:\", os.environ.get('PYTHONPATH'))\n    print(\"PATH:\", os.environ.get('PATH'))\n\ndef callTuShareService():\n    print(\"callTuShareService started...\")\n\n    try:\n        tuShareChinaStockIndexService = TuShareChinaStockIndexService()\n        dataFrame = tuShareChinaStockIndexService.prepareDataFrame(ts_code='000001.SZ,600000.SH',\n                                                  start_date='20000101',\n                                                  end_date='20220430')\n        jsonString = tuShareChinaStockIndexService.convertDataFrame2JSON()\n        tuShareChinaStockIndexService.saveDateFrameToDisk(\"D:\\workspace_python\\practice\\data\\df_tushare_stock_daily20220507.csv\")\n        tuShareChinaStockIndexService.saveDateToClickHouse()\n\n    except Exception as e:\n        print('Exception', e)\n\n    print(\"callTuShareService ended...\")\n\ndef main():\n    print(\"main started\")\n    callTuShareService()\n    print(\"end ended\")\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"snetlogon20/dataInegrator","sub_path":"dataIntegrator/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74660993208","text":"from flask import request, g, make_response, jsonify\nfrom flask.views import MethodView\n\nfrom server import db\nfrom server.models.feedback import Feedback\nfrom server.models.fiat_ramp import FiatRamp, FiatRampStatusEnum, Token\nfrom server.models.user import User\nfrom server.utils.auth import requires_auth\nfrom server.utils.mobile_version import check_mobile_version\nfrom server.utils.poli_payments import PoliPaymentsError, create_poli_link, get_poli_link_status\nfrom server.utils.credit_transfer import make_deposit_transfer\nfrom server.utils.transfer_enums import TransferModeEnum\n\nclass MeFeedbackAPI(MethodView):\n    @requires_auth\n    def post(self):\n        post_data = request.get_json()\n\n        rating = post_data.get('rating')\n\n        if rating is None:\n\n            response_object = {\n                'message': 'No rating provided',\n            }\n\n            return make_response(jsonify(response_object)), 400\n\n        rating = float(rating)\n\n        question = post_data.get('question', None)\n        additional_information = post_data.get('additional_information', None)\n\n        feedback = Feedback(question=question, rating=rating, additional_information=additional_information)\n\n        db.session.add(feedback)\n\n        feedback.user = g.user\n\n        db.session.commit()\n\n        response_object = {\n            'message': 'Feedback Received',\n        }\n\n        return make_response(jsonify(response_object)), 201\n\n\n# TODO: fix this\nclass ReferralAPI(MethodView):\n    @requires_auth\n    def get(self):\n\n        e = NotImplementedError('Referral has been updated and needs to be fixed!')\n\n        return make_response(jsonify(str(e))), 501\n\n        # referrals = Referral.query.filter_by(referring_user_id=g.user.id).all()\n        #\n        # response_object = {\n        #     'message': 'Referrals Loaded',\n        #     'data': {\n        #         'referrals': referrals_schema.dump(referrals).data\n        #     }\n        # }\n        #\n        # return make_response(jsonify(response_object)), 201\n\n    @requires_auth\n    def post(self):\n        e = NotImplementedError('Referral has been updated and needs to be fixed!')\n\n        return make_response(jsonify(str(e))), 501\n\n        # post_data = request.get_json()\n        #\n        # referral = Referral()\n        #\n        # referral.first_name = post_data.get('first_name')\n        # referral.last_name = post_data.get('last_name')\n        # referral.phone = post_data.get('phone')\n        # referral.reason = post_data.get('reason')\n        #\n        # referral.referring_user = g.user\n        #\n        # db.session.add(referral)\n        #\n        # db.session.commit()\n        #\n        # response_object = {\n        #     'message': 'Referral Created',\n        #     'data': {\n        #         'referral': referral_schema.dump(referral).data\n        #     }\n        # }\n\n        # return make_response(jsonify(response_object)), 201\n\n\nclass VersionAPI(MethodView):\n    def post(self):\n        post_data = request.get_json()\n        version = post_data.get('version')\n\n        if version is None:\n            response_object = {\n                'message': 'No version provided',\n            }\n\n            return make_response(jsonify(response_object)), 400\n\n        response_object = {\n            'version': version,\n            'action': check_mobile_version(version)\n        }\n\n        return make_response(jsonify(response_object)), 201\n\n\nclass PoliPaymentsAPI(MethodView):\n    @requires_auth\n    def put(self):\n        put_data = request.get_json()\n        reference = put_data.get('reference')\n\n        if reference:\n\n            fiat_ramp = FiatRamp.query.filter_by(payment_reference=reference).first()\n            if fiat_ramp is None:\n                return make_response(jsonify({'message': 'Could not find payment for reference {}'.format(reference)})), 400\n\n            poli_link_url_token = fiat_ramp.payment_metadata['poli_link_url_token']\n            try:\n                get_poli_link_status_response = get_poli_link_status(poli_link_url_token=poli_link_url_token)\n\n            except PoliPaymentsError as e:\n                response_object = {\n                    'message': str(e)\n                }\n                return make_response(jsonify(response_object)), 400\n\n            status = get_poli_link_status_response['status']\n\n            if fiat_ramp.payment_status == FiatRampStatusEnum.PENDING:\n                if status == 'Completed':\n                    # \"The full amount has been paid\"\n                    individual_recipient_user = User.query.execution_options(show_all=True).get(fiat_ramp.authorising_user_id)\n                    deposit = make_deposit_transfer(\n                        transfer_amount=fiat_ramp.payment_amount,\n                        token=fiat_ramp.token,\n                        receive_account=individual_recipient_user,\n                        transfer_mode=TransferModeEnum.MOBILE,\n                        fiat_ramp=fiat_ramp)\n\n                    deposit.resolve_as_complete_and_trigger_blockchain()\n\n                else:\n                    # The POLi link failed for some reason.\n                    # e.g. Unused, Activated, PartPaid, Future\n                    fiat_ramp.payment_status = FiatRampStatusEnum.FAILED\n                    fiat_ramp.payment_metadata = {'poli_link_url_token': poli_link_url_token, 'reason': status}\n\n            else:\n                return make_response(jsonify({'message': 'Transfer Already Updated.'})), 400\n\n            response_object = {\n                'message': 'Updated POLi Link Status',\n                'data': get_poli_link_status_response\n            }\n\n            return make_response(jsonify(response_object)), 200\n\n        else:\n            return make_response(jsonify({'message': 'Must provide POLi Link to get status'})), 400\n\n    @requires_auth\n    def post(self):\n        post_data = request.get_json()\n        amount = post_data.get('amount')\n        token_id = post_data.get('token_id')\n\n        if amount is None or token_id is None:\n            return make_response(jsonify({'message': 'Must provide amount and token_id to generate POLi Link'})), 400\n\n        token = Token.query.get(token_id)\n\n        if token is None:\n            return make_response(jsonify({'message': 'No token for ID {}'.format(token_id)})), 400\n\n        if token.symbol != 'AUD':\n            return make_response(jsonify({'message': 'POLi payments only support AUD'})), 400\n\n        fiat_ramp = FiatRamp(\n            payment_method='POLI',\n            payment_amount=int(amount),\n        )\n\n        try:\n            create_poli_link_response = create_poli_link(\n                amount=amount,\n                reference=fiat_ramp.payment_reference,\n                currency=token.symbol,\n            )\n\n        except PoliPaymentsError as e:\n            response_object = {\n                'message': str(e),\n            }\n            return make_response(jsonify(response_object)), 400\n\n        # converts \"https://poli.to/XNSBV\" into XNSBV to save in DB\n        fiat_ramp.payment_metadata = {'poli_link_url_token': create_poli_link_response['poli_link'].split('/')[-1]}\n\n        fiat_ramp.token = token\n\n        db.session.add(fiat_ramp)\n\n        response_object = {\n            'message': 'Created POLi Link',\n            'data': create_poli_link_response\n        }\n\n        return make_response(jsonify(response_object)), 201\n","repo_name":"teamsempo/SempoBlockchain","sub_path":"app/server/me_api/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":7407,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"77"}
+{"seq_id":"71060794809","text":"NORTH=\"NORTH\"\nSOUTH=\"SOUTH\"\nEAST=\"EAST\"\nWEST=\"WEST\"\n\ndirection = [NORTH, EAST, SOUTH, WEST]\n\nclass Robot(object):\n\n    def __init__(self, bearing=NORTH, EW=0, NS=0):\n        self.bearing = bearing\n        self.EW = EW\n        self.NS = NS\n        self.coordinates = (self.EW, self.NS)\n\n    def turn_right(self):\n        dir_index = direction.index(self.bearing)\n        if (dir_index == 3): \n            dir_index = 0\n            self.bearing = direction[dir_index]\n        else: \n            self.bearing = direction[dir_index+1]\n\n    def turn_left(self):\n        dir_index = direction.index(self.bearing)\n        if (dir_index == 0):\n            dir_index = 3\n            self.bearing = direction[dir_index]\n        else:\n            self.bearing = direction[dir_index-1]\n\n    def advance(self):\n        if self.bearing == NORTH:\n            self.NS += 1\n        elif self.bearing == EAST:\n            self.EW += 1\n        elif self.bearing == SOUTH:\n            self.NS -= 1\n        elif self.bearing == WEST:\n            self.EW -= 1\n        self.coordinates = (self.EW, self.NS)\n\n    def simulate(self, commands):\n        for c in commands:\n            if c == 'L':\n                self.turn_left()\n                continue\n            elif c == 'R':\n                self.turn_right()\n                continue\n            elif c == 'A':\n                self.advance()\n                continue\n            else:\n                break","repo_name":"deepbsd/exercism_python","sub_path":"robot-simulator/robot_simulator.py","file_name":"robot_simulator.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70102982968","text":"\nclass Solution(object):\n\tdef lengthOfLongestSubstring(self, s):\n\t\tMap = {}\n\t\tresult = 0\n\t\tstart = 0\n\n\n\t\tfor index in range(len(s)):\n\t\t\tif s[index] in Map and Map[s[index]] >= start:\n\t\t\t\tstart = Map[s[index]] + 1\n\t\t\telse:\n\t\t\t\tprint(result, index - start + 1)\n\t\t\t\tresult = max(result, index - start + 1)\n\t\t\t\n\t\t\tMap[s[index]] = index \n\n\n\t\treturn result\n\ns = Solution()\nstring = \"aabcdbadaacc\"\nprint(s.lengthOfLongestSubstring(string))\n\n\n","repo_name":"gauriwankhade/leetcode","sub_path":"medium/3leet.py","file_name":"3leet.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39020509888","text":"import numpy as np \nimport random\nimport cv2 as cv2\nfrom scipy.ndimage.filters import gaussian_filter\nimport scipy.ndimage.filters as nd_filters\n# from astropy.modeling.models import Gaussian2D\nfrom scipy.ndimage import gaussian_filter\nimport math\nimport matplotlib.pyplot as plt\n\n\ndef imshowBGR2RGB( im ):\n  img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n  plt.imshow(img)\n  plt.axis('off')\n  return\n\ndef local_filter(x, order):\n    x.sort()\n    return x[order]\n    \ndef ordfilt2(A, order, mask_size):\n    return nd_filters.rank_filter(A, lambda x, ord=order: local_filter(x, ord), size=(mask_size, mask_size))\n\n\ndef nonMaximumSuppression(corner, radius, threshold):\n    N = 50\n    x_start = -1.0\n    y_start= -1.0,\n\n    x = np.linspace(x_start, radius*2, N)\n    y = np.linspace(y_start, radius*2, N)\n\n    x, y = np.meshgrid(x, y)\n\n    r = np.sqrt((x - radius)**2 + (y - radius)**2)\n    inside = (r <= radius)\n\n    fig, ax = plt.subplots()\n    ax.set(xlabel='X', ylabel='Y', aspect=1.0)\n    ax.scatter(x[inside], y[inside])\n    \n    localMax = ordfilt2(corner.astype('float'), (radius**2), inside.astype('int'))\n    index = np.where(localMax > threshold and corner == localMax)\n    return index\n\ndef non_max_suppression_fast(boxes, overlapThresh):\n\t# if there are no boxes, return an empty list\n\tif len(boxes) == 0:\n\t\treturn []\n \n\t# if the bounding boxes integers, convert them to floats --\n\t# this is important since we'll be doing a bunch of divisions\n\tif boxes.dtype.kind == \"i\":\n\t\tboxes = boxes.astype(\"float\")\n \n\t# initialize the list of picked indexes\t\n\tpick = []\n \n\t# grab the coordinates of the bounding boxes\n\tx1 = boxes[:,0]\n\ty1 = boxes[:,1]\n\tx2 = boxes[:,2]\n\ty2 = boxes[:,3]\n \n\t# compute the area of the bounding boxes and sort the bounding\n\t# boxes by the bottom-right y-coordinate of the bounding box\n\tarea = (x2 - x1 + 1) * (y2 - y1 + 1)\n\tidxs = np.argsort(y2)\n \n\t# keep looping while some indexes still remain in the indexes\n\t# list\n\twhile len(idxs) > 0:\n\t\t# grab the last index in the indexes list and add the\n\t\t# index value to the list of picked indexes\n\t\tlast = len(idxs) - 1\n\t\ti = idxs[last]\n\t\tpick.append(i)\n \n\t\t# find the largest (x, y) coordinates for the start of\n\t\t# the bounding box and the smallest (x, y) coordinates\n\t\t# for the end of the bounding box\n\t\txx1 = np.maximum(x1[i], x1[idxs[:last]])\n\t\tyy1 = np.maximum(y1[i], y1[idxs[:last]])\n\t\txx2 = np.minimum(x2[i], x2[idxs[:last]])\n\t\tyy2 = np.minimum(y2[i], y2[idxs[:last]])\n \n\t\t# compute the width and height of the bounding box\n\t\tw = np.maximum(0, xx2 - xx1 + 1)\n\t\th = np.maximum(0, yy2 - yy1 + 1)\n \n\t\t# compute the ratio of overlap\n\t\toverlap = (w * h) / area[idxs[:last]]\n \n\t\t# delete all indexes from the index list that have\n\t\tidxs = np.delete(idxs, np.concatenate(([last],\n\t\t\tnp.where(overlap > overlapThresh)[0])))\n \n\t# return only the bounding boxes that were picked using the\n\t# integer data type\n\treturn boxes[pick].astype(\"int\")\n\n\n\n# In[ ]:\n\nif __name__ == '__main__':\n  img = cv2.imread('./images/building.jpg')\n\n  imshowBGR2RGB(img)\n\n\n  # In[ ]:\n\n\n  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n  blur = cv2.GaussianBlur(gray,(5,5),7)\n  Ix = cv2.Sobel(blur, cv2.CV_64F, 1, 0, ksize=5)\n  Iy = cv2.Sobel(blur, cv2.CV_64F, 0, 1, ksize=5)\n\n\n  # In[ ]:\n\n\n  plt.subplot(2,1,1), plt.imshow(Ix,cmap = 'gray')\n  plt.subplot(2,1,2), plt.imshow(Iy,cmap = 'gray')\n\n\n  # In[ ]:\n\n\n  IxIy = np.multiply(Ix, Iy)\n  Ix2 = np.multiply(Ix, Ix)\n  Iy2 = np.multiply(Iy, Iy)\n\n\n  # In[ ]:\n\n\n  Ix2_blur = cv2.GaussianBlur(Ix2,(7,7),10) \n  Iy2_blur = cv2.GaussianBlur(Iy2,(7,7),10) \n  IxIy_blur = cv2.GaussianBlur(IxIy,(7,7),10) \n\n\n  # In[ ]:\n\n\n  plt.subplot(1,3,1), plt.imshow(Ix2_blur,cmap = 'gray')\n  plt.subplot(1,3,2), plt.imshow(Iy2_blur,cmap = 'gray')\n  plt.subplot(1,3,3), plt.imshow(IxIy_blur,cmap = 'gray')\n\n\n  # In[ ]:\n\n\n  det = np.multiply(Ix2_blur, Iy2_blur) - np.multiply(IxIy_blur,IxIy_blur)\n  trace = Ix2_blur + Iy2_blur\n\n\n  # In[ ]:\n\n\n  plt.subplot(1,2,1), plt.imshow(det,cmap = 'gray')\n  plt.subplot(1,2,2), plt.imshow(trace,cmap = 'gray')\n  \n\n\n  # In[ ]:\n  np.seterr(divide='ignore', invalid='ignore')\n\n# harris method\n  R = det - 0.05 * np.multiply(trace,trace)\n    \n# brown's method\n#   R = np.divide(det,trace)\n#   plt.imshow(R, extent=[0, 1, 0, 1])\n\n#   plt.subplot(1,2,1), plt.imshow(img), plt.axis('off')\n#   plt.subplot(1,2,2), plt.imshow(R,cmap = 'gray'), plt.axis('off')\n#   plt.show()\n#   plt.savefig(\"Brown.png\")\n\n\n  #q1.b\n  \n  nonMaximumSuppression(R, 10 , 0.01)\n#   plt.show()","repo_name":"muserxu/ImageUnderstand","sub_path":"ImageUnderstanding/A2/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":4469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5767187191","text":"from config import bot,own\nfrom functools import wraps\nfrom pyrogram import enums\nfrom time import time\nfrom pyrogram.enums import ChatMemberStatus\n\nadmins_in_chat = {}\n\nasync def list_admin(chat_id):\n  global admins_in_chat\n  if chat_id in admins_in_chat:\n    interval = time() - admins_in_chat[chat_id][\"last_updated_at\"]\n    if interval < 3600:\n      return admins_in_chat[chat_id][\"data\"]\n\n    admins_in_chat[chat_id] = {\n        \"last_updated_at\": time(),\n        \"data\": [member.user.id async for member in bot.get_chat_members(chat_id, filter=enums.ChatMembersFilter.ADMINISTRATORS)],\n    }\n  return admins_in_chat[chat_id][\"data\"]\n\n\n\n\nasync def member_permissions(chat_id, user_id):\n  perms = []\n  try:\n    member = await bot.get_chat_member(chat_id, user_id)\n    perijinan = member.privileges\n  except Exception:\n    return []\n  if member.status != enums.ChatMemberStatus.MEMBER:\n    if perijinan.can_post_messages:\n      perms.append(\"can_post_messages\")\n    if perijinan.can_edit_messages:\n      perms.append(\"can_edit_messages\")\n    if perijinan.can_delete_messages:\n      perms.append(\"can_delete_messages\")\n    if perijinan.can_restrict_members:\n      perms.append(\"can_restrict_members\")\n    if perijinan.can_promote_members:\n      perms.append(\"can_promote_members\")\n    if perijinan.can_change_info:\n      perms.append(\"can_change_info\")\n    if perijinan.can_invite_users:\n      perms.append(\"can_invite_users\")\n    if perijinan.can_pin_messages:\n      perms.append(\"can_pin_messages\")\n    if perijinan.can_manage_video_chats:\n      perms.append(\"can_manage_video_chats\")\n  return perms \n\n\ndef izin(permission):\n  def subFunc(func):\n    @wraps(func)\n    async def subFunc2(client, message, *args, **kwargs):\n      chatID = message.chat.id\n      if not message.from_user:\n        # For anonymous admins\n        if message.sender_chat and message.sender_chat.id == message.chat.id:\n          return await message.reply_text(f\"Anda tidak memiliki izin yang diperlukan untuk melakukan tindakan ini.\\n**Izin:** {permission}\")\n      # For admins and sudo users\n      userID = message.from_user.id\n      permissions = await member_permissions(chatID, userID)\n      if userID not in own and permission not in permissions:\n        return await message.reply_text(f\"Anda tidak memiliki izin yang diperlukan untuk melakukan tindakan ini.\\n**Izin:** {permission}\")\n  \n      return await func(client, message, *args, **kwargs)\n    return subFunc2\n\n  return subFunc\n","repo_name":"LucasID26/Asta-Robot","sub_path":"Asta/decorators/permission.py","file_name":"permission.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71523033530","text":"# Antonio Claudio Teixeira Alves\n\n# Faça um Programa que receba uma lista de 5\n# números inteiros e mostre-os.\n\n\nlist = []\nprint(\"Adicione cinco valores a seguir\")\n\nfor i in range(0, 5, 1):\n    list.append(int(input(f\"Valor 0{i+1}: \")))\n\nprint(list)\n","repo_name":"kladDev/poo-python","sub_path":"lista05/lista.py","file_name":"lista.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13897701358","text":"import cards\nimport moneybag\nimport validationint\n\ndef mainMenu():\n\n    # storage\n    quitProgram = False\n    choice = 0\n    money = ''\n    while quitProgram == False:\n        \n        # main menu loop\n        showMenu()\n        print(\"\\n\\nChoose an option.\")\n        #Validates the choices, to force only 1-4\n        choice = validationint.getRangedInt(\"Your choice:\", \"Enter 1-4 only.\", 1, 4)\n        if choice == 1:\n            # have to read or create the moneybag\n            money = moneybag.createMoneyBag()\n            print('You have ' + str(money) + ' dollars to bet.' )\n            # assign a variable to call actualBet and to use as a parameter\n            # than passes to make a bet and goes into the game menu\n            if money != -1:\n                storedMoney = moneybag.actualBet()\n                # prevents typing invalid values\n                # also gets the money to store for the win/loss\n                blackJackMenu(storedMoney)\n            else:\n                print('Please create a moneybag to use the program.')\n            # essentially the same exact thing as option 1, but does not move them to game menu\n        elif choice == 2:\n            money = moneybag.createMoneyBag()\n            # me using -1 is just to handle errors since the moneybag cannot be -1\n            if money != -1:\n                print('You have ' + str(money) + ' dollars to bet.' )\n        # jumps to purchasing\n        elif choice == 3:\n            theShop = moneybag.purchaseStuff()\n        # quits the program entirely   \n        elif choice == 4:\n            quitProgram = True\n            print(\"\\nThanks for playing!\")\n            print (\"\\nCome back again!\")\n\ndef showMenu():\n    print(\"\\nMain Menu\\n\\n\")\n    print(\"\\t1. Bet!\")\n    print(\"\\t2. Money bag!\")\n    print(\"\\t3. The template store!\")\n    print(\"\\t4. Quit Program\")\n\ndef blackJackMenu(storedMoney):\n    # storage\n    quitProgram = False\n    choice = 0\n    \n    while quitProgram == False:\n\n        # game menu loop\n        showMenuBlackjack()\n        print(\"\\n\\nChoose an option.\")\n        choice = validationint.getRangedInt(\"Your choice:\", \"Enter 1-2 only.\", 1, 2)\n        # begins the game\n        if choice == 1:\n            cards.processingGame(storedMoney)\n            # causes user to return to original menu to bet again.\n            break\n            # only moves user to original main menu.\n        elif choice == 2:\n            quitProgram = True\n            \n    print(\"\\nBack to Main Menu.\")\n\ndef showMenuBlackjack():\n    print(\"\\nBlackjack\\n\\n\")\n    print(\"\\t1. Let's Play!\")\n    print(\"\\t2. Go back to Main Menu\")\n\nmainMenu()\n","repo_name":"Rpena308/PythonBlackJack","sub_path":"blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14796314695","text":"#!/usr/bin/env python3\n\nimport testUtils\n\nimport argparse\nimport signal\nfrom collections import namedtuple\nimport os\nimport shutil\n\n###############################################################\n# Test for validating consensus based block production. We introduce malicious producers which\n#  reject all transactions.\n# We have three test scenarios:\n#  - No malicious producers. Transactions should be incorporated into the chain.\n#  - Minority malicious producers (less than a third producer count). Transactions will get incorporated\n# into the chain as majority appoves the transactions.\n#  - Majority malicious producer count (greater than a third producer count). Transactions won't get\n# incorporated into the chain as majority rejects the transactions.\n###############################################################\n\n\nPrint=testUtils.Utils.Print\nerrorExit=Utils.errorExit\n\nStagedNodeInfo=namedtuple(\"StagedNodeInfo\", \"config logging\")\n\n\nlogging00=\"\"\"{\n  \"includes\": [],\n  \"appenders\": [{\n      \"name\": \"stderr\",\n      \"type\": \"console\",\n      \"args\": {\n        \"stream\": \"std_error\",\n        \"level_colors\": [{\n            \"level\": \"debug\",\n            \"color\": \"green\"\n          },{\n            \"level\": \"warn\",\n            \"color\": \"brown\"\n          },{\n            \"level\": \"error\",\n            \"color\": \"red\"\n          }\n        ]\n      },\n      \"enabled\": true\n    },{\n      \"name\": \"stdout\",\n      \"type\": \"console\",\n      \"args\": {\n        \"stream\": \"std_out\",\n        \"level_colors\": [{\n            \"level\": \"debug\",\n            \"color\": \"green\"\n          },{\n            \"level\": \"warn\",\n            \"color\": \"brown\"\n          },{\n            \"level\": \"error\",\n            \"color\": \"red\"\n          }\n        ]\n      },\n      \"enabled\": true\n    },{\n      \"name\": \"net\",\n      \"type\": \"gelf\",\n      \"args\": {\n        \"endpoint\": \"10.160.11.21:12201\",\n        \"host\": \"testnet_00\"\n      },\n      \"enabled\": true\n    }\n  ],\n  \"loggers\": [{\n      \"name\": \"default\",\n      \"level\": \"debug\",\n      \"enabled\": true,\n      \"additivity\": false,\n      \"appenders\": [\n        \"stderr\",\n        \"net\"\n      ]\n    }\n  ]\n}\"\"\"\n\nconfig00=\"\"\"genesis-json = ./genesis.json\nblock-log-dir = blocks\nreadonly = 0\nsend-whole-blocks = true\nshared-file-dir = blockchain\nshared-file-size = 8192\nhttp-server-address = 127.0.0.1:8888\np2p-listen-endpoint = 0.0.0.0:9876\np2p-server-address = localhost:9876\nallowed-connection = any\np2p-peer-address = localhost:9877\nrequired-participation = true\nprivate-key = [\"EOS6MRyAjQq8ud7hVNYcfnVPJqcVpscN5So8BhtHuGYqET5GDW5CV\",\"5KQwrPbwdL6PhXujxW37FSSQZ1JiwsST4cqQzDeyXtP79zkvFD3\"]\nproducer-name = initu\nplugin = eosio::producer_plugin\nplugin = eosio::chain_api_plugin\nplugin = eosio::history_plugin\nplugin = eosio::history_api_plugin\"\"\"\n\n\nconfig01=\"\"\"genesis-json = ./genesis.json\nblock-log-dir = blocks\nreadonly = 0\nsend-whole-blocks = true\nshared-file-dir = blockchain\nshared-file-size = 8192\nhttp-server-address = 127.0.0.1:8889\np2p-listen-endpoint = 0.0.0.0:9877\np2p-server-address = localhost:9877\nallowed-connection = any\np2p-peer-address = localhost:9876\nrequired-participation = true\nprivate-key = [\"EOS6MRyAjQq8ud7hVNYcfnVPJqcVpscN5So8BhtHuGYqET5GDW5CV\",\"5KQwrPbwdL6PhXujxW37FSSQZ1JiwsST4cqQzDeyXtP79zkvFD3\"]\nproducer-name = defproducerb\nplugin = eosio::producer_plugin\nplugin = eosio::chain_api_plugin\nplugin = eosio::history_plugin\nplugin = eosio::history_api_plugin\"\"\"\n\n\nproducers=\"\"\"producer-name = defproducerd\nproducer-name = defproducerf\nproducer-name = defproducerh\nproducer-name = defproducerj\nproducer-name = defproducerl\nproducer-name = defproducern\nproducer-name = defproducerp\nproducer-name = defproducerr\nproducer-name = defproducert\nproducer-name = defproducera\nproducer-name = defproducerc\nproducer-name = defproducere\nproducer-name = defproducerg\nproducer-name = defproduceri\nproducer-name = defproducerk\nproducer-name = defproducerm\nproducer-name = defproducero\nproducer-name = defproducerq\nproducer-name = defproducers\"\"\"\n\nzeroExecTime=\"trans-execution-time = 0\"\n\ndef getNoMaliciousStagedNodesInfo():\n    stagedNodesInfo=[]\n    myConfig00=config00\n    stagedNodesInfo.append(StagedNodeInfo(myConfig00, logging00))\n    myConfig01=config01+\"\\n\"+producers\n    stagedNodesInfo.append(StagedNodeInfo(myConfig01, logging00))\n    return stagedNodesInfo\n\ndef getMinorityMaliciousProducerStagedNodesInfo():\n    stagedNodesInfo=[]\n    myConfig00=config00+\"\\n\"+producers\n    stagedNodesInfo.append(StagedNodeInfo(myConfig00, logging00))\n    myConfig01=config01+\"\\n\"+zeroExecTime\n    stagedNodesInfo.append(StagedNodeInfo(myConfig01, logging00))\n    return stagedNodesInfo\n\ndef getMajorityMaliciousProducerStagedNodesInfo():\n    stagedNodesInfo=[]\n    myConfig00=config00\n    stagedNodesInfo.append(StagedNodeInfo(myConfig00, logging00))\n    myConfig01=config01+\"\\n\"+producers+\"\\n\"+zeroExecTime\n    stagedNodesInfo.append(StagedNodeInfo(myConfig01, logging00))\n    return stagedNodesInfo\n\nstagingDir=\"staging\"\ndef stageScenario(stagedNodeInfos):\n    assert(stagedNodeInfos != None)\n    assert(len(stagedNodeInfos) > 1)\n\n    os.makedirs(stagingDir)\n    count=0\n    for stagedNodeInfo in stagedNodeInfos:\n        configPath=os.path.join(stagingDir, \"etc/eosio/node_%02d\" % (count))\n        os.makedirs(configPath)\n        with open(os.path.join(configPath, \"config.ini\"), \"w\") as textFile:\n            print(stagedNodeInfo.config,file=textFile)\n        with open(os.path.join(configPath, \"logging.json\"), \"w\") as textFile:\n            print(stagedNodeInfo.logging,file=textFile)\n        count += 1\n    return\n\ndef cleanStaging():\n    os.path.exists(stagingDir) and shutil.rmtree(stagingDir)\n\ndef error(msg=\"\", errorCode=1):\n    Print(\"ERROR:\", msg)\n\nparser = argparse.ArgumentParser()\ntests=[1,2,3]\n\nparser.add_argument(\"-t\", \"--tests\", type=str, help=\"1|2|3 1=run no malicious producers test, 2=minority malicious, 3=majority malicious.\", default=None)\nparser.add_argument(\"-w\", type=int, help=\"system wait time\", default=testUtils.Utils.systemWaitTimeout)\nparser.add_argument(\"-v\", help=\"verbose logging\", action='store_true')\nparser.add_argument(\"--dump-error-details\",\n                    help=\"Upon error print etc/eosio/node_*/config.ini and var/lib/node_*/stderr.log to stdout\",\n                    action='store_true')\nparser.add_argument(\"--keep-logs\", help=\"Don't delete var/lib/node_* folders upon test completion\",\n                    action='store_true')\nparser.add_argument(\"--not-noon\", help=\"This is not the Noon branch.\", action='store_true')\nparser.add_argument(\"--dont-kill\", help=\"Leave cluster running after test finishes\", action='store_true')\n\nargs = parser.parse_args()\ntestsArg=args.tests\ndebug=args.v\nwaitTimeout=args.w\ndumpErrorDetails=args.dump-error-details\nkeepLogs=args.keep-logs\namINoon=not args.not_noon\nkillEosInstances= not args.dont-kill\nkillWallet= not args.dont-kill\n\ntestUtils.Utils.Debug=debug\n\nassert (testsArg is None or testsArg == \"1\" or testsArg == \"2\" or testsArg == \"3\")\nif testsArg is not None:\n    tests=[int(testsArg)]\n\ntestUtils.Utils.setSystemWaitTimeout(waitTimeout)\ntestUtils.Utils.iAmNotNoon()\n\ndef myTest(transWillEnterBlock):\n    testSuccessful=False\n\n    cluster=testUtils.Cluster(walletd=True, staging=True)\n    walletMgr=testUtils.WalletMgr(True)\n\n    try:\n        cluster.killall()\n        cluster.cleanup()\n        walletMgr.killall()\n        walletMgr.cleanup()\n\n        pnodes=2\n        total_nodes=pnodes\n        topo=\"mesh\"\n        delay=0\n        Print(\"Stand up cluster\")\n        if cluster.launch(pnodes=pnodes, totalNodes=total_nodes, topo=topo, delay=delay) is False:\n            error(\"Failed to stand up eos cluster.\")\n            return False\n\n        accounts=testUtils.Cluster.createAccountKeys(1)\n        if accounts is None:\n            error(\"FAILURE - create keys\")\n            return False\n        currencyAccount=accounts[0]\n        currencyAccount.name=\"currency0000\"\n\n        testWalletName=\"test\"\n        Print(\"Creating wallet \\\"%s\\\".\" % (testWalletName))\n        testWallet=walletMgr.create(testWalletName)\n\n        for account in accounts:\n            Print(\"Importing keys for account %s into wallet %s.\" % (account.name, testWallet.name))\n            if not walletMgr.importKey(account, testWallet):\n                error(\"Failed to import key for account %s\" % (account.name))\n                return False\n\n        node=cluster.getNode(0)\n        node2=cluster.getNode(1)\n\n        defproduceraAccount=testUtils.Cluster.defproduceraAccount\n\n        Print(\"Importing keys for account %s into wallet %s.\" % (defproduceraAccount.name, testWallet.name))\n        if not walletMgr.importKey(defproduceraAccount, testWallet):\n            error(\"Failed to import key for account %s\" % (defproduceraAccount.name))\n            return False\n\n        Print(\"Create new account %s via %s\" % (currencyAccount.name, defproduceraAccount.name))\n        transId=node.createAccount(currencyAccount, defproduceraAccount, stakedDeposit=5000, waitForTransBlock=True)\n        if transId is None:\n            error(\"Failed to create account %s\" % (currencyAccount.name))\n            return False\n\n        wasmFile=\"currency.wasm\"\n        abiFile=\"currency.abi\"\n        Print(\"Publish contract\")\n        trans=node.publishContract(currencyAccount, wasmFile, abiFile, waitForTransBlock=True)\n        if trans is None:\n            error(\"Failed to publish contract.\")\n            return False\n\n        Print(\"push transfer action to currency0000 contract\")\n        contract=\"currency0000\"\n        action=\"transfer\"\n        data=\"{\\\"from\\\":\\\"currency0000\\\",\\\"to\\\":\\\"defproducera\\\",\\\"quantity\\\":\"\n        if amINoon:\n            data +=\"\\\"00.0050 CUR\\\",\\\"memo\\\":\\\"test\\\"}\"\n        else:\n            data +=\"50}\"\n        opts=\"--permission currency0000@active\"\n        if not amINoon:\n            opts += \" --scope currency0000,defproducera\"\n\n        trans=node.pushMessage(contract, action, data, opts, silentErrors=True)\n        transInBlock=False\n        if not trans[0]:\n            # On slower systems e.g Travis the transaction rejection can happen immediately\n            #  We want to handle fast and slow failures.\n            if \"allocated processing time was exceeded\" in trans[1]:\n                Print(\"Push message transaction immediately failed.\")\n            else:\n                error(\"Exception in push message. %s\" % (trans[1]))\n                return False\n\n        else:\n            transId=testUtils.Node.getTransId(trans[1])\n\n            Print(\"verify transaction exists\")\n            if not node2.waitForTransInBlock(transId):\n                error(\"Transaction never made it to node2\")\n                return False\n\n            Print(\"Get details for transaction %s\" % (transId))\n            transaction=node2.getTransaction(transId, exitOnError=True)\n            signature=transaction[\"transaction\"][\"signatures\"][0]\n\n            blockNum=int(transaction[\"transaction\"][\"ref_block_num\"])\n            blockNum += 1\n            Print(\"Our transaction is in block %d\" % (blockNum))\n\n            block=node2.getBlock(blockNum, exitOnError=True)\n            cycles=block[\"cycles\"]\n            if len(cycles) > 0:\n                blockTransSignature=cycles[0][0][\"user_input\"][0][\"signatures\"][0]\n                # Print(\"Transaction signature: %s\\nBlock transaction signature: %s\" %\n                #       (signature, blockTransSignature))\n                transInBlock=(signature == blockTransSignature)\n\n        if transWillEnterBlock:\n            if not transInBlock:\n                error(\"Transaction did not enter the chain.\")\n                return False\n            else:\n                Print(\"SUCCESS: Transaction1 entered in the chain.\")\n        elif not transWillEnterBlock:\n            if transInBlock:\n                error(\"Transaction entered the chain.\")\n                return False\n            else:\n                Print(\"SUCCESS: Transaction2 did not enter the chain.\")\n\n        testSuccessful=True\n    finally:\n        if not testSuccessful and dumpErrorDetails:\n            cluster.dumpErrorDetails()\n            walletMgr.dumpErrorDetails()\n            Print(\"== Errors see above ==\")\n\n        if killEosInstances:\n            Print(\"Shut down the cluster%s\" % (\" and cleanup.\" if (testSuccessful and not keepLogs) else \".\"))\n            cluster.killall()\n            walletMgr.killall()\n            if testSuccessful and not keepLogs:\n                Print(\"Cleanup cluster and wallet data.\")\n                cluster.cleanup()\n                walletMgr.cleanup()\n\n    return True\n\n\ntry:\n    if 1 in tests:\n        Print(\"Cluster with no malicious producers. All producers expected to approve transaction. Hence transaction is expected to enter the chain.\")\n        cleanStaging()\n        stageScenario(getNoMaliciousStagedNodesInfo())\n        if not myTest(True):\n            exit(1)\n\n    if 2 in tests:\n        Print(\"\\nCluster with minority(1) malicious nodes. Majority producers expected to approve transaction. Hence transaction is expected to enter the chain.\")\n        cleanStaging()\n        stageScenario(getMinorityMaliciousProducerStagedNodesInfo())\n        if not myTest(True):\n            exit(1)\n\n    if 3 in tests:\n        Print(\"\\nCluster with majority(20) malicious nodes. Majority producers expected to block transaction. Hence transaction is not expected to enter the chain.\")\n        cleanStaging()\n        stageScenario(getMajorityMaliciousProducerStagedNodesInfo())\n        if not myTest(False):\n            exit(1)\n\nfinally:\n    cleanStaging()\n\nexit(0)\n","repo_name":"EOSIO/eos","sub_path":"tests/consensus-validation-malicious-producers.py","file_name":"consensus-validation-malicious-producers.py","file_ext":"py","file_size_in_byte":13524,"program_lang":"python","lang":"en","doc_type":"code","stars":11300,"dataset":"github-code","pt":"77"}
+{"seq_id":"8264029093","text":"from flask import Flask, request, render_template\nimport pymysql\n\ndb = pymysql.connect(\"ec2-54-200-1-210.us-west-2.compute.amazonaws.com\", \"rosie\", \"root\", \"insight\", use_unicode=True)\n\napp = Flask(__name__)\n#api = Api(app)\n\n@app.route('/', methods=['GET', 'POST'])\ndef connectDB():\n    if request.method =='POST':\n        userId = request.form['userId']\n        cursor = db.cursor()\n        \n        # recommend results\n        result_statement = \"SELECT title FROM result JOIN mapping WHERE result.movieId = mapping.movieId AND userId = %s\"\n        cursor.execute(result_statement,(userId, ))\n        results = cursor.fetchall()\n        print(results)\n\n        # Watch History\n        history_statement = \"SELECT title, rating FROM history JOIN mapping WHERE history.movieId = mapping.movieId AND userId = %s ORDER BY rating DESC LIMIT 10\"\n        cursor.execute(history_statement,(userId, ))\n        history = cursor.fetchall()\n        print(history)\n\n        return render_template('dashboard.html', results=results, history=history, userId=userId)\n    return render_template('index.html')\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True,host='0.0.0.0',port =5000)\n","repo_name":"xiaruolei/Insight-Project","sub_path":"Flask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25410532899","text":"import requests\nfrom bs4 import BeautifulSoup\n\n# 2、定义 url 和请求头参数\nurl = 'https://www.autohome.com.cn/news/'\nheaders = {\n    \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36\",\n    }\n\n\n\n\n# 定义url\nurls = []\nfor i in range(6):\n    url = f'https://www.autohome.com.cn/news/{i}/#liststart'\n    urls.append(url)\n\n\nnews = []\n# 遍历所有url\nfor url in urls:\n    try:\n        # 发送请求\n        response = requests.get(url, headers=headers)\n        content = response.content.decode('gbk')\n        # print(content)\n\n        # 4、实例化 BeautifulSoup 对象（中介）\n        soup = BeautifulSoup(content, 'lxml')\n        # print(soup)\n        \n        # 5、获取数据\n        divs = soup.find_all('div', class_='article-wrapper')\n        # print(divs)\n        for div in divs:\n            titles = div.find_all('h3')\n            times = div.find_all('span', class_=\"fn-left\")\n            profiles = div.find_all('p')\n\n            for title,time,profile in zip(titles, times, profiles):\n                title = title.string\n                time = time.string\n                profile =profile.string\n\n                car_news = {\n                    '标题': title,\n                    '时间': time,\n                    '文章': profile, \n                }\n\n                news.append(car_news)\n    except:\n        continue\n\nprint(news)\n\n# 6、存储数据\n\n","repo_name":"HuangKunHong/Python_Advanced","sub_path":"01_爬虫/py_04_bs4爬虫实战2（汽车之家）.py","file_name":"py_04_bs4爬虫实战2（汽车之家）.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20981310642","text":"# Создайте программу для игры с конфетами человек против человека.\n# Условие игры: На столе лежит 117 конфета. Играют два игрока делая ход друг после друга. \n# Первый ход определяется жеребьёвкой. За один ход можно забрать не более чем 28 конфет. \n# Все конфеты оппонента достаются сделавшему последний ход.\n\nimport random\nname1 = input(\"Введите имя первого игрока: \")\nname2 = input(\"Введите имя второго игрока: \")\n\nigrok = random.randrange(1,3)\na = int(input(\"Введите количество конфет: \"))\nif igrok == 1:\n    print(\"Первый ход делает: \", name1)\nelse:\n    print(\"Первый ход делает: \", name2)\n\nwhile a > 0:\n    if a > 28:\n        igrok1 = int(input(\"Ты можешь взять не больше 28 конфет, сколько конфет ты взял? \"))\n        a = a - igrok1\n        print(\"Конфет осталось\", a)\n        if a > 28:\n            igrok2 = int(input(\"Ты можешь взять не больше 28 конфет, сколько конфет ты взял? \"))\n            a = a - igrok2\n            print(\"Конфет осталось\", a)\n            if a <= 28:\n                if igrok == 1:\n                    print(\"Осталось \", a, \"шт, их забирает\", name1, \"а значит победил\", name1)\n                else:\n                    print(\"Осталось \", a, \"шт, их забирает\", name2, \"а значит победил\", name2)\n        else:\n            if igrok == 1:\n                print(\"Осталось \", a, \"шт, их забирает\", name2, \"а значит победил\", name2)\n            else:\n                print(\"Осталось \", a, \"шт, их забирает\", name1, \"а значит победил\", name1)","repo_name":"plstq/python-lesson-1","sub_path":"Python/domashka 5/task 1.py","file_name":"task 1.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29284291612","text":"import argparse\nimport glob\nimport logging\nimport numpy as np\nimport os\nimport sys\nimport time\nimport torch\nfrom models.graph2seq_series_rel import Graph2SeqSeriesRel\nfrom models.seq2seq import Seq2Seq\nfrom torch.utils.data import DataLoader\nfrom utils import parsing\nfrom utils.data_utils import canonicalize_smiles, load_vocab, S2SDataset, G2SDataset\nfrom utils.train_utils import log_tensor, param_count, set_seed, setup_logger\n\n\ndef get_predict_parser():\n    parser = argparse.ArgumentParser(\"predict\")\n    parsing.add_common_args(parser)\n    parsing.add_preprocess_args(parser)\n    parsing.add_train_args(parser)\n    parsing.add_predict_args(parser)\n\n    return parser\n\n\ndef main(args):\n    start = time.time()\n    parsing.log_args(args)\n\n    os.makedirs(os.path.join(\"./results\", args.data_name), exist_ok=True)\n\n    # initialization ----------------- model\n    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n    checkpoints = glob.glob(os.path.join(args.load_from, \"*.pt\"))\n    checkpoints = sorted(\n        checkpoints,\n        key=lambda ckpt: int(ckpt.split(\".\")[-2].split(\"_\")[-1]),\n        reverse=True\n    )\n    checkpoints = [ckpt for ckpt in checkpoints\n                   if (args.checkpoint_step_start <= int(ckpt.split(\".\")[-2].split(\"_\")[0]))\n                   and (args.checkpoint_step_end >= int(ckpt.split(\".\")[-2].split(\"_\")[0]))]\n\n    model = None\n    val_dataset = None\n    vocab_tokens = None\n    smis_tgt = []\n    for ckpt_i, checkpoint in enumerate(checkpoints):\n        logging.info(f\"Loading from {checkpoint}\")\n        state = torch.load(checkpoint)\n\n        pretrain_args = state[\"args\"]\n        pretrain_state_dict = state[\"state_dict\"]\n\n        if model is None:\n            # initialization ----------------- model\n            logging.info(f\"Model is None, building model\")\n            logging.info(f\"First logging args for training\")\n            parsing.log_args(pretrain_args)\n\n            for attr in [\"mpn_type\", \"rel_pos\"]:\n                try:\n                    getattr(pretrain_args, attr)\n                except AttributeError:\n                    setattr(pretrain_args, attr, getattr(args, attr))\n\n            assert args.model == pretrain_args.model, f\"Pretrained model is {pretrain_args.model}!\"\n            if args.model == \"s2s\":\n                model_class = Seq2Seq\n                dataset_class = S2SDataset\n            elif args.model == \"g2s_series_rel\":\n                model_class = Graph2SeqSeriesRel\n                dataset_class = G2SDataset\n                args.compute_graph_distance = True\n                assert args.compute_graph_distance\n            else:\n                raise ValueError(f\"Model {args.model} not supported!\")\n\n            # initialization ----------------- vocab\n            vocab = load_vocab(pretrain_args.vocab_file)\n            vocab_tokens = [k for k, v in sorted(vocab.items(), key=lambda tup: tup[1])]\n\n            model = model_class(pretrain_args, vocab)\n            logging.info(model)\n            logging.info(f\"Number of parameters = {param_count(model)}\")\n\n            # initialization ----------------- data\n            val_dataset = dataset_class(pretrain_args, file=args.valid_bin)\n            val_dataset.batch(\n                batch_type=args.batch_type,\n                batch_size=args.predict_batch_size\n            )\n            with open(args.val_tgt, \"r\") as f:\n                total = sum(1 for _ in f)\n\n            with open(args.val_tgt, \"r\") as f:\n                for line_tgt in f:\n                    smi_tgt = \"\".join(line_tgt.split())\n                    smi_tgt = canonicalize_smiles(smi_tgt)\n                    smis_tgt.append(smi_tgt)\n\n        model.load_state_dict(pretrain_state_dict)\n        logging.info(f\"Loaded pretrained state_dict from {checkpoint}\")\n\n        model.to(device)\n        model.eval()\n\n        val_loader = DataLoader(\n            dataset=val_dataset,\n            batch_size=1,\n            shuffle=False,\n            collate_fn=lambda _batch: _batch[0],\n            pin_memory=True\n        )\n\n        # prediction\n        all_predictions = []\n        with torch.no_grad():\n            for val_idx, val_batch in enumerate(val_loader):\n                if val_idx % args.log_iter == 0:\n                    logging.info(f\"Doing inference on val step {val_idx}, time: {time.time() - start: .2f} s\")\n                    sys.stdout.flush()\n\n                val_batch.to(device)\n                results = model.predict_step(\n                    reaction_batch=val_batch,\n                    batch_size=val_batch.size,\n                    beam_size=args.beam_size,\n                    n_best=args.n_best,\n                    temperature=args.temperature,\n                    min_length=args.predict_min_len,\n                    max_length=args.predict_max_len\n                )\n\n                for predictions in results[\"predictions\"]:\n                    smis = []\n                    for prediction in predictions:\n                        predicted_idx = prediction.detach().cpu().numpy()\n                        predicted_tokens = [vocab_tokens[idx] for idx in predicted_idx[:-1]]\n                        smi = \" \".join(predicted_tokens)\n                        smis.append(smi)\n                    smis = \",\".join(smis)\n                    all_predictions.append(f\"{smis}\\n\")\n\n        # saving prediction results\n        result_file = f\"{args.result_file}.{ckpt_i}\"\n        result_stat_file = f\"{args.result_file}.stat.{ckpt_i}\"\n        with open(result_file, \"w\") as of:\n            of.writelines(all_predictions)\n\n        # scoring\n        invalid = 0\n        accuracies = np.zeros([total, args.n_best], dtype=np.float32)\n\n        with open(result_file, \"r\") as f_predict:\n            for i, (smi_tgt, line_predict) in enumerate(zip(smis_tgt, f_predict)):\n                if smi_tgt == \"CC\":         # problematic SMILES\n                    continue\n\n                # smi_predict = \"\".join(line_predict.split())\n                line_predict = \"\".join(line_predict.split())\n                smis_predict = line_predict.split(\",\")\n                smis_predict = [canonicalize_smiles(smi, trim=False, suppress_warning=True) for smi in smis_predict]\n                if not smis_predict[0]:\n                    invalid += 1\n                smis_predict = [smi for smi in smis_predict if smi and not smi == \"CC\"]\n\n                for j, smi in enumerate(smis_predict):\n                    if smi == smi_tgt:\n                        accuracies[i, j:] = 1.0\n                        break\n\n        with open(result_stat_file, \"w\") as of:\n            line = f\"Total: {total}, top 1 invalid: {invalid / total * 100: .2f} %\"\n            logging.info(line)\n            of.write(f\"{line}\\n\")\n\n            mean_accuracies = np.mean(accuracies, axis=0)\n            for n in range(args.n_best):\n                line = f\"Top {n+1} accuracy: {mean_accuracies[n] * 100: .2f} %\"\n                logging.info(line)\n                of.write(f\"{line}\\n\")\n\n\nif __name__ == \"__main__\":\n    predict_parser = get_predict_parser()\n    args = predict_parser.parse_args()\n\n    # set random seed (just in case)\n    set_seed(args.seed)\n\n    # logger setup\n    logger = setup_logger(args, warning_off=True)\n\n    torch.set_printoptions(profile=\"full\")\n    main(args)\n","repo_name":"coleygroup/Graph2SMILES","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":7310,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"77"}
+{"seq_id":"18234234286","text":"#!/usr/bin/env python\n\n\nimport os\nimport sys\nimport optparse\nimport time\nimport datetime\n\nif 'SUMO_HOME' in os.environ:\n     tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n     sys.path.append(tools)\n     print(\"SUMO_HOME detected!\")\nelse:\n     sys.exit(\"please declare environment variable 'SUMO_HOME'\")\n\n\nimport traci\nimport simpla\nimport _utils2\nimport _platoonmanager2\nfrom xml.etree import ElementTree\n\n# add command line option for not using gui interface (faster simulation runs)\ndef get_options():\n    opt_parser = optparse.OptionParser()\n    opt_parser.add_option(\"--nogui\", action=\"store_true\", default=False, help=\"run the command line version of SUMO\")\n    options, args = opt_parser.parse_args()\n    return options\n\n# TraCI control loop\ndef run():\n    step = 0\n\n    # todo Write VMT + status info to file\n    #  walk through pvehicles for each time step\n    #  time step | pvehicle | vehicle type | getDistance\n    #  make xml\n    #  <time step>\n    #      <pevehicle id | type | distance />\n    #  </time step>\n\n    now = datetime.datetime.now()\n    log = now.strftime(\"%Y-%m-%d-%H-%M-%S\") + \"platoon_status.xml\"\n    file = '/home/thomasrw/Desktop/' + log\n\n    #root = ElementTree.fromstring('<platoon_status>\\n</platoon_status>')\n    #tree = ElementTree.ElementTree(root)\n    #tree.write(file)\n\n    logfile = open(file, 'w')\n    logfile.write('<platoon_status>\\n')\n\n    tree = ''\n\n    #while step < 41000:\n    while traci.simulation.getMinExpectedNumber() > 0:\n        #if step == 100:\n            #simpla._utils.openGap(\"flow20.0\", 1, 1, 1, 1)\n            #sr = _utils2.SizeRestrictor(mgr,3,60)\n            #listenerID = traci.addStepListener(sr)\n\n        #tree = None\n        #if tree == None:\n        #    print(\"reading file\")\n        #    tree = ElementTree.parse(file)\n        #    print(\"read file complete\")\n        #    root = tree.getroot()\n        #timestep = ElementTree.SubElement(root, 'time')\n        #timestep.set('value', str(step))\n        #timestep.tail = \"\\n\"\n\n        tree += '<time value=\"' + str(step) + '\">\\n'\n\n\n        for veh_id in traci.vehicle.getIDList():\n            distance = traci.vehicle.getDistance(veh_id)\n            type = traci.vehicle.getTypeID(veh_id)\n\n            #status = ElementTree.SubElement(timestep, 'vehicle')\n            #status.set('id', str(veh_id))\n            #status.set('distance', str(distance))\n            #status.set('type', str(type))\n            #status.tail=\"\\n\"\n\n            tree += '<vehicle id=\"'+str(veh_id)+'\" distance=\"'+str(distance)+'\" type=\"'+str(type)+'\"/>\\n'\n\n            #print(veh_id, distance, type)\n\n        tree += '</time>\\n'\n        if step % 10000 == 0:  #write to file every 10,000 steps\n            logfile.write(tree)\n            tree = ''\n\n        traci.simulationStep()\n        print(step)\n        step += 1\n\n    if tree == '':\n        pass #last step was a write step\n    else:\n        logfile.write(tree) #write final entry from last set of steps\n\n    logfile.write('</platoon_status>')\n    logfile.close()\n    traci.close()\n    sys.stdout.flush()\n\n\n\n\n\n\n# main entry point\nif __name__ == \"__main__\":\n    options = get_options()\n    if options.nogui:\n        sumoBinary = \"/usr/share/sumo/bin/sumo\"\n    else:\n        sumoBinary = \"/usr/share/sumo/bin/sumo-gui\"\n\n    # passing the \"--start\" option to tell sumo-gui to begin without waiting for the play button to be pressed\n    sumoCmd = [sumoBinary, \"-c\", \"/home/thomasrw/j/jconfig\", \"--start\"]\n    simplaConfig = \"/home/thomasrw/j/mysimpla.cfg.xml\"\n    #simplaConfig2 = \"/home/thomasrw/Model/mysimpla2.cfg.xml\"\n\n    traci.start(sumoCmd)\n    #simpla.load(simplaConfig)\n\n    #calling config.load() and platoonmanger() explicitly exposes the platoon manager variable allowing listeners\n    #  to be specified by platoon manager\n\n    simpla._config.load(simplaConfig)\n    ##mgr = simpla._platoonmanager.PlatoonManager()\n    mgr = _platoonmanager2.PlatoonManager2()\n    mgr_id = traci.addStepListener(mgr)\n\n    #print(str(mgr.getMaxSize()))\n    #mgr.setMaxSize(50)\n    #print(str(mgr.getMaxSize()))\n\n    #simpla.load(simplaConfig2)\n\n    start = time.time()\n    run()\n    end = time.time()\n    total = end - start\n    print(\"time taken is: \" + str(total))\n","repo_name":"thomasrw/TrafficSim","sub_path":"smallMap.py","file_name":"smallMap.py","file_ext":"py","file_size_in_byte":4213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27238038129","text":"class index:\n    user_id: int\n    count: int\n\n    def __init__(self, user_id, count):\n        self.user_id = user_id\n        self.count = count\n\n    def __str__(self):\n        return f\"(uid = {self.user_id}, count = {self.count})\"\n\n    def __repr__(self):\n        # return f\"(uid = {self.user_id}, count = {self.count})\"\n        return f\"({self.user_id},{self.count})\"\n\n    def __eq__(self, oth):\n        return (self.user_id == oth.user_id) and (self.count == oth.count)\n\n    def __hash__(self):\n        return 0\n\n    def isgreater(in1, in2):\n        if in1.count == in2.count:\n            return in1.user_id > in2.user_id\n        return in1.count > in2.count\n\n\nclass add_element:\n    data: str\n    #before: index\n    after: index\n\n    def __init__(self, data, after):\n        self.data = data\n        #self.before = before\n        self.after = after\n\n\nclass crdt:\n    doc: dict[index, [str, bool]]\n    users_count: dict[int, int]      # user_id -> [count]\n    users_name: dict[int, str]  # user_id -> [name]\n    order: list[index]\n    uid: int\n    docStr: str\n    adds: list[(str, index, index)]\n    deletes: list[index]\n\n    def __init__(self, uid: int, name: str):\n        id1 = index(0, 0)\n        self.uid = uid\n        self.doc = {id1: ['\\0', False]}  # indicates start of doc\n        # count of uid is set as 0 as +1 present in 'self_next_index'\n        self.users_count = {0: 0, uid: 0}\n        self.users_name = {0: 'root', uid: name}\n        self.order = [id1]\n        self.docStr = \"\"\n        self.adds = []\n        self.deletes = []\n\n    def add_user(self, id: int, name: str):\n        # add check to make sure user not already present\n        self.users_name[id] = name\n        self.users_count[id] = self.users_count[self.uid]\n\n    def get_index(self, in2):\n        for i in range(len(self.order)):\n            in1 = self.order[i]\n            if(in1.count == in2.count) and (in1.user_id == in2.user_id):\n                return i\n        raise RuntimeError(f'Trying to add after inexistent: {in2}')\n\n    def add_element(self, element):\n        print(element)\n\n        data: str = element[0]\n        current: index = element[1]\n        add_after: index = element[2]\n\n        for user in self.users_count.keys():  # notice in diagram it b starts from 4\n            if(current.count > self.users_count[user]):\n                self.users_count[user] = current.count\n\n        print(f\"CHANGING {current} with {data}\")\n        self.doc[current] = [data, True]\n\n        a_pos = self.get_index(add_after) + 1\n        while(a_pos < len(self.order) and index.isgreater(self.order[a_pos], current)):\n            a_pos += 1\n\n        if current in self.order:\n            self.order.remove(current)     # added to debug\n        self.order.insert(a_pos, current)\n        #print(f\"Succesfully added {data} at {current} after {add_after}\")\n\n    def self_next_index(self):\n        self.users_count[self.uid] += 1\n        id_t = index(self.uid, self.users_count[self.uid])\n        return id_t\n\n    def delete_element(self, current: index):\n        self.doc[current][1] = False\n\n    def print_crdt(self):\n        print(f\"DOC : {self.doc}\\n ORDER: {self.order}\")\n        for ind in self.order:\n            val = self.doc[ind]\n            if(val[1]):\n                print(val[0])\n\n    def make_text(self):\n        text = \"\"\n        for ind in self.order:\n            val = self.doc[ind]\n            if(val[1]):\n                text += val[0]\n        return text\n\n\nif __name__ == '__main__':\n    # Create 2 users\n    userA = crdt(1, 'A')\n    userB = crdt(2, 'B')\n    userA.add_user(2, 'B')\n    userB.add_user(1, 'A')\n    adds_A = []\n    adds_B = []\n\n    # add 'a'\n    add_after = index(0, 0)\n    data = 'a'\n    current = userA.self_next_index()\n    add = (data, current, add_after)\n    adds_A.append(add)\n\n    # add 'b'\n    add_after = index(1, 1)\n    data = 'b'\n    current = userA.self_next_index()\n    add = (data, current, add_after)\n    adds_A.append(add)\n\n    # add append of A to both\n    for add in adds_A:\n        userA.add_element(add)\n\n    for add in adds_A:\n        userB.add_element(add)\n    adds_A = []\n\n    # add 'x'\n    add_after = index(1, 1)\n    data = 'x'\n    current = userA.self_next_index()\n    add = (data, current, add_after)\n    adds_A.append(add)\n\n    # add 'p'\n    add_after = index(1, 1)\n    data = 'p'\n    current = userB.self_next_index()\n    add = (data, current, add_after)\n    adds_B.append(add)\n\n    # add append of A to both\n    for add in adds_A:\n        userA.add_element(add)\n\n    for add in adds_A:\n        userB.add_element(add)\n    adds_A = []\n\n    # add append of B to both\n    for add in adds_B:\n        userA.add_element(add)\n\n    for add in adds_B:\n        userB.add_element(add)\n    adds_A = []\n\n    print('\\n USER B:')\n    userB.print_crdt()\n    print('\\n USER A:')\n    userA.print_crdt()\n","repo_name":"Abhinandan-Pal/sixth-projects","sub_path":"Computer Network/crdt.py","file_name":"crdt.py","file_ext":"py","file_size_in_byte":4828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1827631016","text":"## pull forecast\ndef get_forecast():\n    '''gets weather forecast and returns df with which to predict'''\n\n    import requests\n    import json\n    import pickle\n    from datetime import datetime, timedelta\n    import pandas as pd\n    from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n    import streamlit as st\n    import numpy as np\n\n    today = datetime.today()\n    start_date = today + timedelta(days=1)\n    start_date = start_date.strftime(\"%Y-%m-%d\")\n    end_date = today + timedelta(days=2)\n    end_date = end_date.strftime(\"%Y-%m-%d\")\n\n    url = f'https://api.open-meteo.com/v1/forecast?latitude=40.7761&longitude=-73.8727&hourly=temperature_2m,apparent_temperature,precipitation,windspeed_10m,direct_radiation,is_day&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch&timezone=America%2FNew_York&start_date={start_date}&end_date={end_date}'\n    r = requests.get(url)\n    d = json.loads(r.text)\n\n    wthr = pd.DataFrame({\n        'date':d['hourly']['time'],\n        'is_day':d['hourly']['is_day'],\n        'rad':d['hourly']['direct_radiation'],\n        'prcp':d['hourly']['precipitation'],\n        'temp':d['hourly']['temperature_2m'],\n        'real_feel':d['hourly']['apparent_temperature'],\n        'wind':d['hourly']['windspeed_10m']\n        }).dropna()\n\n    wthr['tmin'] = wthr['temp']\n    wthr['tmax'] = wthr['temp']\n    wthr['day_precip'] = wthr['is_day'] * wthr['prcp']\n    wthr['day_real_feel'] = wthr['is_day'] * wthr['real_feel']\n    wthr['day_wind'] = wthr['is_day'] * wthr['wind']\n    wthr.loc[wthr['is_day']==0., 'day_real_feel'] = np.nan\n    wthr.loc[wthr['is_day']==0., 'day_wind'] = np.nan\n    wthr['date'] = pd.to_datetime(wthr['date'])\n    wthr = wthr[['date','prcp', 'tmax', 'tmin', 'rad', 'day_precip', 'day_real_feel',\n        'day_wind']]\n    \n    wthr = wthr.groupby(wthr['date'].dt.date).agg({\n    'tmax':'max',\n    'tmin':'min',\n    'rad':'sum',\n    'prcp':'sum',\n    'day_precip':'sum',\n    'day_real_feel':'mean',\n    'day_wind':'mean'\n    }).reset_index()\n    wthr['prev_count'] = 17297 # use average of dataset\n\n    # day of week\n    wthr['date'] = pd.to_datetime(wthr['date'])\n    wthr['year'] = wthr['date'].dt.year\n    wthr['month'] = wthr['date'].dt.month\n    wthr['date'] = pd.to_datetime(wthr['date'])\n    wthr['dow'] = wthr['date'].dt.dayofweek\n    wthr['dom'] = wthr['date'].dt.day\n\n    # holidays\n    cal = calendar()\n    holidays = cal.holidays(start=wthr['date'].min(), end=wthr['date'].max())\n    wthr['hol'] = wthr['date'].isin(holidays)\n\n    for col in wthr.columns:\n        if (col=='date') or (col=='hol'):\n            continue\n        else:\n            wthr[col] = pd.to_numeric(wthr[col])\n            wthr[col] = wthr[col].round(1)\n\n    wthr = wthr[['date', 'prcp', 'tmax', 'tmin', 'rad', 'day_precip', 'day_real_feel', 'day_wind', 'year', 'month', 'dow', 'dom', 'hol', 'prev_count']].copy()\n    return wthr\n\ndef predict_biking():\n    '''takes weather forecast and predicts ridership'''\n    import pickle\n    import pandas as pd\n    import streamlit as st\n    from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n\n    #import model\n    with open('model/xgb_v1.pkl', 'rb') as f:\n        model = pickle.load(f)\n\n    df = pd.DataFrame(index=[0])\n\n    for k, v in st.session_state.items():\n        if k=='pred':\n            continue\n        elif k=='date':\n            df[k] = v\n        elif k=='hol':\n            df[k] = bool(v)\n        elif k=='prev_count':\n            st.session_state['last_count'] = v\n            df[k] = float(v)\n        else:\n            df[k] = float(v)\n\n    \n    df['date'] = pd.to_datetime(df['date'])\n    df['year'] = df['date'].dt.year\n    df['month'] = df['date'].dt.month\n    df['dow'] = df['date'].dt.dayofweek\n    df['dom'] = df['date'].dt.day\n    # holidays\n    cal = calendar()\n    holidays = cal.holidays(start=df['date'].min(), end=df['date'].max())\n    df['hol'] = df['date'].isin(holidays)\n\n    df = df[['prcp', 'tmax', 'tmin', 'rad', 'day_precip', 'day_real_feel', 'day_wind', 'year', 'month', 'dow', 'dom', 'hol', 'prev_count']]\n    pred = model.predict(df)[0]\n    st.session_state['pred'] = pred\n","repo_name":"nliusont/nyc-bike-prediction","sub_path":"app_funcs.py","file_name":"app_funcs.py","file_ext":"py","file_size_in_byte":4149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74067521208","text":"from functionaltest import FunctionalTest\n\n\nclass Test_2770_ClearDependentCellErrors(FunctionalTest):\n\n    def test_dependent_cell_errors_are_cleared(self):\n        # * Harold logs in to Dirigible and creates a new sheet\n        self.login_and_create_new_sheet()\n\n        # * He enters \"=1/0\" into A1\n        self.enter_cell_text(1, 1, '=1/0')\n        self.wait_for_spinner_to_stop()\n        # * He enters \"=A1 + 1\" into A2\n        self.enter_cell_text(1, 2, '=A1 + 1')\n        self.wait_for_spinner_to_stop()\n        # * He enters \"=A2 + 1\" into A3\n        self.enter_cell_text(1, 3, '=A2 + 1')\n        self.wait_for_spinner_to_stop()\n\n        # * He confirms that both A1 and A2 have errors\n        self.assert_cell_has_error(1, 1, 'ZeroDivisionError: division by zero')\n        self.assert_cell_has_error(\n            1, 2,\n            \"TypeError: unsupported operand type(s) for +: 'Undefined' and 'int'\"\n        )\n        self.assert_cell_has_error(\n            1, 3,\n            \"TypeError: unsupported operand type(s) for +: 'Undefined' and 'int'\"\n        )\n\n        # * He changes A1 to \"=1\"\n        self.enter_cell_text(1, 1, '=1')\n\n        # * A1's error will clear.\n        self.wait_for_cell_value(1, 1, '1')\n        # * A2's should too\n        self.wait_for_cell_value(1, 2, '2')\n        # * and A3's should too\n        self.wait_for_cell_value(1, 3, '3')\n\n","repo_name":"pythonanywhere/dirigible-spreadsheet","sub_path":"dirigible/fts/tests/test_2770_ClearDependentCellErrors.py","file_name":"test_2770_ClearDependentCellErrors.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":206,"dataset":"github-code","pt":"77"}
+{"seq_id":"72416974969","text":"from typing import TYPE_CHECKING, List, Optional\n\nif TYPE_CHECKING:\n    from pygame.event import Event\n\n    from hcraft.env import HcraftEnv\n\n\ndef get_human_action(\n    env: \"HcraftEnv\",\n    additional_events: List[\"Event\"] = None,\n    can_be_none: bool = False,\n    fps: Optional[float] = None,\n):\n    \"\"\"Update the environment rendering and gather potential action given by the UI.\n\n    Args:\n        env: The running HierarchyCraft environment.\n        additional_events (Optional): Additional simulated pygame events.\n        can_be_none: If False, this function will loop on rendering until an action is found.\n            If True, will return None if no action was found after one rendering update.\n\n    Returns:\n        The action found using the UI.\n\n    \"\"\"\n    action_chosen = False\n    while not action_chosen:\n        action = env.render_window.update_rendering(additional_events, fps)\n        action_chosen = action is not None or can_be_none\n    return action\n\n\ndef render_env_with_human(env: \"HcraftEnv\", n_episodes: int = 1):\n    \"\"\"Render the given environment with human iteractions.\n\n    Args:\n        env (HcraftEnv): The HierarchyCraft environment to run.\n        n_episodes (int, optional): Number of episodes to run. Defaults to 1.\n    \"\"\"\n    print(\"Purpose: \", env.purpose)\n\n    for _ in range(n_episodes):\n        env.reset()\n        done = False\n        total_reward = 0\n        while not done:\n            env.render()\n            action = get_human_action(env)\n            print(f\"Human did: {env.world.transformations[action]}\")\n\n            _observation, reward, done, _info = env.step(action)\n            total_reward += reward\n\n        print(\"SCORE: \", total_reward)\n","repo_name":"IRLL/HierarchyCraft","sub_path":"src/hcraft/render/human.py","file_name":"human.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"44811237966","text":"#!/usr/bin/env python3\n\n\nfrom datetime import date\nfrom faker import Faker\nimport random\nimport sqlite3\n\nfake = Faker()\n\ndef create_db(con, cur):\n    cur.executescript(\"\"\"\n-- Table: subjects\nDROP TABLE IF EXISTS subjects;\nCREATE TABLE subjects (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    title VARCHAR(255) UNIQUE NOT NULL\n);\n\n-- Table: groups\nDROP TABLE IF EXISTS groups;\nCREATE TABLE groups (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    codename VARCHAR(255) UNIQUE NOT NULL\n);\n\n-- Table: teachers\nDROP TABLE IF EXISTS teachers;\nCREATE TABLE teachers (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    fullname VARCHAR(255) UNIQUE NOT NULL\n);\n\n-- Table: students\nDROP TABLE IF EXISTS students;\nCREATE TABLE students (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    fullname VARCHAR(255) UNIQUE NOT NULL,\n    group_id INTEGER,\n    FOREIGN KEY (group_id) REFERENCES groups (id)\n        ON DELETE SET NULL\n        ON UPDATE CASCADE\n);\n\n-- Table: subject per student\nDROP TABLE IF EXISTS student_subjects;\nCREATE TABLE student_subjects (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    student_id INTEGER NOT NULL,\n    subject_id INTEGER NOT NULL,\n    FOREIGN KEY (student_id) REFERENCES students (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE,\n    FOREIGN KEY (subject_id) REFERENCES subjects (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE\n);\n\n-- Table: subject per teacher\nDROP TABLE IF EXISTS teacher_subjects;\nCREATE TABLE teacher_subjects (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    teacher_id INTEGER NOT NULL,\n    subject_id INTEGER NOT NULL,\n    FOREIGN KEY (teacher_id) REFERENCES teachers (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE,\n    FOREIGN KEY (subject_id) REFERENCES subjects (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE\n);\n\n-- Table: grades\nDROP TABLE IF EXISTS grades;\nCREATE TABLE grades (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    date_of DATE NOT NULL,\n    grade TINYINT NOT NULL,\n    student_id INTEGER NOT NULL,\n    subject_id INTEGER NOT NULL,\n    teacher_id INTEGER NOT NULL,\n    FOREIGN KEY (student_id) REFERENCES students (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE,\n    FOREIGN KEY (subject_id) REFERENCES subjects (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE,\n    FOREIGN KEY (teacher_id) REFERENCES teachers (id)\n        ON DELETE CASCADE\n        ON UPDATE CASCADE\n);\"\"\")\n\ndef fake_fill_db(con, cur):\n    fake_subjects = [\n        \"Mathematical psychotherapy\",\n        \"Quantum astrology\",\n        \"Meteorology of infinity\",\n        \"Archeology of logic\",\n        # \"Introduction to hypercubic epicycles\",\n        \"Differential geometry of programming\",\n        \"Infinite structures calculus\",\n        # \"The theory of complex integration of fractals\",\n        \"Mirror calculations\",\n        # \"Calculation of infinite cycles in finite time\",\n        # \"The topology of the unknown\",\n        \"Absurdology\"\n    ]\n    random.shuffle(fake_subjects)\n\n    fake_groups = [\n        \"GOIT-31\",\n        \"GOIT-32\",\n        \"GOIT-33\"\n    ]\n    random.shuffle(fake_groups)\n\n    fake_teachers = [f\"{random.choice(['Dr.','Phd.'])} {fake.first_name()} {fake.last_name()}\" for _ in range(5)]\n\n    num_students = random.randint(30, 50)\n    fake_students = [(f\"{fake.last_name()}, {fake.first_name()}\", random.randint(1,len(fake_groups))) for _ in range(num_students) ]\n\n    cur = con.cursor()\n\n    #{{{ Table: subjects -----------------------------------\n    cur.executemany(\"INSERT INTO subjects (title) VALUES (?)\", zip(fake_subjects))\n    #}}}\n\n    #{{{ Table: groups -------------------------------------\n    cur.executemany(\"INSERT INTO groups (codename) VALUES (?)\", zip(fake_groups))\n    #}}}\n\n    #{{{ Table: teachers -----------------------------------\n    cur.executemany(\"INSERT INTO teachers (fullname) VALUES (?)\", zip(fake_teachers))\n    #}}}\n\n    #{{{ Table: teacher_subjects ---------------------------\n    subjects1 = list(range(1,len(fake_subjects)+1))\n    subjects2 = []\n    for _ in range(len(fake_subjects) - len(fake_teachers)):\n        n = random.randint(0, len(subjects1)-1)\n        subjects2.append(subjects1.pop(n))\n    teacher_subjects = list(zip(subjects1, range(1,len(fake_teachers)+1)))\n    teacher_subjects.extend(\n            list(zip(subjects2,\n                random.choices(range(1,len(fake_teachers)+1),k=len(subjects2)))))\n    cur.executemany(\"INSERT INTO teacher_subjects (subject_id, teacher_id) VALUES (?,?)\",\n            teacher_subjects)\n    #}}}\n\n    #{{{ Table: students -----------------------------------\n    cur.executemany(\"INSERT INTO students (fullname,group_id) VALUES (?,?)\", fake_students)\n    #}}}\n\n    #{{{ Table: student_subjects ---------------------------\n    # Each student must listen any 5 subjects\n    SUBJECTS_PER_STUDENT = 5\n    student_subjects = []\n    for i in range(1, num_students+1):\n        subjects1 = list(range(1,len(fake_subjects)+1))\n        for _ in range(len(fake_subjects) - SUBJECTS_PER_STUDENT):\n            subjects1.pop(random.randint(0, len(subjects1)-1))\n        student_subjects.extend(zip(subjects1, [i] * SUBJECTS_PER_STUDENT))\n    cur.executemany(\"INSERT INTO student_subjects (subject_id, student_id) VALUES (?,?)\",\n            student_subjects)\n    #}}}\n\n    #{{{ Table: grades -------------------------------------\n    STADY_DAYS = 30 * 3\n    ord_today = date.today().toordinal()\n    grades = []\n    for student_id in range(1, num_students+1):\n        for _ in range(random.randint(1,20)):\n            # date_of\n            while True:\n                date_of = date.fromordinal(ord_today - random.randint(0,STADY_DAYS-1))\n                if date_of.weekday() < 5:\n                    break # not Saturday and not Sunday\n            # subject_id\n            subject_id = student_subjects[\n                student_id * SUBJECTS_PER_STUDENT - random.randint(1,SUBJECTS_PER_STUDENT)][0]\n            # real life grades distribution\n            grade = random.randint(1,100)\n            if grade < 10:\n                grade = 2\n            elif grade < 25:\n                grade = 3\n            elif grade < 65:\n                grade = 4\n            else:\n                grade = 5\n            # what teacher who can conduct lecture on this subject\n            teacher_ids = [t_id for s_id, t_id in teacher_subjects if s_id == subject_id ]\n            teacher_id = random.choice(teacher_ids)\n            grades.append((date_of, grade, student_id, subject_id, teacher_id))\n    cur.executemany(\n        \"INSERT INTO grades (date_of,grade,student_id,subject_id,teacher_id) VALUES (?,?,?,?,?)\",\n        grades)\n    #}}}\n\n    con.commit()\n\nif __name__ == \"__main__\":\n\n    with sqlite3.connect('uni.db') as con:\n        cur = con.cursor()\n        create_db(con, cur)\n        fake_fill_db(con, cur)\n","repo_name":"RoyBebru/sqlite-unidb","sub_path":"uni-main.py","file_name":"uni-main.py","file_ext":"py","file_size_in_byte":6770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42347257101","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul  7 08:07:49 2023\n\n@author: vishuddhimakeshwaran\n\"\"\"\nfrom tkinter import *\nimport random\n\nroot = Tk()\nroot.title(\"Random Cities and Countries\")\nroot.geometry(\"500x600\")\n\nlistCountry = []\nlistCity = []\n\nentry1 = Entry(root)\nentry1.place(relx = 0.2, rely = 0.2, anchor = CENTER)\nentry2 = Entry(root)\nentry2.place(relx = 0.8, rely = 0.2, anchor = CENTER)\n\nlabelCountries = Label(root)\nlabelCountries.place(relx = 0.5, rely = 0.4, anchor = CENTER)\n\nlabelCities = Label(root)\nlabelCities.place(relx = 0.5, rely = 0.5, anchor = CENTER)\n\nlabelAns1 = Label(root)\nlabelAns1.place(relx = 0.2, rely = 0.7, anchor = CENTER)\n\nlabelAns2 = Label(root)\nlabelAns2.place(relx = 0.8, rely = 0.7, anchor = CENTER)\n\ndef add(): \n    listCountry.append(entry1.get())\n    entry1.delete(0, END)\n    listCity.append(entry2.get())\n    entry2.delete(0, END)\n    labelCountries[\"text\"] = \"List of countries: \" + str(listCountry)\n    labelCities[\"text\"] = \"List of cities: \" + str(listCity)\n    \n    \nbutton1 = Button(root, text = \"Add country and city\", command = add)\nbutton1.place(relx = 0.5, rely = 0.3, anchor = CENTER)\n\ndef gen(): \n    random1 = random.randint(0, len(listCountry) - 1)\n    random2 = random.randint(0, len(listCity) - 1)\n    labelAns1[\"text\"] = \"Random Country: \" + str(listCountry[random1])\n    labelAns2[\"text\"] = \"Random City: \" + str(listCity[random2])\n    \n    \nbutton2 = Button(root, text = \"Generate random country and city\", command = gen)\nbutton2.place(relx = 0.5, rely = 0.6, anchor = CENTER)\n\nroot.mainloop()\n","repo_name":"Vishuddhi431/Project150","sub_path":"RandomCountryandCities.py","file_name":"RandomCountryandCities.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26572540201","text":"# -*- coding: utf-8 -*-\n\n\"\"\" Fast EER computation \"\"\"\n\n__author__ = \"Gaël Le Lan\"\n__copyright__ = \"Copyright 2020, Gaël Le Lan\"\n__license__ = \"Apache 2.0\"\n\nimport numpy as np\n\n\ndef precise_eer(negative_scores, positive_scores):\n    \"\"\"Linear complexity EER computation\n\n    Args:\n        negative_scores (numpy array): impostor scores\n        positive_scores (numpy array): genuine scores\n\n    Returns:\n        float: Equal Error Rate (EER)\n    \"\"\"\n\n    neg_labels = -np.ones(negative_scores.shape, dtype=np.int)\n    pos_labels = np.ones(positive_scores.shape, dtype=np.int)\n\n    scores = np.vstack((negative_scores.reshape(-1, 1), positive_scores.reshape(-1, 1))).flatten()\n    t_nt = np.vstack((neg_labels.reshape(-1, 1), pos_labels.reshape(-1, 1))).flatten()\n\n    sort_indices = np.argsort(-scores)\n    sorted_scores = scores[sort_indices]\n    sorted_t_nt = t_nt[sort_indices]\n\n    t_count = np.sum(t_nt > 0)\n    nt_count = np.sum(t_nt < 0)\n\n    m_count = t_count\n    fa_count = 0\n    eer = 100\n    far_frr_predicate = +np.Inf\n    eer_threshold = 0\n\n    last_score = -np.Inf\n    m_count_increment = 0\n    fa_count_increment = 0\n\n    jdx = 0\n    for idx, score in enumerate(sorted_scores):\n        if score != last_score:\n            m_count += m_count_increment\n            fa_count += fa_count_increment\n            m_count_increment = 0\n            fa_count_increment = 0\n        # target\n        candidate_predicate = abs((m_count) / t_count - fa_count / nt_count)\n        candidate_predicate = abs(score - 0.015)\n        if sorted_t_nt[idx] == 1:\n            if candidate_predicate <= far_frr_predicate:\n                far_frr_predicate = candidate_predicate\n                eer_threshold = score\n                eer = ((m_count) / t_count + fa_count / nt_count)/2\n                fnr = (m_count) / t_count\n                fpr = fa_count / nt_count\n            else:\n                break\n            m_count_increment -= 1\n            jdx += 1\n        # non-target\n        elif sorted_t_nt[idx] == -1:\n            if candidate_predicate <= far_frr_predicate:\n                far_frr_predicate = candidate_predicate\n                eer_threshold = score\n                eer = ((m_count) / t_count + fa_count / nt_count)/2\n                fnr = (m_count) / t_count\n                fpr = fa_count / nt_count\n            else:\n                break\n            fa_count_increment += 1\n            jdx += 1\n        last_score = score\n    print(fnr, fpr, eer_threshold)\n    return eer\n\ndef eer(negatives, positives):\n    \"\"\"Logarithmic complexity EER computation\n\n    Args:\n        negative_scores (numpy array): impostor scores\n        positive_scores (numpy array): genuine scores\n\n    Returns:\n        float: Equal Error Rate (EER)\n    \"\"\"\n\n    positives = np.sort(positives)\n    negatives = np.sort(negatives)[::-1]\n\n    pos_count = positives.shape[0]\n    neg_count = negatives.shape[0]\n\n    p_score = positives[0]\n    n_score = negatives[0]\n\n    p_index = 0\n    n_index = 0\n\n    next_p_jump = pos_count//2\n    next_n_jump = neg_count//2\n\n    kdx = 0\n    while True:\n        kdx += 1\n        if p_index < 0 or n_index < 0:\n            return 0\n        if p_index > pos_count or n_index > neg_count:\n            return 100\n        if p_score < n_score:\n            p_index = p_index + next_p_jump\n            n_index = n_index + next_n_jump\n            if next_p_jump == 0 and next_n_jump == 0:\n                break\n        elif p_score >= n_score:\n            p_index = p_index - next_p_jump\n            n_index = n_index - next_n_jump\n            if next_p_jump == 0 and next_n_jump == 0:\n                break\n                \n        p_score = positives[p_index]\n        n_score = negatives[n_index]\n        next_p_jump = next_p_jump//2\n        next_n_jump = next_n_jump//2\n\n    eer_predicate = 100\n\n    tfr = (abs(p_index))/pos_count\n    tfa = (1+abs(n_index))/neg_count\n    if (p_score == n_score and tfr == tfa):\n        return tfr\n\n    while positives[p_index] < negatives[n_index]:\n        if p_index < pos_count - 1:\n            p_index += 1\n        elif n_index < neg_count - 1:\n            n_index += 1\n        else:\n            break\n\n    while positives[p_index] > negatives[n_index] and n_index >= 1:\n        n_index -= 1\n\n    tfr = (1+p_index)/pos_count\n    tfa = (1+n_index)/neg_count\n\n    while tfa > tfr:\n        p_index += 1\n        while positives[p_index] > negatives[n_index] and n_index >= 1:\n            n_index -= 1\n        tfr = (1+p_index)/pos_count\n        tfa = (1+n_index)/neg_count\n\n    if abs(tfr - tfa) <= eer_predicate:\n        eer_predicate = abs(tfr - tfa)\n        eer = (tfr + tfa) / 2\n    else:\n        return eer\n\n    tfr = p_index/pos_count\n    tfa = (1+n_index)/neg_count\n    if abs(tfr - tfa) <= eer_predicate:\n        eer_predicate = abs(tfr - tfa)\n        eer = (tfr + tfa) / 2\n    else:\n        return eer\n\n    while True:\n        while negatives[n_index + 1] <= positives[p_index - 1]:\n            p_index -= 1\n            tfr = p_index/pos_count\n            tfa = (1+n_index)/neg_count\n            if abs(tfr - tfa) <= eer_predicate:\n                eer_predicate = abs(tfr - tfa)\n                eer = (tfr + tfa) / 2\n            else:\n                return eer\n        while negatives[n_index + 1] > positives[p_index - 1]:\n            n_index += 1\n            tfr = p_index/pos_count\n            tfa = (1+n_index)/neg_count\n            if abs(tfr - tfa) <= eer_predicate:\n                eer_predicate = abs(tfr - tfa)\n                eer = (tfr + tfa) / 2\n            else:\n                return eer\n\n    return eer\n","repo_name":"gl3lan/fast_eer","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5579,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"1994534756","text":"import os.path as osp\nimport tempfile\nfrom unittest import TestCase\n\nimport torch\nfrom torch import Tensor\n\nfrom mmengine.evaluator import DumpResults\nfrom mmengine.fileio import load\n\n\nclass TestDumpResults(TestCase):\n\n    def test_init(self):\n        with self.assertRaisesRegex(ValueError,\n                                    'The output file must be a pkl file.'):\n            DumpResults(out_file_path='./results.json')\n\n        # collect_dir could only be configured when collect_device='cpu'\n        with self.assertRaises(ValueError):\n            DumpResults(\n                out_file_path='./results.json',\n                collect_device='gpu',\n                collect_dir='./tmp')\n\n    def test_process(self):\n        metric = DumpResults(out_file_path='./results.pkl')\n        data_samples = [dict(data=(Tensor([1, 2, 3]), Tensor([4, 5, 6])))]\n        metric.process(None, data_samples)\n        self.assertEqual(len(metric.results), 1)\n        self.assertEqual(metric.results[0]['data'][0].device,\n                         torch.device('cpu'))\n\n    def test_compute_metrics(self):\n        temp_dir = tempfile.TemporaryDirectory()\n        path = osp.join(temp_dir.name, 'results.pkl')\n        metric = DumpResults(out_file_path=path)\n        data_samples = [dict(data=(Tensor([1, 2, 3]), Tensor([4, 5, 6])))]\n        metric.process(None, data_samples)\n        metric.compute_metrics(metric.results)\n        self.assertTrue(osp.isfile(path))\n\n        results = load(path)\n        self.assertEqual(len(results), 1)\n        self.assertEqual(results[0]['data'][0].device, torch.device('cpu'))\n\n        temp_dir.cleanup()\n","repo_name":"open-mmlab/mmengine","sub_path":"tests/test_evaluator/test_metric.py","file_name":"test_metric.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":853,"dataset":"github-code","pt":"77"}
+{"seq_id":"18943482379","text":"import signal\n\nimport kk.events.caps as caps\nimport kk.events.alphanum as alphanum\nimport kk.ckb as ckb\n\ndef main():\n    global ckbObj\n\n    ckbObj = ckb.CKB(\"ffffff\")\n    capsObj = caps.Caps(ckbObj)\n    keyObj = alphanum.AlphaNum(ckbObj)\n\n    for line in ckbObj.notifier:\n        if line.strip(\"\\n\") in \" \".join(keyObj.events):\n            keyObj.action(line)\n        elif line.strip(\"\\n\") in \" \".join(capsObj.events):\n            capsObj.action(line)\n\ndef cleanup(signum, frame):\n    ckbObj.reset()\n\nif __name__ == '__main__':\n    signal.signal(signal.SIGTERM, cleanup)\n    signal.signal(signal.SIGINT, cleanup)\n\n    main()\n","repo_name":"obihann/killer-keyboard","sub_path":"killer-keyboard.py","file_name":"killer-keyboard.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"72396125370","text":"# dsname: Datastream name to pull data from\n# dsname2: Secondary datastream(s) to include and merge\n# t_delta: Script will resample data to 1 min.  If gaps are longer set this appropriately\n# workers: Set to 1 in the case of radar data so it doesn't crash the system\nconf = {\n    'site': 'anx', # Site code\n    'facility': 'S2', # Facility code\n    'start_date': '2019-12-01', # Campaign start date for plot to span\n    'end_date': '2020-05-31', # Campaign end date\n    'outname': '/home/theisen/www/afc_summary/anx_s2.png', # Where to save.  Options are png, pdf, etc\n    'chart_style': 'linear', # Which chart linear or 2D\n    'info_style': 'simple', # simple will only display instrument name and datastream;\n    'dqr_table': False, # True or False to add a table of DQRs at the end\n    'doi_table': False, # True will make a table of DOIs, False and info_style not simple will add to plots\n    'instruments':{\n        # arm_inst_class: {'dsname': datastream name without site,\n        #                  'dsname2': second datastream to include on same plot\n        #                  't_delta': if expected sampling interval is not 1 minute set to minutes\n        #                  'data_path': where to get data if not /data/archive on adc systems\n        #                  'workers': if it is a large dataset like radars, set to 1 otherwise will\n        #                             default to dask processing}\n        'ceil': {'dsname': 'ceil10mS2.b1'},\n        'dl': {'dsname': 'dlfptS2.b1', 'dsname2': 'dlppiS2.b1'},\n        'ecor': {'dsname': '30ecorS2.b1', 't_delta': 30},\n        'ldis': {'dsname': 'ldS2.b1'},\n        'met': {'dsname': 'metwxtS2.b1'},\n        'mpl': {'dsname': 'mplpolfsS2.b1'},\n        'mwr': {'dsname': 'mwrlosS2.b1'},\n        'tsi': {'dsname': 'tsiskycoverS2.b1'}\n    }\n}\n","repo_name":"ARM-Development/AFC_Summary","sub_path":"conf/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26651623624","text":"def maxSub_brute(arr):\n\n\t# running record of max sum and indexes\n\tmax = None\n\tmax_s = []\n\n\t# for every possible starting index i\n\tfor i in range(len(arr)):\n\t\tsub = 0\n\n\t\t# for every possible ending index after i\n\t\tfor j in range(len(arr)-i):\n\t\t\tsub += arr[i+j]\n\n\t\t\t# if current sum larger than recorded, update\n\t\t\tif max is None or sub > max:\n\t\t\t\tmax = sub\n\t\t\t\tmax_s = arr[i:i+j+1]\n\treturn(max_s)\n\ndef maxSub_linear(arr):\n\t# keep track of overall max \n\tmaxSum_ovr = float('-inf')\n\tmaxArr_ovr = []\n\n\t# keep track of max ending in last index\n\tmaxSum_end = 0\n\tmaxArr_end = []\n\n\t# for each i in arr\n\tfor i in range(len(arr)):\n\t\t# update max ending in last index\n\t\t\n\t\t# max ending at last index is either:\n\t\t# current index (only)\n\t\tmaxSum_end += arr[i]\n\n\t\tif arr[i] > maxSum_end:\n\t\t\tmaxSum_end = arr[i]\n\t\t\tmaxArr_end = [arr[i]]\n\n\t\t# or past max + current\n\t\telse:\n\t\t\tmaxArr_end.append(arr[i])\n\t\t\n\t\t# if larger than overall max, update\n\t\tif maxSum_end > maxSum_ovr:\n\t\t\tmaxSum_ovr = maxSum_end\n\t\t\tmaxArr_ovr = maxArr_end\n\n\t# checking edge case: empty array\n\tif maxSum_ovr == float('-inf'):\n\t\tmaxSum_ovr = 0\n\n\n\treturn(maxSum_ovr, maxArr_ovr)\n\ndef max_addOne(x, mx):\n\t# find max ending in last index\n\t# keep track of running max ending in n\n\tmaxSum = float('-inf')\n\t# keep track of sum up til current index\n\tsum_curr = 0\n\n\t# for each index from last to first\n\tfor i in range(len(x)-1,-1,-1):\n\t\t# add new i to sum_Curr\n\t\tsum_curr += x[i]\n\n\t\t# if current sum more than global, update\n\t\tif sum_curr > maxSum:\n\t\t\tmaxSum = sum_curr\n\t# return larger of old max sum or new max sum\n\tif mx > maxSum:\n\t\treturn(mx)\n\telse:\n\t\treturn(maxSum)\n\ndef quickSort(arr):\n    if len(arr) <= 1:\n        return(arr)\n\n    # select last elem as pivot\n    j = len(arr)-1\n\n    # sort arr so all items less than pivot are on left of the rest\n    i = 0\n    k = j-1\n\n    while i <= k:\n        while i <= j and arr[i] <= arr[j]:\n            i += 1\n        while k >= 0  and arr[k] >= arr[j]:\n            k -= 1\n        if i < k:\n            arr[k],arr[i] = arr[i],arr[k]\n\n    # recurse on items less than and more than pivot\n    return(quickSort(arr[0:k+1]) + [arr[j]] + quickSort(arr[k+1:-1]))\n\n\ndef closeFar(arr):\n\tif len(arr) < 2:\n\t\treturn('less than 2 is not a pair :(')\n\tarr_s = quickSort(arr)\n\tfar = [arr_s[0],arr_s[-1]]\n\tleast_dist = float('inf')\n\tclose = []\n\tfor i in range(1,len(arr_s),1):\n\t\tif abs(arr_s[i] - arr_s[i-1]) < least_dist:\n\t\t\tleast_dist = arr_s[i] - arr_s[i-1]\n\t\t\tclose = [arr_s[i],arr_s[i-1]]\n\treturn(close,far)\n\n\t# T(n) approx= nlgn + n (but quicksort worst case n^2)\n\t# O(n) = n^2\n\t# omega(n) = nlgn\n\t# average case = nlgn\n\n\nif __name__ == \"__main__\":\n\tprint(maxSub_linear([-1,2,3,-4,5,10]))\n\tprint(maxSub_linear([1,-2,3]))\n\tprint(maxSub_linear([]))\n\tprint(maxSub_linear([-2,-3,-1,-4,-6]))\n\tprint(max_addOne([-1,2,3,-4,5,10],6))\n\tprint(max_addOne([1,-2,3],1))\n\tprint(max_addOne([-2,-3,-1,-4,-6, -1],-1))\n\tprint(closeFar([4,-10,2]))\n\tprint(closeFar([4,-10,2,9999,-89,-90]))\n\n\n","repo_name":"michlee1337/practice","sub_path":"Fundamentals/max_sub.py","file_name":"max_sub.py","file_ext":"py","file_size_in_byte":2957,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"11212637582","text":"import logging\n\nfrom django.conf import settings\nfrom django.http import HttpResponse, HttpResponseRedirect\n\nimport ModSecurity\n\nSETTINGS_NAMES = {\n    'rule_files': 'MODSECURITY_RULE_FILES',\n    'rule_lines': 'MODSECURITY_RULES',\n}\n\n\nclass PyModSecurityMiddleware(object):\n    def __init__(self, get_response):\n        '''\n        PyModSecurityMiddleware\n        This integrates the python bindings to the lib modsecurity to the\n        django ecosystem\n\n        :param callable get_response\n        '''\n        self.logger = logging.getLogger(__name__)\n\n        self.get_response = get_response\n\n        self.modsecurity = ModSecurity.ModSecurity()\n        self.modsecurity.setServerLogCb(self.modsecurity_log_callback)\n        self.rules = ModSecurity.Rules()\n\n        self.rule_files = getattr(settings, SETTINGS_NAMES['rule_files'], None)\n        if isinstance(self.rule_files, str):\n            self.rule_files = [self.rule_files]\n\n        self.rule_lines = getattr(settings, SETTINGS_NAMES['rule_lines'], None)\n        if isinstance(self.rule_lines, list):\n            self.rule_lines = '\\n'.join(self.rule_lines)\n\n        self._rules_count = 0\n        if self.rule_files is not None:\n            self.load_rule_files(self.rule_files)\n\n        if self.rule_lines is not None:\n            self.load_rules(self.rule_lines)\n\n    def modsecurity_log_callback(self, data, msg):\n        self.logger.info(msg)\n\n    @property\n    def rules_count(self):\n        return self._rules_count\n\n    def load_rule_files(self, rule_files):\n        '''\n        Process a list of files (can be a list of globs) and loads into modsecurity\n        :param list(str) rule_files\n        :rtype: int\n        :return the total rules that were loaded\n        '''\n        before_count = self.rules_count\n        import glob\n        for pattern in rule_files:\n            for rule_file in glob.glob(pattern, recursive=True):\n                rules_count = self.rules.loadFromUri(rule_file)\n                if rules_count < 0:\n                    msg = '[ModSecurity] Error trying to load rule file %s. %s' % (\n                        rule_file, self.rules.getParserError())\n                    self.logger.warning(msg)\n                else:\n                    self._rules_count += rules_count\n\n        return self.rules_count - before_count\n\n    def load_rules(self, rules):\n        '''\n        Process rules\n        :param str: rules\n        :rtype: int\n        :return the total rules that were loaded\n        '''\n        if rules is None or not len(rules) > 0:\n            return 0\n\n        rules_count = self.rules.load(rules)\n        if rules_count < 0:\n            msg = '[ModSecurity] Error trying to load rules: %s' % self.rules.getParserError(\n            )\n            self.logger.warning(msg)\n        else:\n            self._rules_count += rules_count\n\n        return rules_count\n\n    def __call__(self, request):\n        transaction = ModSecurity.Transaction(self.modsecurity, self.rules)\n        response = self.process_request(request, transaction)\n\n        # We got an intervention response when processing the request\n        # Do not proceed!\n        if response is not None:\n            return response\n\n        response = self.get_response(request)\n        response = self.process_response(request, response, transaction)\n        return response\n\n    def process_request(self, request, transaction):\n        '''\n        Process a request and checks with modsecurity if it's safe or if it should\n        make an intervention\n        '''\n        meta = request.META\n        transaction.processConnection(meta['REMOTE_ADDR'],\n                                      int(request.get_port()),\n                                      meta['SERVER_NAME'],\n                                      int(meta['SERVER_PORT']))\n\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        transaction.processURI(request.path, request.method, '1.1')\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        for key, value in self._iter_headers(request):\n            transaction.addRequestHeader(key, value)\n\n        transaction.processRequestHeaders()\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        transaction.appendRequestBody(request.body)\n        transaction.processRequestBody()\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        return None\n\n    def _iter_headers(self, request):\n        for key, value in request.META.items():\n            if key.startswith('HTTP_'):\n                yield key[5:], value\n\n    def process_response(self, request, original_response, transaction):\n        '''\n        Process a response and checks with modsecurity if it's safe or if it should\n        make an intervention\n        '''\n        for field, value in original_response.items():\n            transaction.addResponseHeader(field, value)\n\n        transaction.processResponseHeaders(original_response.status_code,\n                                           'HTTP/1.1')\n\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        transaction.appendResponseBody(original_response.getvalue())\n        transaction.processResponseBody()\n\n        response = self.process_intervention(transaction)\n        if response is not None:\n            return response\n\n        # No intervention so far, assume the response is safe\n        return original_response\n\n    def process_intervention(self, transaction):\n        '''\n        Check if there's interventions\n        :return the apropriate response, if any:\n        :rtype HttpResponse:\n        '''\n        intervention = ModSecurity.ModSecurityIntervention()\n\n        if intervention is None:\n            return None\n\n        if transaction.intervention(intervention):\n            if intervention.log is not None:\n                self.logger.info(intervention.log)\n\n            if not intervention.disruptive:\n                return None\n\n            if intervention.url is not None:\n                response = HttpResponseRedirect(intervention.url)\n            else:\n                response = HttpResponse(status=intervention.status)\n\n            return response\n        else:\n            return None\n","repo_name":"pymodsecurity/django-pymodsecurity","sub_path":"django_pymodsecurity/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"20510983038","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul  9 01:08:54 2022\n\n@author: juyeon\n\"\"\"\nimport requests\nfrom bs4 import BeautifulSoup\nimport numpy as np\nimport re\nfrom selenium import webdriver\nimport pandas as pd\nimport warnings\nimport random\n\n###############################\n\n# 페이지\npages = np.arange(1, 11)\n\n# 카테고리 번호\n# 반소매 티셔츠 : 001001\n\nnum = \"001001\"\n\n\ndef clothes_num(num, pages):\n    url_head = \"\"\"https://www.musinsa.com/category/{num}?d_cat_cd={num}&brand=&rate=&page_kind=search&list_kind=small\n    &sort=sale_high&sub_sort=1y&page={page}&display_cnt=90&sale_goods=&group_sale=&kids=N&ex_soldout=&color=&price1=&price2=&\n    exclusive_yn=&shoeSizeOption=&tags=&campaign_id=&timesale_yn=&q=&includeKeywords=&measure=\"\"\"\n    clothes_num = []\n\n    for page in pages:\n        url = url_head.format(num=num, page=page)\n        r = requests.get(url)\n        content = r.content\n        soup = BeautifulSoup(content, \"html.parser\")\n        items = soup.select(\"div > ul > li.li_box\")\n        for item in items:\n            clothes_num.append(item[\"data-no\"])\n\n    return clothes_num\n\n\n# 다 리스트 안에 저장 선형적으로 저장\nnum_li = clothes_num(num, pages)\n\nnum_li\n###############################\n\ndef coordi_processing(data):\n    \n    coordi_series = pd.Series(data)\n    coordi_list = [\"아메리칸 캐주얼\", \"캐주얼\", \"시크\", \"댄디\", \"포멀\", \"걸리시\", \"골프\", \"홈웨어\", \"레트로\", \"로맨틱\", \"스포츠\", \"스트릿\"]\n    coordi_TF = coordi_series.isin(coordi_list)\n\n    a = []\n    for i in np.arange(len(coordi_TF)):\n        if coordi_TF[i] == True:\n            a.append(data[i])\n            \n    if len(a) == 0:\n        a = \"\"\n    else:\n        a = list(set(a))\n        \n    return a\n\ndef season_processing(data, season_find):\n\n    if data == \"시즌\":\n        season = re.sub(\" +|\\n\", \" \", season_find).strip()\n    else:\n        season = \"\"\n\n    return season\n\n################################\n\n\ndef crawling(num_li):\n    # 크롬 드라이버 객체 생성\n    driver = webdriver.Chrome(\"/Users/yoohajun/Library/Mobile Documents/com~apple~CloudDocs/Hajun/프로젝트/Cordi_RecSys/crawling/chromedriver\")\n\n    driver.implicitly_wait(3)\n\n    # 상품을 담을 리스트\n    clothes_li = []\n\n    # beautifulsoup의 select 태그는 조건에 맞는 여러 개를 가져옴\n    for num in num_li:\n\n        # 상품 코드 바꿔가면서 페이지 순회 => 상품 조회\n        url = f'https://www.musinsa.com/app/goods/{num}'\n\n        try :\n            driver.get(url)\n\n            # 1초에서 2초 사이 랜덤하게 휴식\n            driver.implicitly_wait(random.randint(1, 2))\n\n            # 바뀐 url 정보를 parsing해서 soup변수에 저장\n            soup = BeautifulSoup(driver.page_source, 'html.parser')\n\n            # 상품 이름\n            name = soup.select(\"div > div > span > em\")[0].text\n            # 대분류\n            main_category = soup.select(\"div > div > p.item_categories > a\")[0].text\n            # 중분류\n            sub_category = soup.select(\"div > div > p.item_categories > a\")[1].text\n            # 브랜드\n            brand = soup.select(\"div > ul > li > p > strong > a\")[0].text\n            # 품번\n            number = soup.select_one(\"div > ul > li > p > strong\").text.split(\" / \")[1]\n            # 해시태그\n            tags = [re.sub('[#]', '', s.text) for s in soup.select(\"li > p > a.listItem\")]\n            # 가격\n            price = re.sub(\" +|\\n\", \"\", soup.select(\"#goods_price\")[0].text)\n            # 평점\n            rating = soup.select(\"span.prd-score__rating\")[0].text\n            # 후기 개수\n            rating_num = soup.select(\"span.prd-score__review-count\")[0].text\n            rating_num = re.sub(r'[^0-9]', '', rating_num)\n            # 시즌\n            season_word = soup.select(\"a.ui-toggle-btn\")[0].text.split(\" \")[0]\n            season_find = soup.select(\"li > p.product_article_contents > strong\")[1].text\n            season = season_processing(season_word, season_find)\n\n            # 성별\n            gender = [g.text for g in soup.select(\"li > p.product_article_contents > span.txt_gender > span\")]\n\n            # 좋아요 개수\n            like = soup.select(\"li > p > span.prd_like_cnt\")[0].text\n            # 조회수\n            view = soup.select(\"#pageview_1m\")[0].text\n            # 누적판매\n            sale = soup.select(\"#sales_1y_qty\")[0].text\n            # 코디 정보\n            coordi = [s.text for s in soup.select(\"p.style_txt\")]\n            coordi = coordi_processing(coordi)\n\n            # ~ 18세\n            age18 = soup.select(\"span.bar_num\")[0].text\n            # 19 ~ 23세\n            age19_23 = soup.select(\"span.bar_num\")[1].text\n            # 24 ~ 28세\n            age24_28 = soup.select(\"span.bar_num\")[2].text\n            # 29 ~ 33세\n            age29_33 = soup.select(\"span.bar_num\")[3].text\n            # 34 ~ 39세\n            age34_39 = soup.select(\"span.bar_num\")[4].text\n            # 40 ~\n            age40 = soup.select(\"span.bar_num\")[5].text\n            # 남성 구매 비율\n            man = soup.select(\"dd.label_info_value\")[0].text\n            # 여성 구매 비율\n            woman = soup.select(\"dd.label_info_value\")[1].text\n            # 상품 사진\n            img = 'https:' + soup.select(\"#detail_bigimg > div.product-img > img\")[0]['src']\n\n            # 옷 아이템 객체\n            clothes = [name, main_category, sub_category, brand, number, tags, price, rating, rating_num, season, gender,\n                       like, view, sale, coordi, age18, age19_23, age24_28, age29_33, age34_39, age40, man, woman, img]\n\n            # 옷 리스트에 아이템 객체 추가\n            clothes_li.append(clothes)\n\n            print(f'{num}, : {clothes} |\\n{\"*\" * 20}')\n\n        except :\n            pass\n\n    driver.quit()\n    \n    df = pd.DataFrame(clothes_li,\n                      columns=[\"name\", \"main_category\", \"sub_category\", \"brand\", \"number\", \"tags\",\"price\", \"rating\", \"rating_num\", \"season\", \"gender\",\n                               \"like\", \"view\", \"sale\", \"coordi\", \"age18\",\"age19_23\", \"age24_28\", \"age29_33\", \"age34_39\", \"age40\", \"man\", \"woman\", \"img\"])\n        \n    return df\n\n# 크롤링 실행해서 저장\nclothes = crawling(num_li)\n\n# csv 저장\nclothes.to_csv('short_sleeve_t-shirt.csv', index=True)\n\n","repo_name":"HaJunYoo/RecSys_Cordi","sub_path":"crawling/무신사_크롤링.py","file_name":"무신사_크롤링.py","file_ext":"py","file_size_in_byte":6382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12742330336","text":"from django.shortcuts import render\nimport requests\nfrom django.conf import settings\nfrom .models import Token, Logistics, LafastaCity\nfrom core.models import Shipment\nfrom dateutil import parser\nfrom django.utils.timezone import make_aware\nfrom cities_light.models import City\nfrom user.views import send_email\n# Create your views here.\n\nif settings.DEBUG:\n    lafasta_base_url = 'https://dev.quick.sa.com/API/'\nelse:\n    lafasta_base_url = 'https://c.lafasta.sa.com/API/'\n\ndef ProcessShipping(order):\n\n    logistic = order.shipment.logistic.name\n    if logistic.lower() == 'lafasta':\n        ProcessLafasta(order)\n\ndef ProcessLafasta(order):\n\n    logistic_name = order.get_logistic_name()\n    \n    logistic_model = Logistics.objects.get(name=logistic_name)\n    token_obj = Token.objects.filter(logistic=logistic_model)\n    \n    if token_obj.exists():\n        token = token_obj[0]\n        access_token = GetNewToken(token)\n        CreatePolicyLafasta(access_token, order)\n    else:\n        access_token = GetLafastaToken(logistic_name)\n        CreatePolicyLafasta(access_token, order)\n\ndef CreatePolicyLafasta(token_access, order):\n    \n    url = lafasta_base_url+'V3/Store/Shipment'\n\n    if order.payment.is_prepaid():\n        PaymentMethodId = 1\n    else:\n        PaymentMethodId = 4\n\n    city_obj =  City.objects.get(name=order.shipment.address.city.name)\n    lafasta_cities = LafastaCity.objects.all()\n    city_id = 0\n    for city in lafasta_cities:\n       sub_names = city_obj.alternate_names.split(';')\n       for name in sub_names:\n           if name == city.city_name:\n               city_id = city.city_id\n\n    if city_obj == 0:\n        # report an issue\n        send_email(\"Lafasta Policy Error!\", [settings.REPORT_EMAIL], f'error in order {order.id} policy creation !!')\n    else:\n   \n        payload = '''{{\n            SandboxMode: true,\n            CustomerName: \"{name}\",\n            CustomerPhoneNumber: \"{mobile}\",\n            PaymentMethodId: {PaymentMethodId},\n            ShipmentContentValueSAR: {total},\n            ShipmentContentTypeId: 4,\n            CustomerLocation: {{\n                Desciption: \"{address}\",\n                CityId: {cityId},\n            }},\n            ExternalStoreShipmentIdRef: \"{orderId}\",\n            Currency:\"SAR\"\n        }}'''.format(name=order.address.name, mobile=f'{order.address.mobile}', PaymentMethodId=f'{PaymentMethodId}', total=f'{order.total}', address=order.address.get_policy_address(), orderId=f'orderId-{order.id}', cityId=city_id)\n        # print(payload)\n        auth = f'Bearer {token_access}'\n        headers = {\n            \"Authorization\": auth,\n            'Content-Type': 'application/json'\n        }\n\n        response = requests.request(\"POST\", url, headers=headers, data=payload.encode('utf-8'))\n        response_json = response.json()\n        if response_json['isSuccess']:\n            order.shipment.tracking = response_json['resultData']['id']\n            order.shipment.save()\n            # barcode = response_json['resultData']['barcodeImage']['imgDataBase64']\n            # status = response_json['resultData']['currentShipmentStatus']['id']\n        else:\n            # report an issue\n            send_email(\"Lafasta Policy Fail!\", [settings.REPORT_EMAIL], f'Order {order.id} policy creation failed response from lafasta server !!')\n        # print(response.text)\n\ndef GetNewToken(token):\n\n    url = lafasta_base_url+'Login/RefreshToken'\n    payload=f'refresh_token={token.refresh_token}'\n    headers = {\n      'Content-Type': 'application/x-www-form-urlencoded'\n    }\n\n    response = requests.request(\"POST\", url, headers=headers, data=payload)\n    response_json = response.json()\n\n    refresh = response_json['resultData']['refresh_token']\n    access = response_json['resultData']['access_token']\n    refresh_date = make_aware(parser.parse(response_json['resultData']['refresh_token_expires']))\n    access_date = make_aware(parser.parse(response_json['resultData']['access_token_expires']))\n\n    token.access_token = access\n    token.access_expire = access_date\n    token.refresh_token = refresh\n    token.refresh_date = refresh_date\n    token.save()\n\n    return access\n\ndef GetLafastaToken(logistic_name):\n\n    url = lafasta_base_url+'Login/GetAccessToken'\n    payload=f'UserName={settings.LAFASTA_USERNAME}&Password={settings.LAFASTA_PASSWORD}'\n    headers = {\n      'Content-Type': 'application/x-www-form-urlencoded'\n    }\n\n    response = requests.request(\"POST\", url, headers=headers, data=payload)\n    response_json = response.json()\n    refresh = response_json['resultData']['refresh_token']\n    access = response_json['resultData']['access_token']\n    refresh_date = make_aware(parser.parse(response_json['resultData']['refresh_token_expires']))\n    access_date = make_aware(parser.parse(response_json['resultData']['access_token_expires']))\n\n    _ = Token.objects.create(\n        logistic = Logistics.objects.get(name=logistic_name),\n        access_token = access,\n        access_expire = access_date,\n        refresh_token = refresh,\n        refresh_date = refresh_date\n    )\n    return access\n","repo_name":"amaniBamugabel/test2","sub_path":"CurlyCrownSite/logistics/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2778828578","text":"# 1926. 간단한 369게임 <D2>\n\nN = int(input())\n\nfor i in range(1,N+1):\n    i = str(i)\n    clap = i.count('3') + i.count('6') + i.count('9')\n    \n    if clap == 0:\n        print(i, end=' ')\n    else:\n        print('-' * clap, end=' ')","repo_name":"todayis-sunny/Algorithm_test","sub_path":"D2/1926/1926_01.py","file_name":"1926_01.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33046485257","text":"import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\nfrom bokeh.plotting import figure\nfrom bokeh.io import output_notebook, show\nimport pandas as pd\nfrom bokeh.models import Range1d\n\nfrom bokeh.models import LinearAxis\nfrom bokeh.models import DatetimeTickFormatter\nfrom bokeh.models.sources import ColumnDataSource\n\n\n\n\ndef draw_stockdf(df):\n    startIndex = df.index[0]\n    data = df.sort_index(ascending=True, axis=0)\n    columnNames = df.columns\n    #new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'AdjClose', 'Close', 'Volume'])\n    trimColumnNames = []\n    for cname in columnNames:\n        trimColumnNames.append(cname.replace(' ', ''))\n    \n    data.columns = trimColumnNames\n    data['PlotVolume'] = data.apply(lambda x: round(x['Volume']/1000000, 1), axis=1)\n    \n    new_data = pd.DataFrame(index=range(0, len(df)), columns=trimColumnNames)\n\n    for i in range(0,len(data)):\n        for cname in trimColumnNames:\n            if cname != 'Volume' :\n                new_data[cname][i] =  data[cname][i+startIndex]\n            else :\n                new_data[cname][i] =  data['PlotVolume'][i+startIndex]\n\n    new_data['Date']=pd.to_datetime(new_data['Date'])\n\n    p = figure(title='Price vs Volume trend', \n            plot_width=1200, \n            plot_height=800,\n            x_axis_label='Month-Year', y_axis_label='Price', \n            x_axis_type='datetime')\n\n    rgb = 101\n    for cname in trimColumnNames:  \n        if (rgb<50) :\n            rgb += 17\n        elif (rgb>200) :\n            rgb -= 13\n        else:\n            pass\n        rgbcolor = (rgb, rgb+25, rgb-25)\n        if cname == 'Volume' :\n            rgbcolor = (rgb, rgb, rgb)\n            p.scatter(x = new_data.Date, y = new_data.Volume, color = 'green', size = 2)\n        if cname == 'Close':\n            rgbcolor = (rgb, rgb+50, rgb)\n            p.scatter(x = new_data.Date, y = new_data.Close, color = 'blue', size = 5)\n        if cname == 'AdjClose':\n            rgbcolor = (rgb, rgb, rgb-50)\n            p.scatter(x = new_data.Date, y = new_data.AdjClose, color = rgbcolor, size = 2)\n        rgb += 5\n\n    show(p)\n\ndef main():\n    \n    #filePath = r\"file:///home/lan/Documents/DownloadData/CNQ.csv\"\n    filePath = r\"file:///home/lan/Documents/py3ds/output/downloadedStockcsv/TSLA_Stock.csv\"\n    stockcsv = pd.read_csv(filePath, delimiter = \",\")\n    df = stockcsv[(stockcsv.Date>'2019-01-01')]\n    draw_stockdf(df)\n\n\n\nif __name__ == '__main__':\n    main()\n   \n\n\n\n\"\"\"new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'AdjClose', 'Close', 'Volume'])\n\nfor i in range(0,len(data)):\n    new_data['Date'][i] = data['Date'][i+startIndex]\n    new_data['Close'][i] = data['Close'][i+startIndex]\n    new_data['AdjClose'][i] = data['Adj Close'][i+startIndex]\n    new_data['Volume'][i] = round(data['Volume'][i+startIndex]/100000, 1)\n\n#print(new_data.head())\nnew_data['Date']=pd.to_datetime(new_data['Date'])\n#print(new_data.head())\n#data_source = ColumnDataSource(new_data)\n\np = figure(title='Price vs Volume trend', \n           plot_width=1200, \n           plot_height=800,\n           x_axis_label='Month-Year', y_axis_label='Price', \n          x_axis_type='datetime')\n\np.scatter(x = new_data.Date, y = new_data.Close, color = 'blue', size = 1)\np.scatter(x = new_data.Date, y = new_data.AdjClose, color = 'cyan' , size = 1)\np.line(x = new_data.Date, y = new_data.Volume, color = 'navy' )\nshow(p)\n\"\"\"\n","repo_name":"stockdata123/upgraded-journey","sub_path":"pointNotice/stock_plot.py","file_name":"stock_plot.py","file_ext":"py","file_size_in_byte":3423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22721595378","text":"def main():\n    ss = list(input())\n    n = len(ss)\n\n    ans1 = 0\n    ans2 = 0\n    for i in range(n):\n        if i % 2 == 0 and ss[i] == \"1\":\n            ans1 += 1\n        if i % 2 != 0 and ss[i] == \"0\":\n            ans1 += 1\n\n    for i in range(n):\n        if i % 2 == 0 and ss[i] == \"0\":\n            ans2 += 1\n        if i % 2 != 0 and ss[i] == \"1\":\n            ans2 += 1\n    ans = min(ans1, ans2)\n    print(ans)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Msksgm/atcoder_msksgm_practice","sub_path":"abc/124/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70778054650","text":"# -*- coding: utf-8 -*-\n# 开发者：顾昕健\n# 开发时间：2023/9/24 17:00\nimport os\n# import PySide2\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\n# dirname = os.path.dirname(PySide2.__file__)\n# plugin_path = os.path.join(dirname, 'plugins', 'platforms')\n# os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = plugin_path\n\n# 读取Excel文件，并指定要读取的工作表名称\ndf = pd.read_excel(r'D:\\PycharmProjects\\git\\问题2\\newdata2.1.xlsx')\n# 获取第四行到第243行的数据范围，第一次评审范围，并选择第极差列,第四列\ndata = df.iloc[2:242, [7, 10, 13, 16, 19]]\n# 获取对应的名次范围\ndata2 = df.iloc[2:242,1]\n# 获取第一列最终成绩\ndata3 = df.iloc[2:242,0]\n\n# 计算各列的均值\nmean_values = data.mean(axis=1)\nmean=mean_values.tolist()\n\n# 名次部分转换为列表\nmc = data2.tolist()\nmc_int = [int(x) for x in mc]\n\n# 最终成绩转换为列表\nzz = data3.tolist()\n\n\n# 绘制折线图\nplt.rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体为系统自带的黑体\nplt.rcParams['axes.unicode_minus'] = False  # 解决负号显示为方块的问题\nplt.plot(mc_int, mean)\nplt.xlabel('名次')\nplt.ylabel('标准分均值')\nplt.title('第一次评审标准分均值成绩图')\nplt.grid(True)\nplt.savefig('第一次评审标准分均值成绩图.png')\nplt.show()\n\n# 绘制折线图\nplt.rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体为系统自带的黑体\nplt.rcParams['axes.unicode_minus'] = False  # 解决负号显示为方块的问题\nplt.plot(mc_int, zz)\nplt.xlabel('名次')\nplt.ylabel('最终成绩图')\nplt.title('第二次评审最终成绩图')\nplt.grid(True)\nplt.savefig('第二次评审最终成绩图.png')\nplt.show()","repo_name":"flowerchar/HuaWeiCup23","sub_path":"day4/成绩对比图.py","file_name":"成绩对比图.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4830044917","text":"from math import sqrt, acos,pi \nfrom decimal import Decimal,getcontext\ngetcontext().prec=30\n\nclass Vector(object):\n    CANNOT_NORMALIZE_VECTOR_MSG='can not normalize the zero vector'\n    NO_UNIQUE_PARALLEL_COMPONENT_MESG='NO_UNIQUE_PARALLEL_COMPONENT_MESG'\n    def __init__(self,coordinates):\n        try:\n            if not coordinates:\n                raise ValueError\n            self.coordinates = tuple([Decimal(x)for x in  coordinates])\n            self.dimension = len(coordinates)\n        except ValueError:\n            raise ValueError('the coordinate must nonempty')\n        except TypeError:\n            raise TypeError('the coordinate must be itreable')\n    def __str__(self):\n        return 'Vector: {}'.format(self.coordinates)\n    def __eq__ (self,v):\n        return self.coordinates == v.coordinates\n    def plus(self,v):\n        new_coordinates = [x+y for x,y in zip (self.coordinates , v.coordinates)]\n        return Vector(new_coordinates) \n    def minus(self,v):\n        new_coordinates = [x-y for x,y in zip(self.coordinates , v.coordinates)]\n        return Vector(new_coordinates)\n    def time_scalar(self,c):\n        new_coordinates = [Decimal(c)*x for x in self.coordinates]\n        return new_coordinates\n    def magnitude(self):\n        coordinate_squared = [Decimal(x)**2 for x in self.coordinates]\n        return Decimal(sqrt(sum(coordinate_squared)))\n    def normalized(self):\n        try:\n            magnitude = self.magnitude()\n            return Vector(self.time_scalar(Decimal('1.0')/magnitude))\n        except ZeroDivisionError:\n            raise Exception('can Not normalized zero vector')\n    def dot(self,v):\n        return sum([x*y for x,y in zip(self.coordinates,v.coordinates)])\n    def angle(self,v,in_degree = False):\n        try:\n            u1= self.normalized\n            u2= v.normalized\n            angel_in_radianes= acos(u1.dot(u2))\n            if in_degree:\n                degree_per_radian= 180. / pi \n                return angel_in_radianes * degree_per_radian\n            else:\n                return angel_in_radianes\n        except Exception as e:\n            if str(e)==self.CANNOT_NORMALIZE_VECTOR_MSG:\n                raise Exception('can Not compute an angle with zero vector')\n            else:\n                raise e\n    \n    def is_orthogonal_to(self,v,tolerance=1e-10):\n        return abs(self.dot(v)) < tolerance\n    \n    def is_zero(self,tolerance=1e-10):\n        return self.magnitude() < tolerance\n    \n    def is_parallel_to(self,v):\n        return (self.is_zero() or v.is_zero() or self.angle(v)==0 or self.angle(v)== pi )\n    \n    def component_parallel_to(self,basis):\n        try:\n            u = basis.normalized()\n            weight= self.dot(u)\n            return Vector(u.time_scalar(weight))\n        except Exception as e :\n            if str(e) == self.CANNOT_NORMALIZE_VECTOR_MSG:\n                raise Exception(self.NO_UNIQUE_PARALLEL_COMPONENT_MESG)\n            else:\n                raise e\n                \n    def component_orthogonal_to(self,basis):\n        try:\n            projection= self.component_parallel_to(basis)\n            return self.minus(projection)\n        except Exception as e:\n            if str(e)== self.NO_UNIQUE_PARALLEL_COMPONENT_MESG:\n                raise Exception(self.NO_UNIQUE_ORTHOGONAL_COMPONENT_MSG)\n            else:\n                raise e\n    def cross(self,v):\n        try:\n            x1,y1,z1 = self.coordinates\n            x2,y2,z2 = v.coordinates\n            new_coordinates = [y1*z2 - y2*z1 ,\n                              -(x1*z2 - x2*z1),\n                               x1*y2 - x2*y1]\n            return Vector(new_coordinates)\n        except ValueError as e :\n            msg=str(e)\n            \n            if msg == 'need more than value to unpack':\n                self_embedded_in_R3= Vector(self.coordinates + ('0',))\n                v_embedded_in_R3= Vector(v.coordinates + ('0',))\n                return self_embedded_in_R3.cross(v_embedded_in_R3)\n            elif(msg == 'to many values to unpack' or msg == 'need more than 1 value to unpack' ):\n                raise Exception(self.ONLY_DEFINED_IN_TWO_THREE_DIM_MSG)\n            else :\n                raise e\n                \n    def area_of_parallelogram(self,v):\n        cross_product= self.cross(v)\n        return cross_product.magnitude()\n    \n    def area_of_triangle(self,v):\n        return self.area_of_parallelogram(v) / Decimal('2.0')\n        \n        ","repo_name":"DataDetactive/Machine-Learning","sub_path":"Vectors operations/VectorFile.py","file_name":"VectorFile.py","file_ext":"py","file_size_in_byte":4454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8154373820","text":"import platform\r\nimport sys\r\nsys.path.append(\"..\")\r\nimport pypibot\r\nfrom pypibot import log\r\nfrom transport import Transport\r\nfrom dataholder import MessageID\r\nimport params\r\nimport time\r\nimport signal \r\n\r\n#for linux\r\nport=\"/dev/pibot\"\r\n\r\n#for windows\r\n#port=\"com3\"\r\n\r\npypibot.assistant.enableGlobalExcept()\r\n#log.enableFileLog(log_dir + \"ros_$(Date8)_$(filenumber2).log\")\r\nlog.setLevel(\"i\")\r\n\r\nrun_flag = True\r\n\r\ndef exit(signum, frame):\r\n    global run_flag\r\n    run_flag = False\r\n\r\nif __name__ == '__main__':\r\n    signal.signal(signal.SIGINT, exit)\r\n\r\n    mboard = Transport(port, params.pibotBaud)\r\n    if not mboard.start():\r\n        log.error(\"can not open %s\"%port)\r\n        sys.exit()\r\n    \r\n    DataHolder = mboard.getDataHolder()\r\n\r\n    for num in range(0,3):\r\n        log.info(\"****************get robot version*****************\")\r\n        boardVersion = DataHolder[MessageID.ID_GET_VERSION]\r\n        p = mboard.request(MessageID.ID_GET_VERSION)\r\n        if p:\r\n            log.info(\"firmware version:%s buildtime:%s\\r\\n\"%(boardVersion.version.decode(), boardVersion.build_time.decode()))\r\n            break\r\n        else:\r\n            log.error('read firmware version err\\r\\n')\r\n            import time\r\n            time.sleep(1)\r\n            if num == 2:\r\n                log.error('please check connection or baudrate\\r\\n')\r\n                sys.exit()\r\n                \r\n    # get robot parameter\r\n    robotParam = DataHolder[MessageID.ID_GET_ROBOT_PARAMETER]\r\n    p = mboard.request(MessageID.ID_GET_ROBOT_PARAMETER)\r\n    if p:\r\n        log.info(\"model_type:%d wheel_diameter:%d wheel_track:%d encoder_resolution:%d\" \\\r\n                 %(robotParam.param.model_type, \\\r\n                   robotParam.param.wheel_diameter, \\\r\n                   robotParam.param.wheel_track, \\\r\n                   robotParam.param.encoder_resolution\r\n                   ))\r\n\r\n        log.info(\"do_pid_interval:%d kp:%d ki:%d kd:%d ko:%d\" \\\r\n                 %(robotParam.param.do_pid_interval, \\\r\n                   robotParam.param.kp, \\\r\n                   robotParam.param.ki, \\\r\n                   robotParam.param.kd, \\\r\n                   robotParam.param.ko))\r\n\r\n        log.info(\"cmd_last_time:%d imu_type:%d\" \\\r\n                 %(robotParam.param.cmd_last_time,\\\r\n                   robotParam.param.imu_type\r\n                   ))\r\n\r\n        log.info(\"max_v:%d %d %d\\r\\n\" \\\r\n                 %(robotParam.param.max_v_liner_x,\\\r\n                   robotParam.param.max_v_liner_y, \\\r\n                   robotParam.param.max_v_angular_z\r\n                   ))\r\n                   \r\n        log.info(\"motor flag:%d encoder flag: %d\\r\\n\" \\\r\n                 %(robotParam.param.motor_nonexchange_flag,\\\r\n                   robotParam.param.encoder_nonexchange_flag\r\n                   ))\r\n    else:\r\n        log.error('get params err\\r\\n')\r\n        quit(1)\r\n\r\n    log.info(\"****************get odom&imu*****************\")\r\n    while run_flag:\r\n        robotOdom = DataHolder[MessageID.ID_GET_ODOM]\r\n        p = mboard.request(MessageID.ID_GET_ODOM)\r\n        if p:\r\n            log.info('request get odom success, vx=%d vy=%d vangular=%d,    x=%d y=%d yaw=%d'%(robotOdom.v_liner_x, \\\r\n                                                        robotOdom.v_liner_y, \\\r\n                                                        robotOdom.v_angular_z, \\\r\n                                                        robotOdom.x, \\\r\n                                                        robotOdom.y, \\\r\n                                                        robotOdom.yaw))\r\n        else:\r\n            log.error('get odom err')\r\n            quit(1)\r\n        \r\n        robotIMU = DataHolder[MessageID.ID_GET_IMU].imu\r\n        p = mboard.request(MessageID.ID_GET_IMU)\r\n        if p:\r\n            log.info('get imu success, imu=[%f %f %f    %f %f %f    %f %f %f]'%(robotIMU[0], robotIMU[1], robotIMU[2], \\\r\n                                                                                        robotIMU[3], robotIMU[4], robotIMU[5], \\\r\n                                                                                        robotIMU[6], robotIMU[7], robotIMU[8]))\r\n        else:\r\n            log.error('get imu err')\r\n            quit(1)\r\n       \r\n        time.sleep(0.1) \r\n","repo_name":"pibotek/pibot_bringup","sub_path":"tools/pypibot/transport/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19380195857","text":"# Complete a tabela respondendo True ou False, considerando:\n\na = 4\nb = 10\nc = 5.0\nd = 1\nf = 5\n\n# a==c False\n# a<b  True\n# d>b  False\n# c!=f False\n# a==b False\n# c<d  False\n# b>a  True\n# c>=f True\n# f>=c True\n# c<=c True\n# c<=f True\n","repo_name":"edmilsonlibanio/Ola-Mundo-Python","sub_path":"iapcompython/exe_3_2.py","file_name":"exe_3_2.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7747312259","text":"import requests\nimport keys\nfrom datetime import *\nfrom twilio.rest import Client\n\n\n#--------------------GLOBAL VARIABLES--------------------#\n\n# list containing of the tickers we are watching out for\nstock_tickers = [\"NFLX\", \"TSLA\"]\n# \"NFLX\", \"NVDA\", \"AMD\", \"GME\", \"SPCE\",\"GOOGL\"\n\n# variables to store the relative values of today and yesterday\ntoday = date.today()\nyesterday = today - timedelta(days = 1)\n\n# data structure collectors set as global variables to be accessed by all the functions\nstocks_yest = {\"NFLX\":250,\"GOOGL\":250}\nstocks_today = {\"NFLX\":270,\"GOOGL\":230}\n\n\n#--------------------FUNCTIONS--------------------#\ndef fetch_stock():\n\n    # loop through stocks\n    for stock in stock_tickers:\n\n        # update the param with current stock, get the data, raise errors if any\n        keys.STOCKS_PARAM[\"symbol\"] = stock\n        stock_reponse = requests.get(url=keys.STOCKS_API_URL, params=keys.STOCKS_PARAM)\n        stock_reponse.raise_for_status()\n\n        # add opening price of the stock\n        stocks_today[stock] = stock_reponse.json()[\"Time Series (Daily)\"][str(today)][\"1. open\"]\n\n        # if today is monday then yesterday will be set to friday's date\n        if datetime.now().weekday() == 0:\n\n            yesterday =  today - timedelta(days=3)\n\n        # add stock's closing price from yesterday\n        stocks_yest[stock] = stock_reponse.json()[\"Time Series (Daily)\"][str(yesterday)][\"4. close\"]\n\ndef send_alerts(stock,value,emoji,title,description):\n\n    account_sid = 'ACf83fa8ce30bef8fcc9757af90b05a18d'\n    auth_token = 'a66a3408837539a0736ec39202ccf0b0'\n\n    client = Client(account_sid, auth_token)\n\n    message = client.messages.create(\n        from_='+18445591173',\n        to='+14085505024',\n        body= f\"{stock}: {emoji} {abs(value)}%\\nHeadline:{title}\\nDescription:{description}\"\n    )\n\n    print(message.sid)\n\ndef fetch_news(ticker,from_date,to_date):\n\n    news = \"potty\"\n    keys.NEWS_PARAM[\"from\"] = from_date\n    keys.NEWS_PARAM[\"to\"] = to_date\n    keys.NEWS_PARAM[\"q\"] = keys.ticker_to_company[ticker]\n\n    news_response = requests.get(url=keys.NEWS_API_URL,params=keys.NEWS_PARAM)\n    title = news_response.json()['articles'][0][\"title\"]\n    description = news_response.json()['articles'][0][\"description\"]\n\n    return [title,description]\n\ndef compare_stocks():\n\n    # loop through today's stocks\n    for stock,price in stocks_today.items():\n\n        # calculate diff of stock prices and convert its percentage\n        delta = (price - stocks_yest[stock])\n        delta_percent =  (delta / stocks_yest[stock]) * 100\n\n        # if delta change in price is greater than 2% then trigger notifications\n        if int(delta_percent) > 2:\n\n            news = fetch_news(stock, yesterday, today)\n            send_alerts(stock,delta_percent,\"🔼 🟩\",news[0],news[1])\n\n        elif int(delta_percent) < -2:\n\n            news = fetch_news(stock, yesterday, today)\n            send_alerts(stock, delta_percent, \"⬇� 🔴\",news[0],news[1])\n\n\n#--------------------MAIN--------------------#\\\nif __name__ == '__main__':\n\n   # fetch_stock()\n\n    print(stocks_yest)\n    print(stocks_today)\n\n    compare_stocks()\n\n","repo_name":"Prathamdoshi/Bull-Signals","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31188069097","text":"# Will instanciate and hold user info\n# Username, Credit, Type\nfrom decimal import Decimal\nfrom utils.settings import (\n    LOGOUT,\n    CREATE,\n    DELETE,\n    SELL,\n    BUY,\n    REFUND,\n    ADD_CREDIT,\n    AA,\n    FS,\n    BS,\n    SS,\n    MAX_CREDIT,\n    MIN_CREDIT\n)\nfrom utils.permissions import getPermissions\nclass User:\n    def __init__(self, username, type, credit = 0.00):\n        self.username = username\n        self.type = type\n        self.credit = Decimal(credit)\n        self.permissions = getPermissions(self.type)\n\n    @classmethod\n    def fromJSON(cls, userJSON):\n        tempName = userJSON['username']\n        tempType = userJSON['type']\n        tempCredit = userJSON['credit']\n        return cls(tempName, tempType, tempCredit)\n\n\n    def description(self) -> None:\n        print(\"  Name: {} \\n\"\n              \"  Type: {} \\n\"\n              \"Credit: {}\"\n              .format(self.username, self.type, self.credit))\n\n    def hasPermission(self, req: str) -> bool:\n        return (req in self.permissions)\n\n    def asJSON(self):\n        return {\n            'username': self.username,\n            'type': self.type,\n            'credit': self.credit\n        }\n\n    def chargeCredit(self, amount):\n        if (self.credit - amount) < MIN_CREDIT:\n            raise ValueError(\"User does not have enough credit.\")\n        self.credit -= amount\n\n    def addCredit(self, amount):\n        if (self.credit + amount) > MAX_CREDIT:\n            raise ValueError(\"Maximum credit exceeded.\")\n        self.credit += amount\n","repo_name":"rgoncales/CPS707","sub_path":"user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41947496200","text":"# coding;utf8\n\nimport datetime\nimport os\nimport uuid\nfrom functools import wraps\n\nfrom flask import render_template, redirect, url_for, flash, session, request,abort\nfrom werkzeug.utils import secure_filename\n\nfrom app import db, app\nfrom app.admin import admin\nfrom app.admin.forms import LoginForm, TagForm, MovieForm, PreviewForm, PwdForm, AuthForm,RoleForm,AdminForm\nfrom app.models import Admin, Tag, Movie, Preview, User, Comment, Moviecol, Auth,Role\n\n\n# 用户登陆状态检查--装饰器\ndef admin_login_require(f):\n    @wraps(f)\n    def inner_func(*args, **kwargs):\n        if 'account' not in session:\n            return redirect(url_for('admin.login', next=request.url))\n        return f(*args, **kwargs)\n\n    return inner_func\n\n\n\n#权限限制--装饰器\ndef admin_auth(f):\n    @wraps(f)\n    def inner_func(*args,**kwargs):\n        admin = Admin.query.join(Role).filter(Admin.name==session['account']).filter(Role.id==Admin.role_id,).first()\n        authlist = Auth.query.all()\n        auths = admin.role.auths\n        auth = []\n        if auths:\n            auth = map(lambda i:int(i),auths.split(','))\n        urls = ['/admin'+j.url for i in auth for j in authlist if i==j.id]\n        print(urls)\n        url = request.url_rule\n        print(url)\n        if admin.is_super and str(url) not in urls:\n            abort(404,'No Permission!')\n        return f(*args,**kwargs)\n    return inner_func\n\n\n\n\n\n\n# 更改文件文件名称\ndef change_filename(filename):\n    filename_old = os.path.splitext(filename)\n    filename = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + str(uuid.uuid4().hex) + filename_old[-1]\n    return filename\n\n\n# 用户登录界面\n@admin.route('/login', methods=['POST', 'GET'])\ndef login():\n    form = LoginForm()\n    if form.validate_on_submit():\n        data = form.data\n        admin = Admin.query.filter_by(name=data['account']).first()\n        if not admin.check_pwd(data['pwd']):\n            flash('密码错误')\n            return redirect(url_for('admin.login'))\n        session['account'] = data['account']\n        return redirect(request.args.get('next') or url_for('admin.index'))\n    return render_template('admin/login.html', form=form)\n\n\n@admin.route('/logout')\ndef logout():\n    session.pop('account', None)\n    return redirect(url_for('admin.login'))\n\n\n@admin.route('/')\n@admin_login_require\ndef index():\n    return render_template('admin/index.html')\n\n\n# 标签列表\n@admin.route('/tag/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef tag_list(page=1):\n    page_data = Tag.query.order_by(Tag.id.asc()).paginate(page=page, per_page=10)\n    return render_template('admin/tag_list.html', page_data=page_data)\n\n\n# 添加标签\n@admin.route('/tag/add', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef tag_add():\n    form = TagForm()\n    if form.validate_on_submit():\n        data = form.data\n        tag = Tag.query.filter_by(name=data['name']).count()\n        if tag == 1:\n            flash('操作失败', 'err')\n            return redirect(url_for('admin.tag_add'))\n        tag = Tag(name=data['name'])\n        db.session.add(tag)\n        db.session.commit()\n        flash('操作成功', 'ok')\n        return redirect(url_for('admin.tag_add'))\n    return render_template('admin/tag_add.html', form=form)\n\n\n# 修改标签\n@admin.route('/tag/edit/<int:id>', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef tag_edit(id):\n    form = TagForm()\n    tag = Tag.query.get_or_404(id)\n    if form.validate_on_submit():\n        data = form.data\n        tag_co = Tag.query.filter_by(name=data['name']).count()\n        if tag.name != data['name'] and tag_co == 1:\n            flash('标签已存在', 'err')\n            return redirect(url_for('admin.tag_edit', id=id))\n        tag.name = data['name']\n        db.session.add(tag)\n        db.session.commit()\n        flash('操作成功', 'ok')\n        return redirect(url_for('admin.tag_list', page=1))\n    return render_template('admin/tag_edit.html', form=form, tag=tag)\n\n\n# 删除标签\n@admin.route('/tag/del/<int:id>', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef tag_del(id):\n    tag = Tag.query.filter_by(id=id).first_or_404()\n    db.session.delete(tag)\n    db.session.commit()\n    flash('标签删除成功', 'ok')\n    return redirect(url_for('admin.tag_list', page=1))\n\n\n# 电影列表\n@admin.route('/movie/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef movie_list(page=1):\n    page_data = Movie.query.join(Tag).filter(Movie.tag_id == Tag.id).order_by(Movie.id.desc()).paginate(page=page,\n                                                                                                             per_page=10)\n    return render_template('admin/movie_list.html', page_data=page_data)\n\n\n# 删除电影\n@admin.route('/movie/del/<int:id>', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef movie_del(id):\n    tag = Movie.query.filter_by(id=id).first_or_404()\n    db.session.delete(tag)\n    db.session.commit()\n    flash('电影删除成功', 'ok')\n    return redirect(url_for('admin.movie_list', page=1))\n\n\n# 添加电影\n@admin.route('/movie/add/', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef movie_add():\n    form = MovieForm()\n    if request.method == 'POST':\n        data = form.data\n        if not os.path.exists(app.config['UP_DIR']):\n            os.makedirs(app.config['UP_DIR'], mode=0o777)\n        file_url = secure_filename(form.moviefile.data.filename)\n        file_logo = secure_filename(form.logo.data.filename)\n        movieurl = change_filename(file_url)\n        movielogo = change_filename(file_logo)\n        form.moviefile.data.save(app.config['UP_DIR'] + movieurl)\n        form.logo.data.save(app.config['UP_DIR'] + movielogo)\n        movie = Movie(\n            title=data['title'],\n            url=movieurl,\n            info=data['info'],\n            logo=movielogo,\n            star=data['star'],\n            tag_id=data['tag'],\n            area=data['area'],\n            release_time=data['release_time'],\n            length=data['length'],\n            playnum=0,\n            commentnum=0,\n        )\n        db.session.add(movie)\n        db.session.commit()\n        flash('电影添加成功', 'ok')\n        return redirect(url_for('admin.movie_add'))\n    return render_template('admin/movie_add.html', form=form)\n\n\n# 修改电影\n@admin.route('/movie/edit/<int:id>/', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef movie_edit(id):\n    form = MovieForm()\n    form.moviefile.validators = False\n    form.logo.validators = False\n    movie = Movie.query.get_or_404(id)\n    if request.method == 'GET':\n        form.info.data = movie.info\n\n    if request.method == 'POST':\n        data = form.data\n        if not os.path.exists(app.config['UP_DIR']):\n            os.makedirs(app.config['UP_DIR'], mode=0o777)\n        if form.moviefile.data:\n            file_url = secure_filename(form.moviefile.data.filename)\n            movieurl = change_filename(file_url)\n            form.moviefile.data.save(app.config['UP_DIR'] + movieurl)\n            movie.url = movieurl\n        if form.logo.data:\n            file_logo = secure_filename(form.logo.data.filename)\n            movielogo = change_filename(file_logo)\n            form.logo.data.save(app.config['UP_DIR'] + movielogo)\n            movie.logo = movielogo\n        movie.title = data['title']\n        movie.info = data['info']\n        movie.star = data['star']\n        movie.tag_id = data['tag']\n        movie.area = data['area']\n        movie.release_time = data['release_time']\n        movie.length = data['length']\n        db.session.add(movie)\n        db.session.commit()\n        flash('电影修改成功', 'ok')\n        return redirect(url_for('admin.movie_list', page=1))\n    return render_template('admin/movie_edit.html', form=form, movie=movie)\n\n\n# 预告列表\n@admin.route('/preview/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef preview_list(page=1):\n    pagedata = Preview.query.order_by(Preview.addtime.desc()).paginate(page=page, per_page=10)\n    return render_template('admin/preview_list.html', pagedata=pagedata)\n\n\n# 添加预告\n@admin.route('/preview/add', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef preview_add():\n    form = PreviewForm()\n    if request.method == 'POST':\n        data = form.data\n        if not os.path.exists(app.config['UP_DIR']):\n            os.makedirs(app.config['UP_DIR'], mode=0o777)\n        file_logo = secure_filename(form.logo.data.filename)\n        previewlogo = change_filename(file_logo)\n        form.logo.data.save(app.config['UP_DIR'] + previewlogo)\n        preview = Preview(\n            title=data['title'],\n            logo=previewlogo,\n        )\n        db.session.add(preview)\n        db.session.commit()\n        flash('预告添加成功！', 'ok')\n        return redirect(url_for('admin.preview_add'))\n    return render_template('admin/preview_add.html', form=form)\n\n\n# 修改预告\n@admin.route('/preview/edit/<int:id>/', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef preview_edit(id):\n    form = PreviewForm()\n    form.title.validators = []\n    form.logo.validators = []\n    preview = Preview.query.get_or_404(id)\n    if request.method == 'POST':\n        data = form.data\n        if not os.path.exists(app.config['UP_DIR']):\n            os.makedirs(app.config['UP_DIR'], mode=0o777)\n        if form.logo.data:\n            file_logo = secure_filename(form.logo.data.filename)\n            previewlogo = change_filename(file_logo)\n            form.logo.data.save(app.config['UP_DIR'] + previewlogo)\n            preview.logo = previewlogo\n        preview.title = data['title']\n        db.session.add(preview)\n        db.session.commit()\n        flash('预告修改成功！', 'ok')\n        return redirect(url_for('admin.preview_edit', id=id))\n    return render_template('admin/preview_edit.html', form=form, preview=preview)\n\n\n# 删除预告\n@admin.route('/preview/del/<int:id>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef preview_del(id):\n    preview = Preview.query.get_or_404(id)\n    db.session.delete(preview)\n    db.session.commit()\n    flash('预告删除成功！', 'ok')\n    return redirect(url_for('admin.preview_list', page=1))\n\n\n# 用户列表\n@admin.route('/user/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef user_list(page=1):\n    pagedata = User.query.order_by(User.addtime.desc()).paginate(page=page, per_page=10)\n    return render_template('admin/user_list.html', pagedata=pagedata)\n\n\n# 用户信息页\n@admin_login_require\n@admin.route('/user/view/<int:id>', methods=['GET'])\n@admin_auth\ndef user_view(id):\n    user = User.query.get_or_404(id)\n    return render_template('admin/user_view.html', user=user)\n\n\n# 删除用户\n@admin_login_require\n@admin.route('/user/del/<int:id>')\n@admin_auth\ndef user_del(id):\n    user = User.query.get_or_404(id)\n    db.session.delete(user)\n    db.session.commit()\n    flash('用户删除成功', 'ok')\n    return redirect(url_for('admin.user_list', page=1))\n\n\n# 评论管理\n@admin.route('/comment/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef comment_list(page=1):\n    pagedata = Comment.query.join(Movie).join(User).filter(\n        Comment.movie_id == Movie.id,\n        Comment.user_id == User.id\n    ).order_by(Comment.addtime.desc()).paginate(page=page, per_page=10)\n    return render_template('admin/comment_list.html', pagedata=pagedata)\n\n\n# 删除评论\n@admin_login_require\n@admin.route('/comment/del/<int:id>')\n@admin_auth\ndef comment_del(id):\n    comment = Comment.query.get_or_404(id)\n    db.session.delete(comment)\n    db.session.commit()\n    flash('评论删除成功', 'ok')\n    return redirect(url_for('admin.comment_list', page=1))\n\n\n# 收藏管理\n@admin.route('/moviecol/list/<int:page>/', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef moviecol_list(page=1):\n    pagedata = Moviecol.query.join(Movie).join(User).filter(\n        Moviecol.movie_id == Movie.id,\n        Moviecol.user_id == User.id\n    ).order_by(Moviecol.addtime.desc()).paginate(page=page, per_page=10)\n    return render_template('admin/moviecol_list.html', pagedata=pagedata)\n\n\n# 删除收藏\n@admin.route('/moviecol/del/<int:id>')\n@admin_login_require\n@admin_auth\ndef moviecol_del(id):\n    moviecol = Moviecol.query.get_or_404(id)\n    db.session.delete(moviecol)\n    db.session.commit()\n    flash('收藏删除成功', 'ok')\n    return redirect(url_for('admin.moviecol_list', page=1))\n\n\n# 修改密码\n@admin.route('/pwd', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef pwd():\n    form = PwdForm()\n    if request.method == 'POST':\n        data = form.data\n        admin = Admin.query.filter_by(name=session['account']).first()\n        if not admin.check_pwd(data['old_pwd']):\n            flash('原密码错误！', 'ok')\n            return redirect(url_for('admin.pwd'))\n        from werkzeug.security import generate_password_hash\n        admin.pwd = generate_password_hash(data['new_pwd'])\n        db.session.add(admin)\n        db.session.commit()\n        flash('修改密码成功', 'ok')\n        return redirect(url_for('admin.logout'))\n    return render_template('admin/pwd.html', form=form)\n\n\n@admin.route('/admin/oplog_list')\n@admin_login_require\n@admin_auth\ndef oplog_list():\n    return render_template('admin/oplog_list.html')\n\n\n@admin.route('/admin/adminloginlog_list')\n@admin_login_require\n@admin_auth\ndef adminloginlog_list():\n    return render_template('admin/adminloginlog_list.html')\n\n\n@admin.route('/admin/userloginlog_list')\n@admin_login_require\n@admin_auth\ndef userloginlog_list():\n    return render_template('admin/userloginlog_list.html')\n\n\n# 添加权限\n@admin.route('/auth/add', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef auth_add():\n    form = AuthForm()\n    if form.validate_on_submit():\n        data = form.data\n        auth = Auth(\n            name=data['name'],\n            url=data['url']\n        )\n        db.session.add(auth)\n        db.session.commit()\n        flash('权限添加成功!', 'ok')\n        return redirect(url_for('admin.auth_add'))\n    return render_template('admin/auth_add.html', form=form)\n\n\n# 修改权限\n@admin.route('/auth/edit/<int:id>', methods=['GET', 'POST'])\n@admin_login_require\n@admin_auth\ndef auth_edit(id):\n    form = AuthForm()\n    form.name.validators = []\n    form.url.validators = []\n    auth = Auth.query.get_or_404(id)\n    if form.validate_on_submit():\n        data = form.data\n        auth_co = Auth.query.filter_by(name=data['name']).count()\n        if auth.name != data['name'] and auth_co == 1:\n            flash('权限名称已存在', 'err')\n            return redirect(url_for('admin.auth_edit',id=id))\n        auth.name = data['name']\n        auth.url = data['url']\n        db.session.add(auth)\n        db.session.commit()\n        flash('权限修改成功!', 'ok')\n        return redirect(url_for('admin.auth_edit',id=id))\n    return render_template('admin/auth_edit.html', form=form, auth=auth)\n\n\n# 权限列表\n@admin.route('/auth/list/<int:page>', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef auth_list(page=1):\n    pagedata = Auth.query.order_by(Auth.addtime.desc()).paginate(page=page, per_page=10)\n    return render_template('admin/auth_list.html', pagedata=pagedata)\n\n\n# 删除权限\n@admin.route('/auth/del/<int:id>', methods=['GET'])\n@admin_login_require\n@admin_auth\ndef auth_del(id):\n    auth = Auth.query.get_or_404(id)\n    db.session.delete(auth)\n    db.session.commit()\n    flash('权限删除成功', 'ok')\n    return redirect(url_for('admin.auth_list', page=1))\n\n#添加角色\n@admin.route('/role/add',methods=['GET','POST'])\n@admin_login_require\n@admin_auth\ndef role_add():\n    form = RoleForm()\n    if form.validate_on_submit():\n        data = form.data\n        role_co = Role.query.filter_by(name=data['name']).count()\n        if role_co == 1:\n            flash('角色已存在','err')\n            return redirect(url_for('admin.role_add'))\n        role = Role(\n            name = data['name'],\n            auths = ','.join(map(lambda v :str(v),data['auths'])),\n        )\n        db.session.add(role)\n        db.session.commit()\n        flash('角色添加成功！','ok')\n        return redirect(url_for('admin.role_add'))\n    return render_template('admin/role_add.html',form=form)\n\n#角色列表\n@admin.route('/role/list/<int:page>',methods=['GET'])\n@admin_login_require\n@admin_auth\ndef role_list(page=1):\n    pagedata = Role.query.order_by(Role.addtime.desc()).paginate(page=page,per_page=10)\n    return render_template('admin/role_list.html',pagedata=pagedata)\n\n\n#删除角色\n@admin.route('/role/del/<int:id>',methods=['GET'])\n@admin_login_require\n@admin_auth\ndef role_del(id):\n    role = Role.query.get_or_404(id)\n    db.session.delete(role)\n    db.session.commit()\n    flash('角色删除成功','ok')\n    return redirect(url_for('admin.role_list',page=1))\n\n\n\n#修改角色\n@admin.route('/role/edit/<int:id>',methods=['GET','POST'])\n@admin_login_require\n@admin_auth\ndef role_edit(id):\n    form = RoleForm()\n    role = Role.query.get_or_404(id)\n    if role.auths:\n        form.auths.data = map(lambda v:int(v),(role.auths.split(',')))\n    if form.validate_on_submit():\n        data = form.data\n        role_co = Role.query.filter_by(name=data['name']).count()\n        if role_co == 1 and role.name!=data['name']:\n            flash('角色已存在','err')\n            return redirect(url_for('admin.role_edit',id=id))\n        role.name = data['name']\n        role.auths = ','.join(map(lambda v :str(v),data['auths']))\n        db.session.add(role)\n        db.session.commit()\n        flash('角色修改成功！','ok')\n        return redirect(url_for('admin.role_edit',id=id))\n    return render_template('admin/role_edit.html',form=form,role=role)\n\n\n\n\n@admin.route('/admin/add',methods=['GET','POST'])\n@admin_login_require\n@admin_auth\ndef admin_add():\n    form = AdminForm()\n    if form.validate_on_submit():\n        from werkzeug.security import generate_password_hash\n        data = form.data\n        admin = Admin(\n            name = data['name'],\n            pwd = generate_password_hash(data['pwd']),\n            role_id = data['role_id'],\n            is_super = 1\n        )\n        db.session.add(admin)\n        db.session.commit()\n        flash('管理员添加成功','ok')\n        return redirect(url_for('admin.admin_add'))\n    return render_template('admin/admin_add.html',form=form)\n\n\n@admin.route('/admin/list/<int:page>',methods=['GET'])\n@admin_login_require\n@admin_auth\ndef admin_list(page=1):\n    pagedata = Admin.query.order_by(Admin.addtime.desc()).paginate(page=page,per_page=10)\n    return render_template('admin/admin_list.html',pagedata=pagedata)\n","repo_name":"Zzzzzyk/MovieWeb","sub_path":"app/admin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":18736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16789464926","text":"import math\r\n\r\na = 66528\r\nb = 52920\r\n##a = 10 #test\r\n##b = 6 #test\r\n\r\nif a<b:\r\n    s = b\r\n    b = a\r\n    a = s\r\n\r\ngcd=0\r\n\r\nif a==0:\r\n    gcd=b\r\nif b==0:\r\n    gcd=a\r\n\r\n#a = b*quotient+remainder\r\nq = a//b\r\nr = a-(b*q)\r\nprint(math.gcd(b,r))","repo_name":"laptopoutofbattery/CTF","sub_path":"Cryptography/Math/scripts/Euclid_Algo.py","file_name":"Euclid_Algo.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29452650689","text":"import math\n\nclass Ciudad:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def camino(self, otra_ciudad):\n        pasos = []\n        dx = otra_ciudad.x - self.x\n        dy = otra_ciudad.y - self.y\n        distancia = max(abs(dx), abs(dy))\n\n        if distancia > 0:\n            step_x = dx / distancia\n            step_y = dy / distancia\n\n            for i in range(distancia + 1):\n                x = round(self.x + step_x * i)\n                y = round(self.y + step_y * i)\n                pasos.append([x, y])\n\n        return pasos\n\n    def distancia(self, otra_ciudad):\n        dx = otra_ciudad.x - self.x\n        dy = otra_ciudad.y - self.y\n        distancia = math.sqrt(dx**2 + dy**2)\n        return round(distancia, 15)\n\n\nif __name__ == \"__main__\":\n    p1 = Ciudad(1, 1)\n    p2 = Ciudad(3, 3)\n\n    pasos = p1.camino(p2)\n    print(pasos)  # [[1, 1], [2, 2], [3, 3]]\n\n    distancia = p1.distancia(p2)\n    print(distancia)  # 2.8284271247461903\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema3_ej5/tema3_ej5_e9414f1d0aa92d63f2a9adf93b7a16c1.py","file_name":"tema3_ej5_e9414f1d0aa92d63f2a9adf93b7a16c1.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20088588526","text":"import numpy as np\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import DataLoader\nimport torchvision.models as models\nfrom torchvision import transforms\nfrom tensorboardX import SummaryWriter\n\nfrom emotic import Emotic\nfrom emotic_dataset import Emotic_PreDataset\nfrom loss import DiscreteLoss, ContinuousLoss_SL1, ContinuousLoss_L2\nfrom prepare_models import prep_models\nfrom sam import SAM\nfrom skipnet import get_skipnet, SkipNet\nfrom test import test_data, test_scikit_ap, test_vad\nfrom tqdm import tqdm\n\nfrom utility.bypass_bn import enable_running_stats, disable_running_stats\nfrom utility.log import Log\n\n\ndef train_data(opt, scheduler, models, device, train_loader, val_loader, disc_loss, cont_loss, train_writer, val_writer,\n               model_path, args, ind2cat, ind2vad, a_len):\n    '''\n    Training emotic model on train data using train loader.\n    :param opt: Optimizer object.\n    :param scheduler: Learning rate scheduler object.\n    :param models: List containing model_context, model_body and emotic_model (fusion model) in that order.\n    :param device: Torch device. Used to send tensors to GPU if available.\n    :param train_loader: Dataloader iterating over train dataset.\n    :param val_loader: Dataloader iterating over validation dataset.\n    :param disc_loss: Discrete loss criterion. Loss measure between discrete emotion categories predictions and the target emotion categories.\n    :param cont_loss: Continuous loss criterion. Loss measure between continuous VAD emotion predictions and the target VAD values.\n    :param train_writer: SummaryWriter object to save train logs.\n    :param val_writer: SummaryWriter object to save validation logs.\n    :param model_path: Directory path to save the models after training.\n    :param args: Runtime arguments.\n    '''\n\n    model_context, model_body, emotic_model = models\n\n    emotic_model.to(device)\n    model_context.to(device)\n    model_body.to(device)\n\n    print('starting training')\n    last_ap = 0.0\n    for e in range(args.epochs):\n\n        running_loss = 0.0\n        running_cat_loss = 0.0\n        running_cont_loss = 0.0\n\n        emotic_model.train()\n        model_context.train()\n        model_body.train()\n        t_index = 0\n        # train models for one epoch\n        for images_context, images_body, labels_cat, labels_cont in tqdm(iter(train_loader)):\n            t_index += 1\n            images_context = images_context.to(device)\n            images_body = images_body.to(device)\n            labels_cat = labels_cat.to(device)\n            labels_cont = labels_cont.to(device)\n\n            opt.zero_grad()\n\n            pred_context = model_context(images_context)\n            pred_body = model_body(images_body)\n\n            pred_cat, pred_cont = emotic_model(pred_context, pred_body)\n            cat_loss_batch = disc_loss(pred_cat, labels_cat)\n            # 原cont_loss_batch = cont_loss(pred_cont * 10, labels_cont * 10)\n            # 尝试\n            cont_loss_batch = cont_loss(pred_cont, labels_cont)\n\n            loss = (args.cat_loss_weight * cat_loss_batch) + (args.cont_loss_weight * cont_loss_batch)\n\n            running_loss += loss.item()\n            running_cat_loss += cat_loss_batch.item()\n            running_cont_loss += cont_loss_batch.item()\n\n            loss.backward()\n            opt.step()\n\n        if e % 1 == 0:\n            print('epoch = %d loss = %.4f cat loss = %.4f cont_loss = %.4f' % (\n            e, running_loss / t_index, running_cat_loss / t_index, running_cont_loss / t_index))\n\n        train_writer.add_scalar('losses/total_loss', running_loss / t_index, e)\n        train_writer.add_scalar('losses/categorical_loss', running_cat_loss / t_index, e)\n        train_writer.add_scalar('losses/continuous_loss', running_cont_loss / t_index, e)\n\n        running_loss = 0.0\n        running_cat_loss = 0.0\n        running_cont_loss = 0.0\n\n        emotic_model.eval()\n        model_context.eval()\n        model_body.eval()\n        num_images = a_len\n        cat_preds = np.zeros((num_images, 26))\n        cat_labels = np.zeros((num_images, 26))\n        cont_preds = np.zeros((num_images, 3))\n        cont_labels = np.zeros((num_images, 3))\n\n        t_index = 0\n        indx = 0\n        with torch.no_grad():\n            # validation for one epoch\n            for images_context, images_body, labels_cat, labels_cont in iter(val_loader):\n                t_index += 1\n                images_context = images_context.to(device)\n                images_body = images_body.to(device)\n                labels_cat = labels_cat.to(device)\n                labels_cont = labels_cont.to(device)\n\n                pred_context = model_context(images_context)\n                pred_body = model_body(images_body)\n                pred_cat, pred_cont = emotic_model(pred_context, pred_body)\n\n                cat_loss_batch = disc_loss(pred_cat, labels_cat)\n                # 原cont_loss_batch = cont_loss(pred_cont * 10, labels_cont * 10)\n                # 尝试\n                cont_loss_batch = cont_loss(pred_cont, labels_cont)\n                loss = (args.cat_loss_weight * cat_loss_batch) + (args.cont_loss_weight * cont_loss_batch)\n\n                cat_preds[indx: (indx + pred_cat.shape[0]), :] = pred_cat.to(\"cpu\").data.numpy()\n                cat_labels[indx: (indx + labels_cat.shape[0]), :] = labels_cat.to(\"cpu\").data.numpy()\n                cont_preds[indx: (indx + pred_cont.shape[0]), :] = pred_cont.to(\"cpu\").data.numpy() * 10\n                cont_labels[indx: (indx + labels_cont.shape[0]), :] = labels_cont.to(\"cpu\").data.numpy() * 10\n                indx = indx + pred_cat.shape[0]\n\n                running_loss += loss.item()\n                running_cat_loss += cat_loss_batch.item()\n                running_cont_loss += cont_loss_batch.item()\n\n        cat_preds = cat_preds.transpose()\n        cat_labels = cat_labels.transpose()\n        cont_preds = cont_preds.transpose()\n        cont_labels = cont_labels.transpose()\n\n        ap = test_scikit_ap(cat_preds, cat_labels, ind2cat)\n        mean_ap = ap.mean()\n        if last_ap < mean_ap:\n            last_ap = mean_ap\n            torch.save(emotic_model, os.path.join(model_path, f'{mean_ap:.4f}_{e}_model_emotic1.pth'))\n            torch.save(model_context, os.path.join(model_path, f'{mean_ap:.4f}_{e}_model_context1.pth'))\n            torch.save(model_body, os.path.join(model_path, f'{mean_ap:.4f}_{e}_model_body1.pth'))\n\n        test_vad(cont_preds, cont_labels, ind2vad)\n        # thresholds = get_thresholds(cat_preds, cat_labels)\n        print('completed testing')\n        if e % 1 == 0:\n            print('epoch = %d validation loss = %.4f cat loss = %.4f cont loss = %.4f ' % (\n            e, running_loss / t_index, running_cat_loss / t_index, running_cont_loss / t_index))\n\n        val_writer.add_scalar('losses/total_loss', running_loss / t_index, e)\n        val_writer.add_scalar('losses/categorical_loss', running_cat_loss / t_index, e)\n        val_writer.add_scalar('losses/continuous_loss', running_cont_loss / t_index, e)\n\n        scheduler.step()\n\n    print('completed training')\n    emotic_model.to(\"cpu\")\n    model_context.to(\"cpu\")\n    model_body.to(\"cpu\")\n    torch.save(emotic_model, os.path.join(model_path, 'model_emotic1.pth'))\n    torch.save(model_context, os.path.join(model_path, 'model_context1.pth'))\n    torch.save(model_body, os.path.join(model_path, 'model_body1.pth'))\n    print('saved models')\n\n\ndef train_emotic(result_path, model_path, train_log_path, val_log_path, ind2cat, ind2vad, context_norm, body_norm,\n                 args):\n    ''' Prepare dataset, dataloders, models.\n    :param result_path: Directory path to save the results (val_predidictions mat object, val_thresholds npy object).\n    :param model_path: Directory path to load pretrained base models and save the models after training.\n    :param train_log_path: Directory path to save the training logs.\n    :param val_log_path: Directoty path to save the validation logs.\n    :param ind2cat: Dictionary converting integer index to categorical emotion.\n    :param ind2vad: Dictionary converting integer index to continuous emotion dimension (Valence, Arousal and Dominance).\n    :param context_norm: List containing mean and std values for context images.\n    :param body_norm: List containing mean and std values for body images.\n    :param args: Runtime arguments.\n    '''\n    # Load preprocessed data from npy files\n    train_context = np.load(os.path.join(args.data_path, 'train_context_arr.npy'))\n    train_body = np.load(os.path.join(args.data_path, 'train_body_arr.npy'))\n    train_cat = np.load(os.path.join(args.data_path, 'train_cat_arr.npy'))\n    train_cont = np.load(os.path.join(args.data_path, 'train_cont_arr.npy'))\n\n    val_context = np.load(os.path.join(args.data_path, 'val_context_arr.npy'))\n    val_body = np.load(os.path.join(args.data_path, 'val_body_arr.npy'))\n    val_cat = np.load(os.path.join(args.data_path, 'val_cat_arr.npy'))\n    val_cont = np.load(os.path.join(args.data_path, 'val_cont_arr.npy'))\n\n    print('train ', 'context ', train_context.shape, 'body', train_body.shape, 'cat ', train_cat.shape, 'cont',\n          train_cont.shape)\n    print('val ', 'context ', val_context.shape, 'body', val_body.shape, 'cat ', val_cat.shape, 'cont', val_cont.shape)\n\n    # Initialize Dataset and DataLoader\n    train_transform = transforms.Compose([transforms.ToPILImage(), transforms.RandomHorizontalFlip(),\n                                          transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),\n                                          transforms.ToTensor()])\n    test_transform = transforms.Compose([transforms.ToPILImage(), transforms.ToTensor()])\n\n    train_dataset = Emotic_PreDataset(train_context, train_body, train_cat, train_cont, train_transform, context_norm,\n                                      body_norm)\n    val_dataset = Emotic_PreDataset(val_context, val_body, val_cat, val_cont, test_transform, context_norm, body_norm)\n\n    train_loader = DataLoader(train_dataset, args.batch_size, shuffle=True)\n    val_loader = DataLoader(val_dataset, args.batch_size, shuffle=False)\n\n    print('train loader ', len(train_loader), 'val loader ', len(val_loader))\n\n    # Prepare models\n    model_context, model_body = prep_models(context_model=args.context_model, body_model=args.body_model,\n                                            model_dir=model_path)\n    use_model = 'skipnet'\n    if use_model != 'skipnet':\n        model_body = nn.Sequential(*(list(model_body.children())[:-1]))\n        emotic_model = Emotic(list(model_context.children())[-1].in_features,\n                              list(model_body.children())[-1].in_features)\n    else:\n        model_body = get_skipnet(num_classes=512, skip_w=8)\n\n        model_body = torch.load(r'debug_exp/models/0.3542_13_model_body1.pth',\n                                map_location=lambda storage, loc: storage)\n\n        # state_dict = {str.replace(k, 'module.', ''): v for k, v in model_weight['state_dict'].items()}\n        # model_body.load_state_dict(state_dict)\n\n        emotic_model = Emotic(list(model_context.children())[-1].in_features,\n                              list(model_body.children())[-1].out_features)\n\n        emotic_model = torch.load(r'debug_exp/models/0.3542_13_model_emotic1.pth',\n                                  map_location=lambda storage, loc: storage)\n\n        # state_dict = {str.replace(k, 'module.', ''): v for k, v in model_weight['state_dict'].items()}\n        # emotic_model.load_state_dict(state_dict)\n\n    model_context = nn.Sequential(*(list(model_context.children())[:-1]))\n\n    for param in emotic_model.parameters():\n        param.requires_grad = True\n    for param in model_context.parameters():\n        param.requires_grad = True\n    for param in model_body.parameters():\n        param.requires_grad = True\n\n    device = torch.device(\"cuda:%s\" % (str(args.gpu)) if torch.cuda.is_available() else \"cpu\")\n    # opt = optim.Adam((list(emotic_model.parameters()) + list(model_context.parameters()) + list(model_body.parameters())), lr=args.learning_rate, weight_decay=args.weight_decay)\n    # scheduler = StepLR(opt, step_size=7, gamma=0.1)\n\n    '''原opt = optim.SGD(\n        (list(emotic_model.parameters()) + list(model_context.parameters()) + list(model_body.parameters())),\n        momentum=0.9, lr=args.learning_rate, weight_decay=args.weight_decay)'''\n\n\n    #添加SAM\n    dataset = Emotic_PreDataset(args.batch_size, args.threads)\n    log = Log(log_each=10)\n    model = SkipNet(args.depth, args.width_factor).to(device)\n    base_optimizer = torch.optim.SGD  # define an optimizer for the \"sharpness-aware\" update\n    opt= SAM((list(emotic_model.parameters()) + list(model_context.parameters()) + list(model_body.parameters())),base_optimizer,\n             lr=0.1,  momentum=0.9)\n    scheduler = StepLR(opt, step_size=7, gamma=0.5)\n\n    for epoch in range(args.epochs):\n        model.train()\n        log.train(len_dataset=len(dataset.train))\n\n        for batch in dataset.train:\n            inputs, targets = (b.to(device) for b in batch)\n\n            # first forward-backward step\n            enable_running_stats(model)\n            predictions = model(inputs)\n            #原loss = smooth_crossentropy(predictions, targets, smoothing=args.label_smoothing)\n            disc_loss = DiscreteLoss(predictions, targets, DiscreteLoss=args.label_smoothing)\n            disc_loss.mean().backward()\n            opt.first_step(zero_grad=True)\n\n            # second forward-backward step\n            disable_running_stats(model)\n            DiscreteLoss(model(inputs), targets, smoothing=args.label_smoothing).mean().backward()\n            opt.second_step(zero_grad=True)\n\n            with torch.no_grad():\n                correct = torch.argmax(predictions.data, 1) == targets\n                log(model, disc_loss.cpu(), correct.cpu(), scheduler.lr())\n                scheduler(epoch)\n\n        model.eval()\n        log.eval(len_dataset=len(dataset.test))\n\n        with torch.no_grad():\n            for batch in dataset.test:\n                inputs, targets = (b.to(device) for b in batch)\n\n                predictions = model(inputs)\n\n                disc_loss= DiscreteLoss(predictions, targets)\n                correct = torch.argmax(predictions, 1) == targets\n                log(model, disc_loss.cpu(), correct.cpu())\n\n\n\n\n        #disc_loss = DiscreteLoss(args.discrete_loss_weight_type, device)\n\n                if args.continuous_loss_type == 'Smooth L1':\n                    cont_loss = ContinuousLoss_SL1()\n                else:\n                    cont_loss = ContinuousLoss_L2()\n                opt.first_step(zero_grad=True)\n\n                if args.continuous_loss_type == 'Smooth L1':\n                    cont_loss = ContinuousLoss_SL1()\n                else:\n                    cont_loss = ContinuousLoss_L2()\n    log.flush()\n\n    train_writer = SummaryWriter(train_log_path)\n    val_writer = SummaryWriter(val_log_path)\n\n    # training\n    train_data(opt, scheduler, [model_context, model_body, emotic_model], device, train_loader, val_loader, disc_loss,\n               cont_loss, train_writer, val_writer, model_path, args, ind2cat, ind2vad, len(val_dataset))\n    # validation\n    test_data([model_context, model_body, emotic_model], device, val_loader, ind2cat, ind2vad, len(val_dataset),\n              result_dir=result_path, test_type='val')\n","repo_name":"zhanglina94/EmoReg","sub_path":"train_sam.py","file_name":"train_sam.py","file_ext":"py","file_size_in_byte":15519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13933230997","text":"import requests\nfrom requests.exceptions import RequestException\nfrom flask import Flask, render_template, request\n\n\nfrom extractor import ContentExtractor\nfrom summarizer import summarize\n\napp = Flask(__name__)\nHEADERS = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 \\\n        (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef homepage():\n    if request.method == 'POST':\n        if \"url\" in request.form and request.form[\"url\"]:\n            # Checking if url is in the form.\n            url = request.form[\"url\"]\n            title = \"\"\n            summary = \"\"\n            images = []\n            try:\n                res = requests.get(url, headers=HEADERS)  # requesting the url.\n            except RequestException:\n                title = \"Failed to fetch the article content from given link.\"\n\n            if res.status_code == 200:\n                html_content = res.content\n\n                extractor = ContentExtractor(html_content)\n                content = extractor.content()\n                title = extractor.title()\n                images = extractor.images()\n\n                summary = summarize(title, str(content))\n\n            else:\n                title = \"Page not found.\"\n\n            return render_template(\"form.html\", data={\"summary\": summary, \"title\": title, \"images\": images})\n\n    return render_template(\"form.html\", data={\"summary\": \"\", \"title\": \"\"})\n\n\napp.run(debug=True)\n","repo_name":"tahirs95/pyteaserSummarizer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43372943388","text":"from sqlalchemy.sql import text\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy.engine import Result\nfrom tasks_database.db import get_session\n\n\n# Table: Employee\n# +-------------+---------+\n# | Column Name | Type    |\n# +-------------+---------+\n# | id          | int     |\n# | name        | varchar |\n# | department  | varchar |\n# | managerId   | int     |\n# +-------------+---------+\n# id is the primary key column for this table.\n# Each row of this table indicates the name of an employee, their department, and the id of their manager.\n# If managerId is null, then the employee does not have a manager.\n# No employee will be the manager of themselves.\n# ------------------------\n# Find the managers with at least five direct reports.\n# Return the result table in any order.\n\n# I'm actually done with this style of solution, it's too long and ineffective to use it in every case.\n# So I will just ust PgAdmin to create tables, and Insert data by Leetcode schemas and after that,\n# just paste here correct solution if I don't need to extra search and explain something hard.\n# It was needed for me to practice execute() after bootcamp, but now it's too much.\n\npostgresql_query: str = \"SELECT name \" \\\n                        \"FROM employee \" \\\n                        \"WHERE id IN (\" \\\n                        \"   SELECT managerId \" \\\n                        \"   FROM employee \" \\\n                        \"   GROUP BY managerId \" \\\n                        \"   HAVING COUNT(managerId) >=5 \" \\\n                        \");\"\ndb: Session = next(get_session())\ndata: Result = db.execute(text(postgresql_query))\nfor _ in data:\n    print(_)\n\n# Correct output:\n# +------+\n# | name |\n# +------+\n# | John |\n# +------+\n","repo_name":"Massprod/leetcode-testing","sub_path":"leetcode_problems/p570_managers_with_at_least_5_direct_reports.py","file_name":"p570_managers_with_at_least_5_direct_reports.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29327275169","text":"#Aprobación de créditos\nprint('Buenos días, para solicitar su crédito debe ingresar los siguientes datos')\n\ningreso = int(input('Ingreso mensual: $'))\naño = int(input('Año de nacimiento: '))\nedad = 2017-año\nhijos = int(input('Número de hijos:'))\npertenencia = int(input('Años de pertenencia al banco: '))\nestado = input('Estado civil (coloque una S si es soltero y una C si esta casado): ')\nzona = input('Zona de residencia (si vive en zona urbana coloque una U, si vive en zona rural una R): ')\n\nif pertenencia>10 and hijos>=2:\n    print('APROBADO')\nelif estado=='C' and hijos>3 and 45<edad<55:\n    print('APROBADO')\nelif ingreso>3500000 and pertenencia>5:\n    print('APROBADO')\nelif zona=='R' and estado=='C' and hijos<2:\n    print('APROBADO')\nelse:\n    print('RECHAZADO')\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_449fe3fbfe5ce0f1e755327e98eb0f7b.py","file_name":"hito1_ej3_449fe3fbfe5ce0f1e755327e98eb0f7b.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41971684727","text":"def get_puzzle_input():\n    with open('input.txt', 'r') as file:\n        lines = [line.strip() for line in file.readlines()]\n        width = len(lines[0])\n        return width, lines\n\n\ndef get_most_common(numbers, bit_pos):\n    bits = [n[bit_pos] for n in numbers]\n    return max(('1', '0'), key=lambda x: bits.count(x))\n\n\ndef get_least_common(numbers, bit_pos):\n    bits = [n[bit_pos] for n in numbers]\n    return min(('0', '1'), key=lambda x: bits.count(x))\n\n\ndef filter_numbers(numbers, width, bit_criteria):\n    numbers = list(numbers)\n\n    for bit_pos in range(width):\n        criteria = bit_criteria(numbers, bit_pos)\n        numbers = [n for n in numbers if n[bit_pos] == criteria]\n        if len(numbers) == 1:\n            break\n\n    return numbers[0]\n\n\ndef first_puzzle():\n    width, numbers = get_puzzle_input()\n\n    gamma_bits = [get_most_common(numbers, i) for i in range(width)]\n    gamma = int(''.join(gamma_bits), 2)\n\n    epsilon = gamma ^ int('1' * width, 2)\n\n    print('Puzzle 1 Answer:', gamma * epsilon)\n\n\ndef second_puzzle():\n    width, numbers = get_puzzle_input()\n\n    oxygen_generator = int(filter_numbers(numbers, width, get_most_common), 2)\n    co2_scrubber = int(filter_numbers(numbers, width, get_least_common), 2)\n\n    print('Puzzle 2 Answer:', oxygen_generator * co2_scrubber)\n\n\nif __name__ == '__main__':\n    first_puzzle()  # Puzzle 1 Answer: 3813416\n    second_puzzle()  # Puzzle 2 Answer: 2990784\n","repo_name":"simonbuescher/advent-of-code","sub_path":"2021/03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6463308077","text":"from flask import Blueprint, Response, request\nfrom ..model.model import db, Usuarios\nimport json\n\napp = Blueprint(\"usuarios\", __name__)\n\n\n@app.route('/')\ndef index():\n    usuarios = Usuarios.query.all()\n    result = [e.to_dict() for e in usuarios]\n    return Response(response=json.dumps(result), status=200, content_type=\"application/json\")\n\n\n@app.route('/add', methods=['POST'])\ndef add():\n    usuario = Usuarios(request.form['nome'])\n    db.session.add(usuario)\n    db.session.commit()\n    return Response(response=json.dumps({'status': 'success', 'msg': \"Usuario cadastrado com sucesso\"}), status=200,\n                    content_type=\"application/json\")\n\n\n@app.route('/edit/<int:id>', methods=['PUT', 'POST'])\ndef edit(id):\n    usuario = Usuarios.query.get(id)\n    if request.form['nome']:\n        usuario.nome = request.form['nome'];\n    if request.form['email']:\n        usuario.email = request.form['email']\n    if request.form['ativo']:\n        usuario.ativo = request.form['ativo']\n    db.session.commit()\n    return Response(response=json.dumps({'status': 'success', 'msg': 'Usuario Editado com sucesso'}), status=200, content_type=\"application/json\")\n\n\n@app.route('/delete/<int:id>', methods=['GET', 'DELETE'])\ndef delete(id):\n    disciplina = Usuarios.query.get(id)\n    db.session.delete(disciplina)\n    db.session.commit()\n    return Response(response=json.dumps({'status': 'success', 'msg': 'Usuario Excluido com sucesso'}), status=200, content_type=\"application/json\")\n","repo_name":"filipemorelli/aulas-flask","sub_path":"aula17/controller/usuarios.py","file_name":"usuarios.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"25426035370","text":"from django.contrib import admin\nfrom django.urls import path\nfrom django.urls.conf import include\nfrom . import views\n\napp_name = 'ticket_app'\nurlpatterns = [\n    path('new-ticket/', views.TicketCreateView.as_view(), name='new_ticket'),\n    path('tickets/', views.TicketListView.as_view(), name='all_tickets'),\n    path('ticket/<pk>/', views.TicketDetailView.as_view(), name='ticket_detail'),\n    path('update/<pk>/', views.TicketUpdateView.as_view(), name='ticket_update'),\n    path('update-<pk>/', views.TechTicketUpdateView.as_view(), name='tech_ticket_update'),\n    path('update/c/<pk>/', views.TechTicketCompleteUpdateView.as_view(), name='tech_ticket_complete_update'),\n    path('delete/<pk>', views.TicketDeleteView.as_view(), name='ticket_delete'),\n    path('new_type/', views.TicketTypeCreateView.as_view(), name='new_ticket_type'),\n    path('ticket-types/', views.TicketTypeListView.as_view(), name='ticket_type_list'),\n    path('delete-type/<pk>', views.TicketTypeDeleteView.as_view(), name='tickettype_delete'),\n    path('new-service/', views.ServiceProvidedCreateView.as_view(), name='new_serviceprovided'),\n    path('services/', views.ServiceProvidedListView.as_view(), name='serviceprovided_list'),\n    path('delete-service-<pk>/', views.ServiceProvidedDeleteView.as_view(), name='delete_serviceprovided'),\n    path('reports/', views.reports, name='reports')\n]","repo_name":"McFlyin619/service_ticket","sub_path":"ticket/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5711151844","text":"from collections import deque\n\ndef solution(s):\n    temp = 0\n    stackValue = 0\n    sorryList = [] \n    stack = deque()\n    check = False\n\n    for i in range(1, len(s) // 2 + 1):\n        check = True\n        length = i\n\n        tList = [s[k:k+length] for k in range(0, len(s), length)]\n\n        bar = tList[0]\n        for j in range(0, len(tList)):\n            if bar == tList[j]: temp += 1\n                \n            else : \n                bar = tList[j]\n                stack.append(temp)\n                temp = 1\n        stack.append(temp)\n\n        while (stack) :\n            tmpStack = stack.popleft()\n            if tmpStack > 1 :\n                stackValue = stackValue + len(str(tmpStack)) + i\n            else : \n                if stack : stackValue += i\n                else :\n                    tmpTlist = tList[len(tList)-1]\n                    stackValue += len(tmpTlist)\n\n        sorryList.append(stackValue)\n        stackValue = 0\n        temp = 0\n\n    if check != True : return 1\n    else : return min(sorryList)","repo_name":"Google-Developer-Student-Club-Hanyang/Algorithm-1-","sub_path":"윤하은/Lv.2/01. 문자열 압축.py","file_name":"01. 문자열 압축.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"31219754613","text":"#Hw_1 N!超過 M 的最小 N 值為何？\\\nmax = int(input(\"輸入最大值\"))\ntoatl_1 = 1\nn = 1\nwhile (toatl_1 <= max):\n    toatl_1 = toatl_1 * n\n    n = n + 1\nn = n - 1\nprint(f\"{n}! = {toatl_1} , 大於 {max}\")\n\n#Hw_2 偶數總和,奇數總和,3及7的倍數的總和&比較上續大小\nn = int(input(\"輸入一整數\"))\nprint(f\"你輸入的數為{n}\")\ntoatl_A=toatl_B=toatl_C = 0\npa = 2\nwhile (pa<=n):\n    toatl_A = toatl_A + pa\n    pa = pa + 2\nprint(f\"偶數合為{toatl_A}\")\npb = 1\nwhile (pb<=n):\n    toatl_B = toatl_B + pb\n    pb = pb + 2\nprint(f\"奇數合為{toatl_B}\")\npc = 21\nwhile (pc<=n):\n    toatl_C = toatl_C + pc\n    pc = pc + 21\nprint(f\"3及7的公倍數合為{toatl_C}\")\nif  toatl_A > toatl_B > toatl_C:\n    print(\"偶數合>奇數合>3及7的公倍數合\")\nelif toatl_A > toatl_C > toatl_B:\n    print(\"偶數合>3及7的公倍數合>奇數合\")\nelif toatl_B > toatl_A > toatl_C:\n    print(\"奇數合>偶數合>3及7的公倍數合\")\nelif toatl_B > toatl_C > toatl_A:\n    print(\"奇數合>3及7的公倍數合>偶數合\")\nelif toatl_C > toatl_A > toatl_B:\n    print(\"3及7的公倍數合>偶數合>奇數合\")\nelif toatl_C > toatl_B > toatl_A:\n    print(\"3及7的公倍數合>奇數合>偶數合\")\n\n#Hw_3 模擬帳號與密碼登入\nuser = input(\"輸入帳號\")\nkey = input(\"輸入密碼\")\nwhile (user != key):\n    print(\"帳號與密碼不一致!請重新輸入\")\n    user = input(\"輸入帳號\")\n    key = input(\"輸入密碼\")\nprint(\"帳號與密碼正確!登入成功\")\n\n#Hw_4 請輸入一正整數n，印出因數及判斷是否為質數\nn = int(input(\"輸入一整數\"))\ni = 1\ns = 0\nprint(f\"{n}的因數有: \",end=\"\")\nwhile (n >= i):\n    if n % i == 0:\n        print(f\"{i} \",end=\"\")\n        s = s + 1\n    i = i + 1\nprint(\"\")\nif s == 2:\n    print(f\"{n} 是質數\")\nelse:\n    print(f\"{n}不是質數\")\n","repo_name":"WangHow0219/Python-class_grade-1","sub_path":"上學期/作業/Hw迴圈_while.py","file_name":"Hw迴圈_while.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29467467209","text":"def buscarTodas(a,b):\n  list = []\n  for x in range(len(a)):\n    if(a[x] == b):\n      list.append(str(x))\n    s = \"\"\n    for x in list:\n      if x != list[-1]:\n        s = s + x + \" \"\n      else:\n        s = s + x\n  return s\n  \n\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej2/tema8_ej2_484165c64f6494015bcd73fe39852f23.py","file_name":"tema8_ej2_484165c64f6494015bcd73fe39852f23.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"556799693","text":"import ballstrikecount as bsc\nimport itertools\nimport os\n\ndata_dir = \"data\"\n\ndef combine_dicts(d1, d2):\n    \"\"\"\n    Combine two dictionaries, including all keys in both and summing the entries if a key exists in both dicts.\n    \"\"\"\n    for key, value in d2.items():\n        if isinstance(value, dict):\n            # Recursively combine nested dictionaries\n            d1[key] = combine_dicts(d1.get(key, {}), value)\n        else:\n            d1[key] = d1.get(key, 0) + value\n    return d1\n\ndef write_dict_to_file(keys, d, filename):\n    \"\"\"\n    Write a dictionary to a file, with each line in the format \"key1, key2, ..., keyn, value\"\n    \"\"\"\n    with open(filename, 'a') as f:\n        for key, value in d.items():\n            if isinstance(value, dict):\n                # Recursively write nested dictionaries\n                write_dict_to_file(key, value, filename)\n            else:\n                # Write the key-value pair to the file\n                keys_list = [keys] + [str(k) for k in key]\n                line = ','.join(keys_list) + ',' + str(value) + '\\n'\n                f.write(line)\n\ndata_dir = \"data\"\noutput_file = \"ballstrikevalueoncount.txt\"\nif os.path.exists(output_file):\n    os.remove(output_file)\n\ncounts = {}\ntotals = []\nfor filename in os.listdir(data_dir):\n    filepath = os.path.join(data_dir, filename)\n    if os.path.isfile(filepath) & (filepath.endswith(\".EVA\") | filepath.endswith(\".EVN\")):\n\n        # do something with the file\n        # print(\"Getting counts from \" + filename)\n        temp_counts, total = bsc.getCountNums(filepath)\n        counts = combine_dicts(counts, temp_counts)\n        totals.append(total)\nwrite_dict_to_file('', counts, output_file)\n","repo_name":"hawkinwd/ThesisDraft","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35892846932","text":"import inject\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import QtGui\n\n\nclass ToolbarWidget(QtWidgets.QFrame):\n    actionPopup = QtCore.pyqtSignal(object)\n    actionReload = QtCore.pyqtSignal(object)\n\n    @inject.params(config='config')\n    def __init__(self, config=None):\n        super(ToolbarWidget, self).__init__()\n        self.setSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed)\n\n        from .button import ToolbarButton\n\n        self.setLayout(QtWidgets.QHBoxLayout())\n        self.layout().setAlignment(Qt.AlignVCenter | Qt.AlignLeft)\n        self.layout().setSpacing(0)\n\n        self.popup = ToolbarButton(self, \"...\", QtGui.QIcon('icons/popup'))\n        self.popup.clicked.connect(self.onTogglePopup)\n        self.popup.clicked.connect(self.reload)\n        self.layout().addWidget(self.popup, -1)\n\n        self.frameless = ToolbarButton(self, \"Frameless\", QtGui.QIcon('icons/frameless'))\n        self.frameless.clicked.connect(self.onToggleFrameless)\n        self.frameless.clicked.connect(self.reload)\n        self.layout().addWidget(self.frameless, -1)\n\n        self.position = ToolbarButton(self, \"Fixed position\", QtGui.QIcon('icons/position'))\n        self.position.clicked.connect(self.onTogglePosition)\n        self.position.clicked.connect(self.reload)\n        self.layout().addWidget(self.position, -1)\n\n        self.size = ToolbarButton(self, \"Size\", QtGui.QIcon('icons/size'))\n        self.size.clicked.connect(self.onToggleSize)\n        self.size.clicked.connect(self.reload)\n        self.layout().addWidget(self.size, -1)\n\n        self.reload()\n\n    @inject.params(config='config')\n    def reload(self, event=None, config=None):\n        self.popup.setChecked(int(config.get('popup.enabled', 1)))\n        self.popup.setText('Enabled' if self.popup.isChecked() else 'Disabled')\n\n        self.frameless.setChecked(int(config.get('popup.frameless', 1)))\n        self.position.setChecked(int(config.get('popup.position', 1)))\n        self.size.setChecked(int(config.get('popup.size', 1)))\n\n    @inject.params(config='config')\n    def onTogglePopup(self, event=None, config=None):\n        config.set('popup.enabled', int(event))\n\n    @inject.params(config='config')\n    def onToggleFrameless(self, event=None, config=None):\n        config.set('popup.frameless', int(event))\n\n    @inject.params(config='config')\n    def onTogglePosition(self, event=None, config=None):\n        config.set('popup.position', int(event))\n\n    @inject.params(config='config')\n    def onToggleSize(self, event=None, config=None):\n        config.set('popup.size', int(event))\n","repo_name":"AlexWoroschilow/Dictionary.AppImage","sub_path":"app/src/modules/window_dictionary_popup/toolbar/panel.py","file_name":"panel.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3583464019","text":"import sys\nimport time\nimport datetime\nimport threading\n\nfrom pytz import timezone\n\n__shared_var_non_block = 0\n__shared_var_block = 0\n\ndef thread_acc_test(thread_number, acc_count:int, thread_lock=None):\n    global __shared_var_non_block, __shared_var_block\n    _time = time.time()\n    _currtime = datetime.datetime.now(timezone('Asia/Seoul'))\n    print(str(_currtime) + ', thread_acc_test, thread_number is \\'' + str(thread_number) + '\\', time: \\'' + str(_time) + '\\' [start]')\n\n    if thread_lock:\n        for _i in range(0, acc_count):\n            thread_lock.acquire()\n\n            #there is test codes, thread access\n            __shared_var_block += 1\n\n            thread_lock.release()\n    else:\n        for _i in range(0, acc_count):\n            #there is test codes, thread access\n            __shared_var_non_block += 1\n\n    print('[INFO]__shared_var_non_block= ' + str(__shared_var_non_block) + ', __shared_var_block= ' + str(__shared_var_block))\n\n    _time = time.time()\n    _currtime = datetime.datetime.now(timezone('Asia/Seoul'))\n    print(str(_currtime) + ', thread_acc_test, thread_number is \\'' + str(thread_number) + '\\', time: \\'' + str(_time) + '\\' [end]')\n\ndef main():\n    print('thread test program is started')\n    _thread_lock        = threading.Lock()\n\n    for _i in range(0,10):\n        _thread_acc_test_var    = threading.Thread(target=thread_acc_test, name=\"thread_acc_test_non_blocking\", args=(_i, 100000, None,))\n        _thread_acc_test_var.start()\n\n    for _i in range(10,20):\n        _thread_acc_test_var    = threading.Thread(target=thread_acc_test, name=\"thread_acc_test_blocking\", args=(_i, 100000, _thread_lock,))\n        _thread_acc_test_var.start()\n\n    print('thread test program will be closed')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"aaiaia/test_example","sub_path":"python/thread/lock/run_thread.py","file_name":"run_thread.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35970899932","text":"import pygame\n\nfrom constants import WINDOW_HEIGHT, WINDOW_WIDTH\nfrom screens.game_screen import GameScreen\nfrom screens.menu_screen import MenuScreen\nfrom screens.end_screen import EndScreen\n\ndef game_start():\n    pass\n    \n\ndef main():\n    # Sets score to 0, 0 and initializes pygame and display.\n    score = [0, 0]\n    pygame.init()\n    window = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))\n\n    # Creates game screen\n    game = MenuScreen(window)\n    menu_used = game.loop()\n    # Sets the rounds from the value set in the game\n    rounds = game.gamestate[0]\n\n    # Sets ai bool.\n    ai = game.gamestate[1]\n\n    # if game.practice is True, runs the practice games\n    if game.practice:\n        # Practice runs infinitely until quit.\n        while game.practice:\n            # Makes a new game window\n            game = GameScreen(window)\n            game.current_score = score\n            # Updates score, passed in externally to the game before the loop begins so as to keep the score values outside of the screen.\n            game.score1 = game.scorefont.render(f\"{score[1]}\", True, (125, 150, 245))\n            game.score2 = game.scorefont.render(f\"{score[0]}\", True, (125, 150, 245))\n            # Sets ai to the ai bool\n            game.ai = ai\n\n            # runs the game\n            result = game.loop()\n\n            # updates the score values based on the game results\n            score[0] += game.p1.score\n            score[1] += game.p2.score\n            print(\"Current Score\", score)\n\n\n\n    # If not practice, game runs by default\n    while game.close == False:\n        # Only runs the game to the round limit\n        for i in range(0, rounds):\n            if game.close:\n                break\n            # Makes a new game window if game wasnt toggled to close.\n            game = GameScreen(window)\n            # Updates scores and sets the text to be blit in the screen parameters\n            game.current_score = score\n            game.score1 = game.scorefont.render(f\"{score[1]}\", True, (125, 150, 245))\n            game.score2 = game.scorefont.render(f\"{score[0]}\", True, (125, 150, 245))\n\n            # sets ai bool\n            game.ai = ai\n            # runs game loop\n            result = game.loop()\n            # updates scores\n            score[0] += game.p1.score\n            score[1] += game.p2.score\n            print(\"Current Score\", score)\n        break\n    \n    # Runs end game screen if the game wasn't closed.\n    if game.close == False:\n        # Creates end scree \n        end = EndScreen(window)\n        \n        # Gets winner or tie and adds to a string\n        if score[0] > score[1]:\n            title_string = \"Player 2 \\nWinner!\"\n        elif score[0] < score[1]:\n            title_string = \"Player 1 \\nWinner!\"\n        else:\n            title_string= \"TIE!\"\n        \n        # Writes title string result to a parameter and adds those to the screen parameters score1 and score2\n        end.title = end.titlefont.render(title_string, True, (40, 86, 155))\n        end.text = title_string\n        end.score1 = end.scorefont.render(f\"P1\\nScore: {score[1]}\", True, (125, 150, 245))\n        end.score2 = end.scorefont.render(f\"P2\\nScore: {score[0]}\", True, (125, 150, 245))\n\n        # Runs the game loop\n        end_result = end.loop()\n\n\n\nif __name__ == \"__main__\":\n    # Loops the game forever until closed\n    while True:\n        main()\n","repo_name":"petervanderhook/pyong","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16591330289","text":"import psycopg2\nimport psycopg2.extras\nfrom urllib.parse import urlparse, uses_netloc\nimport configparser\n\n#Load database from config.ini\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\nconnection_string = config['database']['postgres_connection']\n\nuses_netloc.append(\"postgres\")\nurl = urlparse(connection_string)\n\nwith psycopg2.connect(database=url.path[1:], user=url.username, password=url.password, host=url.hostname, port=url.port) as conn:\n    with conn.cursor() as cursor:\n        dict_cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n        cursor.execute(\"SELECT ID, Name FROM Customers;\")\n        print(\"------ Customers -----------------------------------------------------\")\n        for name in cursor:\n            print(\"%5s %31s\" % (name[0], name[1]))\n\n        selection = input(\"Enter a customer ID:  \")\n        print(\"\\n------ Purchase History-----------------------------------------------\\n  Serial #  |   Price  |       Manufactuer\\n----------------------------------------------------------------------\")\n        \n        cursor.execute(\"SELECT Orders.serialnumber, Price, Manufacturers.Name FROM Orders \\\n        JOIN Machines ON Machines.serialNumber = Orders.serialNumber \\\n        Join Models ON Models.id = Machines.modelNumber \\\n        JOIN Manufacturers ON Manufacturers.id = Models.manufacturerid \\\n        WHERE Orders.customerid = %s ;\", (selection) )\n        for orders in cursor:\n            print(\"%10s %11s %19s\" % (orders[0], orders[1], orders[2] ) )\n        print(\"----------------------------------------------------------------------\")\n\n        cursor.execute(\"Select COUNT(Orders.serialnumber), Sum(Price) FROM Orders \\\n        JOIN Machines ON Machines.serialNumber = Orders.serialNumber \\\n        Join Models ON Models.id = Machines.modelNumber \\\n        JOIN Manufacturers ON Manufacturers.id = Models.manufacturerid \\\n        WHERE Orders.customerid = %s ;\", (selection) )\n        for totals in cursor:\n            print(\"Total machines purchased:%4s\\nTotal money spent:%16s\" %(totals[0], totals[1]))\n\n\n","repo_name":"Adam-J-Roberts/Database-Design","sub_path":"Labs/Lab 4/Lab4.py","file_name":"Lab4.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"270107676","text":"# Bronze 3_1009\n# 각 컴퓨터에 1번부터 10번까지의 번호를 부여하고,\n# 10대의 컴퓨터가 다음과 같은 방법으로 데이터들을 처리하기로 하였다.\n#\n# 1번 데이터는 1번 컴퓨터, 2번 데이터는 2번 컴퓨터, 3번 데이터는 3번 컴퓨터, ... ,\n# 10번 데이터는 10번 컴퓨터, 11번 데이터는 1번 컴퓨터, 12번 데이터는 2번 컴퓨터, ...\n#\n# 총 데이터의 개수는 항상 ab개의 형태로 주어진다.\n# 재용이는 문득 마지막 데이터가 처리될 컴퓨터의 번호가 궁금해졌다.\n# 이를 수행해주는 프로그램을 작성하라.\n\nT = int(input())\n\nfor _ in range(T):\n    a, b = map(int, input().split())\n    a = a % 10  # 마지막 컴퓨터를 찾는 것이므로 1의자리만 보면 된다.\n    if a == 0:  # a가 10의 배수인 경우는 무조건 1의 자리가 0이므로 마지막 컴퓨터는 10번째\n        print(10)\n    elif a == 1 or a == 5 or a == 6:  # 1의 자리가 1, 5, 6이면 제곱들의 자릿수는 1, 5, 6에서 변하지 않는다.\n        print(a)\n\n    # 1의 자리 수가 4와 9인 경우는 반복되는 값이 2개 이므로 제곱수를 2로 나누어서\n    # 홀수 승과 짝수 승을 비교하여 계산한다\n    # 4 -> (1)6 -> (2)4\n    # 9 -> (8)1 -> (72)9\n    elif a == 4 or a == 9:\n        b = b % 2\n        if b == 1:\n            print(a)\n        else:\n            print((a * a) % 10)\n    # 4개씩 싸이클을 돌 때 마다 자기 자신이 나오므로 나누었을 때 0이면 자기자신을 출력\n    else:\n        b %= 4\n        if b == 0:\n            print((a ** 4) % 10)\n        else:\n            print((a ** b) % 10)\n\n","repo_name":"chaerui7967/Today_I_Learned","sub_path":"Baekjoon/Distributed_210608.py","file_name":"Distributed_210608.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23142276522","text":"\"\"\"\nmakebamindex\n~~~~~~~~~~~~\n\n:Description: Use PySAM to make bam index\n\n\"\"\"\n# Use PySAM to make bam index\nimport pysam\nimport os\nimport sys\nimport logging\nimport coloredlogs\n\nlogger = logging.getLogger('iCallSV.makebamindex')\ncoloredlogs.install(level='DEBUG')\n\ndef MakeIndex(bamFile):\n    \"\"\"\n    This will make bam index if not there for a bam file using pysam.\n\n    :param str bamFile: Path to bam file\n    :return: None\n    :rtype: None\n\n    \"\"\"\n    logger.info(\"makebamindex: Trying to make index for bam file\")\n    if(os.path.isfile(bamFile)):\n        try:\n            pysam.index(bamFile)\n        except IndexError as err:\n            exception = \"Index error({0}): {1}\".format(err.errno, err.strerror)\n            logger.info(\"%s\", exception)\n        except IOError as err:\n            exception = \"I/O error({0}): {1}\".format(err.errno, err.strerror)\n            logger.info(\"%s\", exception)\n    else:\n        logger.info(\"Bam File %s does not exists\", bamFile)\n        sys.exit()\n","repo_name":"rhshah/iCallSV","sub_path":"iCallSV/makebamindex.py","file_name":"makebamindex.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"77"}
+{"seq_id":"12074037448","text":"def make_dictonary(file):\n    word_dict = {}\n    all_text = \"\"\n    count = 0\n    with open(file, \"r\") as file:\n        for line in file:\n            all_text += line\n        file.close()\n    txt_split = all_text.split(' ')\n    for i in txt_split:\n        count = 0\n        for k in txt_split:\n            if k == i:\n                count += 1\n        word_dict[i] = count\n    return word_dict\n\n\ndef main():\n    print(make_dictonary(\"text3.txt\"))\n\n\nmain()\n","repo_name":"AceCase499/PythonOne-offs","sub_path":"UniqueDictionary/UniqueDiction.py","file_name":"UniqueDiction.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37455169297","text":"import pandas as pd\nimport pickle\nimport os\nimport random\npd.set_option(\"display.max_colwidth\", None)\n\nfilename = \"topic_manual_annotated.csv\"\n\nwith open(\"Topic_modeling_results/pre_topic_modeling_all\", \"rb\") as fp:   # Unpickling\n    b = pickle.load(fp)\n\nif not os.path.exists(filename):\n    file = open(filename, 'w')\n    file.write(\"subject1\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\n    file.write(\"0\\n\")\nelse:\n\tfile = open(filename,\"a\")\n\ndata = pd.read_csv(filename)\n\n# load topics\nwith open(\"Topic_modeling_results/allGPUPDMM.topWords\") as file:\n    gpupdmm_topic = file.readlines()\ngpupdmm_topic = [i[:-2] for i in gpupdmm_topic]\n\nwith open(\"Topic_modeling_results/allGPUDMM.topWords\") as file:\n    gpudmm_topic = file.readlines()\ngpudmm_topic = [i[:-2] for i in gpudmm_topic]\n\nwith open(\"Topic_modeling_results/allLDA.topWords\") as file:\n    lda_topic = file.readlines()\nlda_topic = [i[:-2] for i in lda_topic]\n\nwith open(\"Topic_modeling_results/allLFDMM.topWords\") as file:\n    lfdmm_topic = file.readlines()\nlfdmm_topic = [i[:-2] for i in lfdmm_topic]\n\nwith open(\"Topic_modeling_results/allDMM.topWords\") as file:\n    dmm_topic = file.readlines()\ndmm_topic = [i[:-2] for i in dmm_topic]\n\nwith open(\"Topic_modeling_results/resultsBTM_topic_word_15.txt\") as file:\n    btm_topic = file.readlines()\nbtm_topic = [i[:-2] for i in btm_topic[1::2]]\n\nprint(data)\nprint(\"Existing names: \" + str(data.columns.values.tolist()))\nannotator_name = input(\"Please enter a new name: \")\n\nseed = input(\"Please enter a seed: \")\nrandom.seed(seed)\nrandom_list = random.sample(range(len(b)), 10)\nb_list = [b[i] for i in random_list]\nos.system('clear')\n\nlabel_list = []\nprint(\"BTM\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(btm_topic)):\n        print(\"({}) {}\". format(topic, btm_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_BTM\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\n\nlabel_list = []\nprint(\"GPUDMM\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(gpudmm_topic)):\n        print(\"({}) {}\". format(topic, gpudmm_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_GPUDMM\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\n\nlabel_list = []\nprint(\"GPU-PDMM\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(gpupdmm_topic)):\n        print(\"({}) {}\". format(topic, gpupdmm_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_GPUPDMM\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\n\nlabel_list = []\nprint(\"LDA\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(lda_topic)):\n        print(\"({}) {}\". format(topic, lda_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_LDA\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\n\nlabel_list = []\nprint(\"LFDMM\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(lfdmm_topic)):\n        print(\"({}) {}\". format(topic, lfdmm_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_LFDMM\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\n\nlabel_list = []\nprint(\"DMM\")\nfor i in range(len(b_list)):\n    print(b_list[i])\n    for topic in range(len(dmm_topic)):\n        print(\"({}) {}\". format(topic, dmm_topic[topic]))\n    label = int(input('Enter an integer between 0 and 14: '))\n    label_list.append((random_list[i], label))\n    os.system('clear')\ndata[annotator_name + \"_DMM\" + str(seed)] = label_list\ndata.to_csv(filename, index=False)\nprint(data)","repo_name":"KalleJanssen/user_feedback_performance","sub_path":"everything_data/TM_annotator.py","file_name":"TM_annotator.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43691700271","text":"from ftw.logo import _\nfrom ftw.logo.logoconfig import get_cachekey_from_blob\nfrom ftw.logo.logoconfig import IconConfigOverride\nfrom ftw.logo.logoconfig import LogoConfigOverride\nfrom hashlib import sha256\nfrom plone.dexterity.browser.view import DefaultView\nfrom plone.dexterity.browser import edit\nfrom plone.dexterity.utils import createContentInContainer\nfrom plone.dexterity.utils import iterSchemata\nfrom plone.namedfile.field import INamedBlobImage\nfrom plone.namedfile.field import NamedBlobImage\nfrom plone.supermodel import model\nfrom Products.Five.browser import BrowserView\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\nfrom zope.annotation.interfaces import IAnnotations\nfrom zope.component import adapter\nfrom zope.interface import Invalid\nfrom zope.lifecycleevent.interfaces import IObjectModifiedEvent\nfrom zope.schema import getFieldsInOrder\nimport transaction\n\n\nOVERRIDES_FIXED_ID = 'ftw-logo-overrides'\n# Annotations keys\nLOGO_OVERRIDES_KEY = 'ftw.logo.logo_overrides'\nICON_OVERRIDES_KEY = 'ftw.logo.icon_overrides'\nBLOB_CACHEKEY = 'ftw.logo.blob_cachekey'\nOVERRIDES_KEY_PATTERN = 'ftw.logo.{}_overrides'\n\n\ndef svg_file_only(value):\n    if value.contentType != 'image/svg+xml':\n        raise Invalid(\n            u\"This image must be a SVG file \" +\n            u\"({} supplied)\".format(value.contentType)\n        )\n    return True\n\n\ndef png_file_only(value):\n    if value.contentType != 'image/png':\n        raise Invalid(\n            u\"This image must be a PNG file \" +\n            u\"({} supplied)\".format(value.contentType)\n        )\n    return True\n\n\nclass IManualOverrides(model.Schema):\n\n    # Note: The field names are carefully constructed as one of:\n    #   logo_<logo scale name>, or\n    #   icon_<icon scale name>\n    # The form (manual_override.pt) depends on this to find the ZCML\n    # default image\n\n    logo_BASE = NamedBlobImage(\n        title=_(u\"SVG base logo\"),\n        description=_(u\"Overriding the base logo will generate an override \"\n                      u\"for BOTH logos below if not already set.\"),\n        required=False,\n        constraint=svg_file_only,\n    )\n\n    logo_LOGO = NamedBlobImage(\n        title=_(u\"Standard (desktop) logo (PNG)\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    logo_MOBILE_LOGO = NamedBlobImage(\n        title=_(u\"Mobile logo (PNG)\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_BASE = NamedBlobImage(\n        title=_(u\"SVG base icon\"),\n        description=_(u\"Overriding the base icon will generate an \"\n                      u\"override for ALL icons below if not already set.\"),\n        required=False,\n        constraint=svg_file_only,\n    )\n\n    icon_APPLE_TOUCH_ICON = NamedBlobImage(\n        title=_(u\"Apple touch icon\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_FAVICON_32X32 = NamedBlobImage(\n        title=_(u\"Favicon 32x32\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_FAVICON_16X16 = NamedBlobImage(\n        title=_(u\"Favicon 16x16\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_MSTILE_150X150 = NamedBlobImage(\n        title=_(u\"Mstile icon 150x150\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_ANDROID_192X192 = NamedBlobImage(\n        title=_(u\"Android icon 192x192\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_ANDROID_512X512 = NamedBlobImage(\n        title=_(u\"Android icon 512x512\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n    icon_FAVICON = NamedBlobImage(\n        title=_(u\"Favicon\"),\n        required=False,\n        constraint=png_file_only,\n    )\n\n\n@adapter(IManualOverrides, IObjectModifiedEvent)\ndef overrides_changed(override_object, event):\n    annotations = IAnnotations(override_object)\n    if override_object.logo_BASE:\n        annotations[LOGO_OVERRIDES_KEY] = LogoConfigOverride(override_object.logo_BASE)\n    if override_object.icon_BASE:\n        annotations[ICON_OVERRIDES_KEY] = IconConfigOverride(override_object.icon_BASE)\n\n    # calculate a cachekey combining all blob fields in this object\n    cachekey = sha256()\n    for schemata in iterSchemata(override_object):\n        for name, field in getFieldsInOrder(schemata):\n            try:\n                value = field.get(schemata(override_object))\n                if value is not field.missing_value and field.schema == INamedBlobImage:\n                    blobdata = field.get(override_object).data\n                    cachekey.update(\"|{}\".format(get_cachekey_from_blob(blobdata)))\n                    continue\n            except AttributeError:\n                pass\n            cachekey.update(\"|\")\n    annotations[BLOB_CACHEKEY] = cachekey.hexdigest()\n\n\nclass CreateOverridesIfReqdForm(BrowserView):\n    \"\"\"\n    Create IManualOverrides if it does not exist and redirect to it's edit form\n    \"\"\"\n    def __call__(self):\n        navroot = self.context\n        overridesItem = navroot.get(OVERRIDES_FIXED_ID)\n        if overridesItem is None:\n\n            overridesItem = createContentInContainer(\n                navroot,\n                'ftw.logo.ManualOverrides',\n                checkConstraints=False,\n                id=OVERRIDES_FIXED_ID,\n                title='Logo and Icon Overrides',\n                description='Manual overrides for the site logo(s) and icons',\n            )\n            transaction.get().commit()\n\n            self.request.response.redirect('{}/@@edit'.format(\n                overridesItem.absolute_url_path()\n            ))\n            return\n        self.request.response.redirect('{}/view'.format(\n            overridesItem.absolute_url_path()\n        ))\n        return\n\n\nclass ManualOverrideMixin(object):\n    def get_origin_for_scale(self, fullscalename):\n        \"\"\" Lookup HTML class for depending on authoritative origin for a particular scale \"\"\"\n        fieldname = fullscalename.replace('/', '_')\n        config_name = fullscalename.split('/')[0]\n        if getattr(self.context, fieldname):\n            return 'direct_override'\n        if getattr(self.context, '{}_BASE'.format(config_name)):\n            return 'scaled_base_override'\n        else:\n            return 'zcml'\n\n\nclass EditManualOverrideForm(edit.DefaultEditForm, ManualOverrideMixin):\n    label = _(u\"Edit Manual Logo and Icon Overrides\")\n    description = _(u\"Site logos and icons set in ZCML are shown in \" +\n                    u\"the left column. Overrides from this form are \" +\n                    u\"shown in the right column\")\n\n    template = ViewPageTemplateFile('manual_override.pt')\n\n    def get_origin_for_scale(self, fullscalename):\n        \"\"\" Lookup HTML class for depending on authoritative origin for a particular scale \"\"\"\n        fieldname = fullscalename.replace('/', '_')\n        config_name = fullscalename.split('/')[0]\n        if getattr(self.context, fieldname):\n            return 'direct_override'\n        if getattr(self.context, '{}_BASE'.format(config_name)):\n            return 'scaled_base_override'\n        else:\n            return 'zcml'\n\n\nclass ManualOverrideView(DefaultView, ManualOverrideMixin):\n    pass\n","repo_name":"4teamwork/ftw.logo","sub_path":"ftw/logo/manual_override.py","file_name":"manual_override.py","file_ext":"py","file_size_in_byte":7178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29399654999","text":"def palindromo(palabra):\n    lista=list(palabra)\n    nueva_l=lista.copy()\n    nueva_l.reverse()\n    if lista==nueva_l:\n        return True\n    else:\n        return False\nif __name__==\"__main__\":\n    print(palindromo(\"oso\"))\n    print(palindromo(\"dinosaurio\"))\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema11_ej1/tema11_ej1_c508c59f87b8f2d686875fc2a08f6982.py","file_name":"tema11_ej1_c508c59f87b8f2d686875fc2a08f6982.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38334724024","text":"#!/usr/local/bin/env python\n# -*- coding: utf-8 -*-\n\n\nd = {\n    'competition': 'U.S. Open Cup',\n    'team_data': ['Champion'],\n\n    'Champion': [\n        (2014, 'Seattle Sounders'),\n        (2013, 'DC United'),\n        (2012, 'Sporting Kansas City'),\n        (2011, 'Seattle Sounders'),\n        (2010, 'Seattle Sounders'),\n        (2009, 'Seattle Sounders'),\n        (2008, 'D.C. United'),\n        (2007, 'New England Revolution'),\n        (2006, 'Chicago Fire'),\n        (2005, 'Los Angeles Galaxy'),\n        (2004, 'Kansas City Wizards'),\n        (2003, 'Chicago Fire'),\n        (2002, 'Columbus Crew'),\n        (2001, 'Los Angeles Galaxy'),\n        (2000, 'Chicago Fire'),\n        (1999, 'Rochester Rhinos'),\n        (1998, 'Chicago Fire'),\n        (1997, 'Dallas Burn'),\n        (1996, 'D.C. United'),\n        (1995, 'Richmond Kickers'),\n        (1994, 'Greek-American A.C.'),\n        (1993, 'San Francisco C.D. Mexico'),\n        (1992, 'San Jose Oaks'),\n        (1991, 'Brooklyn Italians'),\n        (1990, 'A.A.C. Eagles'),\n        (1989, 'St. Petersburg Kickers'),\n        (1988, 'St. Louis Busch Seniors'),\n        (1987, 'Club España'),\n        (1986, 'St. Louis Kutis'),\n        (1985, 'Greek-American A.C.'),        \n        (1984, 'A.O. Krete'),\n        (1983, 'New York Pancyprian-Freedoms'),\n        (1982, 'New York Pancyprian-Freedoms'),\n        (1981, 'Maccabi Los Angeles'),\n        (1980, 'New York Pancyprian-Freedoms'),\n        (1979, 'Brooklyn Dodgers S.C.'),\n        (1978, 'Maccabi Los Angeles'),\n        (1977, 'Maccabi Los Angeles'),\n        (1976, 'San Francisco A.C.'),\n        (1975, 'Maccabi Los Angeles'),\n        (1974, 'Greek American AA'),\n        (1973, 'Maccabi Los Angeles'),        \n        (1972, 'Elizabeth S.C.'),\n        (1971, 'New York Hota'),\n        (1970, 'Elizabeth S.C.'),\n        (1969, 'Greek American AA'),\n        (1968, 'Greek American AA'),\n        (1967, 'Greek American AA'),\n        (1966, 'Philadelphia Ukrainians'),\n        (1965, 'New York Ukrainians'),\n        (1964, 'Los Angeles Kickers'),\n        (1963, 'Philadelphia Ukrainians'),\n        (1962, 'New York Hungaria'),\n        (1961, 'Philadelphia Ukrainians'),\n        (1960, 'Philadelphia Ukrainians'),        \n        (1959, 'McIlvane Canvasbacks'),\n        (1958, 'Los Angeles Kickers'),        \n        (1957, 'St. Louis Kutis'),\n        (1956, 'Harmarville Hurricanes'),\n        (1955, 'S.C. Eintracht'),\n        (1954, 'New York Americans'),\n        (1953, 'Chicago Falcons'),\n        (1952, 'Harmarville Hurricanes'),\n        (1951, 'German Hungarian'),\n        (1950, 'St. Louis Simpkins-Ford'),\n        (1949, 'Morgan Strasser'),\n        (1948, 'St. Louis Simpkins-Ford') ,       \n        (1947, 'Ponta Delgada'),\n        (1946, 'Chicago Viking'),\n        (1945, 'Brookhattan'),\n        (1944, 'Brooklyn Hispano'),\n        (1943, 'Brooklyn Hispano'),\n        (1942, 'Gallatin F.C.'),\n        (1941, 'Pawtucket F.C.'),\n        (1940, 'Baltimore S.C.',),\n        (1940, 'Chicago Sparta'),\n        (1939, 'Brooklyn St. Mary\\'s Celtic'),\n        (1938, 'Chicago Sparta'),\n        (1937, 'New York Americans'),\n        (1936, 'Uhrik Truckers'),\n        (1935, 'St. Louis Central Breweries F.C.'),\n        (1934, 'Stix, Baer and Fuller F.C.'),\n        (1933, 'Stix, Baer and Fuller F.C.'),\n        (1932, 'New Bedford Whalers'),\n        (1931, 'Fall River Marksmen'),\n        (1930, 'Fall River Marksmen'),\n        (1929, 'New York Hakoah'),\n        (1928, 'New York Nationals'),\n        (1927, 'Fall River Marksmen'),\n        (1926, 'Bethlehem Steel'),\n        (1925, 'Shawsheen Indians'),\n        (1924, 'Fall River Marksmen'),        \n        (1923, 'Paterson F.C.'),\n        (1922, 'St. Louis Scullin Steel'),\n        (1921, 'Brooklyn Robins Dry Dock'),\n        (1920, 'Ben Millers'),\n        (1919, 'Bethlehem Steel'),        \n        (1918, 'Bethlehem Steel'),        \n        (1917, 'Fall River Rovers'),\n        (1916, 'Bethlehem Steel'),        \n        (1915, 'Bethlehem Steel'),  \n        (1914, 'Brooklyn Field Club'),\n        ]\n    }\n","repo_name":"SoccerStatsUS/metadata","sub_path":"data/lists/awards/usopencup.py","file_name":"usopencup.py","file_ext":"py","file_size_in_byte":4066,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"74177845687","text":"from sphere import settings\nfrom .database_pacs import DatabasePACS\nfrom .dcm_file import DcmFile\nfrom sphere.logs.logs import LOG_DATABASE\n\n\nclass DcmManager:\n\n    def __init__(self, ds, db_pacs):\n        self.db_pacs = DatabasePACS() if db_pacs is None else db_pacs\n\n        if isinstance(ds, list) or isinstance(ds, tuple):\n            self.dataset = [dcm for dcm in ds]\n        elif ds.__class__ == DcmFile or ds.__class__ in DcmFile.__bases__:\n            self.dataset = [ds]\n        elif isinstance(ds, dict):\n            self.dataset = [\n                dcm for dcm in ds if DcmFile == dcm.__class__ or\n                type(dcm) in DcmFile.__bases__]\n        else:\n            LOG_DATABASE.exception('No data in ds')\n\n        try:\n            # Cast\n            for dcm in self.dataset:\n                dcm.__class__ = DcmFile\n        except Exception as exc:\n            LOG_DATABASE.exception(exc)\n\n    def add_metadata_dicom(self, instance_storage_metadata=None, queue_to_load=None):\n        \"\"\"\n        Add object metadata DICOM in queue.\n        It use the db connector defined in self.db_pacs.\n\n        :param instance_storage_metadata: The id of storage metadata included\n        :type instance_storage_metadata: int, None, optional\n        :param queue_to_load: dict objects of data base\n        :type queue_to_load: class queue.Queue, optional\n        \"\"\"\n        for dcm in self.dataset:\n            patient = self.db_pacs.PatientModel(**dcm.patient_metadata())\n            study = self.db_pacs.StudyModel(**dcm.study_metadata(), **{'patient': patient})\n            series = self.db_pacs.SeriesModel(**dcm.series_metadata(), **{'patient': patient, 'study': study})\n            #instance = self.db_pacs.FileStorageMetadataDicomModel(**dcm.instance_metadata(),\n            #                                      **{'patient': patient,\n            #                                         'study': study,\n            #                                         'series': series,\n            #                                         'storage_metadata': storage_metadata})\n\n            d = {**dcm.file_storage_metadata(),\n                 **{'patient': patient,\n                    'study': study,\n                    'series': series},\n                 **instance_storage_metadata}\n            instance = self.db_pacs.FileStorageMetadataDicomModel(**d)\n\n            dict_objects = {\n                'patient': patient,\n                'study': study,\n                'series': series,\n                'instance': instance}\n\n            queue_to_load.put(dict_objects)\n","repo_name":"aphp/SPHERE","sub_path":"sphere/dicmeta/dcm_manager.py","file_name":"dcm_manager.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"12760477027","text":"import json\nimport datetime\n\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\n\nfrom .models import *  # 載入所有models\nfrom .utils import cookieCart, cartData, guestOrder\n\n\n# Create your views here.\ndef store(request):\n    data = cartData(request)\n    cartItems = data['cartItems']\n\n    products = Product.objects.all()  # 搜尋到所有產品資料\n    context = {'products': products, 'cartItems': cartItems}\n    return render(request, 'store/store.html', context)\n\n\ndef cart(request):\n    data = cartData(request)\n    cartItems = data['cartItems']\n    items = data['items']\n    order = data['order']\n\n    context = {'items': items, 'order': order, 'cartItems': cartItems}\n    return render(request, 'store/cart.html', context)\n\n\ndef checkout(request):\n    data = cartData(request)\n    cartItems = data['cartItems']\n    items = data['items']\n    order = data['order']\n\n    context = {'items': items, 'order': order, 'cartItems': cartItems, 'shipping': False}\n    return render(request, 'store/checkout.html', context)\n\n\ndef updateItem(request):\n    data = json.loads(request.body)\n    productId = data['productId']  # 產品ID\n    action = data['action']  # add or remove\n\n    # print('Action:', action, 'ProductId:', productId)\n\n    customer = request.user.customer  # 取得用戶物件\n    product = Product.objects.get(id=productId)  # 取得此ID產品物件\n    order, created = Order.objects.get_or_create(customer=customer,\n                                                 complete=False)  # 搜尋如果不存在則建立，return(返回搜尋物件, 是否建立bool值)\n\n    orderItem, created = OrderItem.objects.get_or_create(order=order, product=product)\n    # print('orderItem:', orderItem, 'created:', created)\n\n    if action == 'add':  # 連結到前端，當使用者按下加入購物車\n        orderItem.quantity = (orderItem.quantity + 1)\n    elif action == 'remove':\n        orderItem.quantity = (orderItem.quantity - 1)\n\n    orderItem.save()\n\n    if orderItem.quantity <= 0:\n        orderItem.delete()\n\n    return JsonResponse('Item was added', safe=False)  # safe=False不偵錯\n\n\ndef processOrder(request):\n    # print('Date:', request.body)\n    transaction_id = datetime.datetime.now().timestamp()\n    data = json.loads(request.body)\n\n    if request.user.is_authenticated:\n        customer = request.user.customer\n        order, created = Order.objects.get_or_create(customer=customer,\n                                                     complete=False)\n\n    else:\n        customer, order = guestOrder(request, data)\n\n    total = float(data['form']['total'])\n    order.transaction_id = transaction_id\n\n    if total == float(order.get_cart_total):  # 前後端金額相同，防止從前端更改\n        order.complete = True  # 訂單狀態改成完成\n    order.save()\n\n    if order.shipping:\n        ShippingAddress.objects.create(\n            customer=customer,\n            order=order,\n            address=data['shipping']['address'],\n            city=data['shipping']['city'],\n            state=data['shipping']['state'],\n            zip_code=data['shipping']['zipcode'],\n        )\n\n    return JsonResponse(\"Payment submitted..\", safe=False)\n","repo_name":"joe94113/Django_Ecommerce","sub_path":"store/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26418271508","text":"import calendar\nimport datetime\nimport time\n\nfrom init import *\nfrom config import config\nimport requests\n\n\ndef LOG(message):\n    with open(\"log\", \"a\") as f:\n        f.write(message + \"\\n\")\n    print(message)\n\n\ndef GetPayerLength() -> int:\n    return requests.get(config[\"FAPI_url\"] + \"payers_length\").json()[\"payers_length\"]\n\n\ndef GetPayments(payment_ids: list[int]):\n    params = {\n        \"payment_ids\": list(set(payment_ids)),\n    }\n    return {\n        x[\"payment_id\"]: x\n        for x in requests.post(config[\"FAPI_url\"] + \"payments\", json=params).json()[\"payments\"]\n    }\n\n\ndef GetPayers(payer_ids: list[int]):\n    params = {\n        \"payer_ids\": payer_ids,\n    }\n    return requests.post(config[\"FAPI_url\"] + \"payers_by_payer_ids\", json=params).json()[\"payers\"]\n\n\ndef Now():\n    current_datetime = datetime.datetime.utcnow()\n    current_timetuple = current_datetime.utctimetuple()\n    current_timestamp = calendar.timegm(current_timetuple)\n    return current_timestamp\n\n\ndef GetAllowance(erc20_address: str, sender: str, recipient: str) -> int:\n    ERC20 = w3.eth.contract(address=erc20_address, abi=config[\"ERC20_abi\"])\n    return ERC20.functions.allowance(sender, recipient).call()\n\n\ndef PayCheck(payer, payment) -> bool:\n    return payment[\"payment_type\"] == \"SUBSCRIPTION\" and \\\n           (payer[\"payment_status\"] == \"ACTIVE\" and\n            payer[\"last_payment_timestamp\"] + payment[\"payment_period\"] <= Now() or\n            payer[\"payment_status\"] == \"TRIAL\" and\n            payer[\"last_payment_timestamp\"] + payment[\"trial_time\"] <=\n            Now() or payer[\"payment_status\"] == \"DECLINE\" and\n            GetAllowance(payment[\"ERC20_address\"], payer[\"billing_address\"], config[\"contract_address\"]) >=\n            payment[\"price\"])\n\n\ndef Execute(payment_ids: list[int], billing_ids: list[int]):\n    transaction = contract.functions.ExecuteSubscriptions(\n        payment_ids,\n        billing_ids\n    ).buildTransaction({\n        'chainId': config[\"chain_id\"],\n        'gasPrice': w3.toWei('2', 'gwei'),\n        'from': config[\"deployer\"],\n        'nonce': w3.eth.get_transaction_count(config[\"deployer\"])\n    })\n\n    signed_txn = w3.eth.account.sign_transaction(transaction, private_key=config[\"private_key\"])\n    send = w3.eth.send_raw_transaction(signed_txn.rawTransaction)\n    LOG(f\"ExecuteSubscription {send.hex()}\\n {list(zip(payment_ids, billing_ids))}\")\n    return send\n\n\nif __name__ == \"__main__\":\n    while True:\n        payers = GetPayers(list(range(1, GetPayerLength())))\n        payment_ids = [x[\"payment_id\"] for x in payers]\n        payments = GetPayments(payment_ids)\n\n        for_execution = []\n\n        for p in payers:\n            if PayCheck(p, payments[p[\"payment_id\"]]):\n                for_execution.append((p[\"payment_id\"], p[\"billing_id\"]))\n\n        if for_execution:\n            send = Execute([x[0] for x in for_execution], [x[1] for x in for_execution])\n            while True:\n                try:\n                    result = w3.eth.getTransactionReceipt(send)\n                    break\n                except:\n                    pass\n\n        time.sleep(1 * 60)\n","repo_name":"KRAVTSOV1506/CStribe","sub_path":"API/ICP.py","file_name":"ICP.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45896262914","text":"from django.conf.urls import *\nfrom produtos.models import Produto\n\nurlpatterns = patterns('pedidos.views',\n\turl(r'^$', 'carrinho', name='carrinho'),\n\turl(r'^config/$', 'carrinho_config', name='carrinho_config'),\n\turl(r'^json/$', 'carrinho_json', name='carrinho_json'),\t\n\turl(r'^adiciona/(?P<slug_produto>[-\\w]+)/$', 'adicionar_ao_carrinho', name='adicionar_ao_carrinho'),\n\turl(r'^remove/$', 'remove_do_carrinho', name='remove_do_carrinho'),\n\turl(r'^edita/$', 'edita_carrinho', name='edita_carrinho'),\n\turl(r'^finaliza/$', 'finaliza_pedido', name='finaliza_pedido')\n)\n","repo_name":"lcnandre/django-phonecommerce","sub_path":"pedidos/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40347476057","text":"# coding=utf-8\nfrom __future__ import absolute_import\n\nimport octoprint.plugin\nimport pyimgur\n\nclass ImgurTimelapsePlugin(octoprint.plugin.SettingsPlugin,\n                           octoprint.plugin.EventHandlerPlugin,\n                           octoprint.plugin.TemplatePlugin,\n                           octoprint.plugin.RestartNeedingPlugin):\n\n    def get_settings_defaults(self):\n        return dict(\n            client_id=None,\n            client_secret=None,\n            album=\"Octoprint\",\n            delete_after_upload=False\n        )\n\n    def get_settings_restricted_paths(self):\n        return dict(\n            admin=[['client_id'], ['client_secret'], ['album'], ]\n        )\n\n    def get_template_configs(self):\n        return [\n            dict(type='settings', custom_bindings=False, template='imgur_timelapse_settings.jinja2')\n        ]\n\n    def get_update_information(self):\n        return dict(\n            imgur_timelapse=dict(\n                displayName=\"Imgur Timelapse Plugin\",\n                displayVersion=self._plugin_version,\n\n                # version check: github repository\n                type=\"github_release\",\n                user=\"vincent\",\n                repo=\"OctoPrint-Imgur-Timelapse\",\n                current=self._plugin_version,\n\n                # update method: pip\n                pip=\"https://github.com/vincent/OctoPrint-Imgur-Timelapse/archive/{target_version}.zip\"\n            )\n        )\n\n    @property\n    def client_id(self):\n        return self._settings.get(['client_id'])\n\n    @property\n    def client_secret(self):\n        return self._settings.get(['client_secret'])\n\n    @property\n    def album(self):\n        return self._settings.get(['album'])\n\n    @property\n    def delete_after_upload(self):\n        return self._settings.get_boolean(['delete_after_upload'])\n\n    def on_event(self, event, payload):\n        from octoprint.events import Events\n        if event == Events.MOVIE_DONE:\n            self.upload_timelapse(payload)\n\n    def upload_timelapse(self, payload):\n        file_path = payload['movie']\n        file_name = payload['movie_basename']\n        if self.client_id and self.api_secret:\n            im = pyimgur.Imgur(client_id=self.client_id, client_secret=self.client_secret)\n        else:\n            self._logger.info('No Imgur API Tokens Defined! Cannot Upload Timelapse %s!' % file_name)\n            return\n\n        delete = self.delete_after_upload\n\n        self._logger.info('Uploading %s to Imgur...' % file_name)\n        try:\n            im.upload_image(path=file_path, title=file_name, album=self.album)\n            self._logger.info('Uploaded %s to Imgur!' % file_name)\n        except Exception as e:\n            delete = False\n            if e.user_message_text:\n                self._logger.info(e.user_message_text)\n            else:\n                self._logger.info(e)\n\n        if delete:\n            import os\n            self._logger.info('Deleting %s from local disk...' % file_name)\n            os.remove(file_path)\n            self._logger.info('Deleted %s from local disk.' % file_name)\n\n\n__plugin_name__ = \"Imgur Timelapse Plugin\"\n\n\ndef __plugin_load__():\n    global __plugin_implementation__\n    __plugin_implementation__ = ImgurTimelapsePlugin()\n\n    global __plugin_hooks__\n    __plugin_hooks__ = {\n        \"octoprint.plugin.softwareupdate.check_config\": __plugin_implementation__.get_update_information\n    }\n\n","repo_name":"vincent/OctoPrint-Imgur-Timelapse","sub_path":"octoprint_imgur_timelapse/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70360731130","text":"# 2019-2020 Programação II (LTI)\n# Grupo 82\n# 55373 José Almeida\n# 54975 Miguel Lages\n\nimport datetime\n\ndef updateTime(timeString, timeIncrement):\n        \"\"\"\n        Increments an amount of minutes to a string\n        Requires: timeString is a string representing the timestamp and timeIncrement is an integer representing the amount of minutes to increment on the given timestamp.\n        Ensures: returnal of a string representing the updated timestamp.\n        \"\"\"\n        if timeString[2]==\":\":\n            updatedTime = datetime.datetime.strptime(str(timeString), '%H:%M') + datetime.timedelta(minutes=int(timeIncrement))\n            updatedTime = str(updatedTime)[11:16]\n\n        elif timeString[2]==\"h\":\n            updatedTime = datetime.datetime.strptime(str(timeString), '%Hh%M') + datetime.timedelta(minutes=int(timeIncrement))\n            updatedTime = \"{}h{}\".format(str(updatedTime)[11:13],str(updatedTime)[14:16])\n        \n\n        return updatedTime\n\ndef updateDate(dateString, timeIncrement):\n        \"\"\"\n        Increments an amount of days to a string\n        Requires: dateString is a string representing the datestamp and timeIncrement is an integer representing the amount of days to increment on the given datestamp.\n        Ensures: returnal of a string representing the updated datestamp.\n        \"\"\"\n        dateString = dateString.strip()\n\n        if dateString[1]==\"-\" or dateString[2]==\"-\":\n            updatedDate = datetime.datetime.strptime(str(dateString), '%d-%m-%Y') + datetime.timedelta(days=int(timeIncrement))\n            updatedDate = \"{}-{}-{}\".format(str(updatedDate)[8:10],str(updatedDate)[5:7],str(updatedDate)[:4])\n        else:\n            updatedDate = datetime.datetime.strptime(str(dateString), '%Y-%m-%d') + datetime.timedelta(days=int(timeIncrement))\n            updatedDate = str(updatedDate)[:10]\n\n        if updatedDate[0]==\"0\":\n            updatedDate = updatedDate[1:]\n    \n        return updatedDate\n    \n\ndef hourToDatetime(timeString):\n    \"\"\"\n    Receives a timestamp and converts it to a datetime object for ease of comparison with other timestamps (datetime objects).\n    Requires: timeString argument is a string in the \"HOURS:MINUTES\" ('%H:%M') format or the \"HOURShMINUTES\" ('%Hh%M') format.\n    Ensures: returnal of the same timestamp but in the form of a datetime object.\n    \"\"\"\n\n    if timeString[2]==\":\":\n        hourDatetime = datetime.datetime.strptime(str(timeString), '%H:%M')\n\n    if timeString[2]==\"h\":\n        hourDatetime = datetime.datetime.strptime(str(timeString), '%Hh%M')\n\n    return hourDatetime\n\ndef dateToDatetime(dateString):\n    \"\"\"\n    Receives a datestamp and converts it to a datetime object for ease of comparison with other datestamps (datetime objects).\n    Requires: dateString argument is a string in the \"DAY-MONTH-YEAR\" ('%d-%m-%Y') format or the \"YEAR-MONTH-DAY\" ('%Y-%m-%d') format.\n    Ensures: returnal of the same timestamp but in the form of a datetime object.\n\n    \"\"\"\n    if dateString[1]==\"-\" or dateString[2]==\"-\":\n        dateDatetime = datetime.datetime.strptime(str(dateString), '%d-%m-%Y')\n    else:\n        dateDatetime = datetime.datetime.strptime(str(dateString), '%Y-%m-%d')\n    \n    return dateDatetime\n\ndef dateMax(date1, date2):\n    \"\"\"\n    Between two datestamps, returns the latter.\n    Requires: date1 and date2 are strings representing datestamps in the \"YEAR-MONTH-DAY\" ('%Y-%m-%d') format.\n    Ensures: returnal of the latter between the two datestamps as string.\n    \"\"\"\n\n    date1 = datetime.datetime.strptime(str(date1).strip(), '%Y-%m-%d')\n    date2 = datetime.datetime.strptime(str(date2).strip(), '%Y-%m-%d')\n\n    latter = max((date1, date2))\n    latter = str(latter)[:10]\n\n    return latter\n\ndef timeMax(time1, time2):\n    \"\"\"\n    Between two timestamps, returns the latter.\n    Requires: time1 and time2 are strings representing timestamps in the \"HOURS:MINUTES\" ('%H:%M') format.\n    Ensures: returnal of the latter between the two timestamps as string.\n\n    \"\"\"\n    time1 = datetime.datetime.strptime(time1, '%H:%M')\n    time2 = datetime.datetime.strptime(time2, '%H:%M')\n    latter = max((time1, time2))\n    latter = str(latter)[11:16]\n\n    return latter\n\ndef timeMin(time1, time2):\n    \"\"\"\n    Between two timestamps, returns the earliest.\n    Requires: time1 and time2 are strings representing timestamps in the \"HOURS:MINUTES\" ('%H:%M') format.\n    Ensures: returnal of the earliest between the two timestamps as string.\n    \"\"\"\n    time1 = datetime.datetime.strptime(time1, '%H:%M')\n    time2 = datetime.datetime.strptime(time2, '%H:%M')\n    earliest = min((time1, time2))\n    earliest = str(earliest)[11:16]\n\n    return earliest\n\n","repo_name":"zev4l/dronyDeliv2","sub_path":"timeTools.py","file_name":"timeTools.py","file_ext":"py","file_size_in_byte":4674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37659388651","text":"import sys\nfrom app import app,db\nfrom sqlalchemy.sql.expression import update\nfrom flask import render_template, flash, redirect, url_for, request\nfrom flask_sqlalchemy import sqlalchemy\nfrom flask_login import current_user, login_user, logout_user, login_required\nfrom wtforms.validators import ValidationError\n\n\nfrom app.forms import UserForm, LoginForm, editTAform, editfacform, CourseForm, ApplyForm, ForTForm, AssignForm\nfrom app.models import User, Course, Apply\n\n@app.route('/ForT', methods=[\"GET\", \"POST\"])\ndef ForT():\n    form = ForTForm()\n    return render_template('ForT.html', title='Faculty or TA', form=form)\n\n@app.route('/registerfac', methods=[\"GET\", \"POST\"])\ndef registerfac():\n    form = UserForm()\n    if form.is_submitted():   ## change to validate_on_submit()\n        user = User(tof=2, username=form.username.data, fullname=form.fullname.data, email = form.email.data, phonenum = form.phonenum.data, wsuid = form.wsuid.data,\n            major = None, cgpa = None, egd = None)\n        user.set_password(form.password.data)\n        try:\n            form.validate_username(form.username)\n        except ValidationError:\n            flash(\"Username already in use!\")\n            return redirect(url_for('registerfac'))\n        try:\n            form.validate_email(form.email)\n        except ValidationError:\n            flash(\"Email already in use!\")\n            return redirect(url_for('registerfac'))\n        try:\n            form.validate_phonenum(form.phonenum)\n        except ValidationError:\n            flash(\"Phone number already in use!\")\n            return redirect(url_for('registerfac'))\n        try:\n            form.validate_wsuid(form.wsuid)\n        except ValidationError:\n            flash(\"WSU ID already in use!\")\n            return redirect(url_for('registerfac'))\n        db.session.add(user)\n        db.session.commit()\n        flash(\"Congratulations, you now have a Faculty account on the TA Course Finder!\")\n        return redirect(url_for('login'))\n    return render_template('registerfac.html', title='Faculty Registration', form=form)\n\n@app.route('/registerTA', methods=[\"GET\", \"POST\"])\ndef registerTA():\n    form = UserForm()\n    if form.is_submitted():   ## change to validate_on_submit()\n        user = User(tof=1, username=form.username.data, fullname=form.fullname.data, email = form.email.data, phonenum = form.phonenum.data, wsuid = form.wsuid.data,\n            major = form.major.data, cgpa = form.cgpa.data, egd = form.egd.data)\n        user.set_password(form.password.data)\n        try:\n            form.validate_username(form.username)\n        except ValidationError:\n            flash(\"Username already in use!\")\n            return redirect(url_for('registerTA'))\n        try:\n            form.validate_email(form.email)\n        except ValidationError:\n            flash(\"Email already in use!\")\n            return redirect(url_for('registerTA'))\n        try:\n            form.validate_phonenum(form.phonenum)\n        except ValidationError:\n            flash(\"Phone number already in use!\")\n            return redirect(url_for('registerTA'))\n        try:\n            form.validate_wsuid(form.wsuid)\n        except ValidationError:\n            flash(\"WSU ID already in use!\")\n            return redirect(url_for('registerTA'))\n        db.session.add(user)        \n        db.session.commit()\n        flash(\"Congratulations, you now have a TA account on the TA Course Finder!\")\n        return redirect(url_for('login'))\n    return render_template('registerTA.html', title='TA Registration', form=form)\n\n\n@app.route(\"/login\", methods = [\"GET\", \"POST\"])\ndef login():\n    \n    if current_user.is_authenticated and current_user.tof == 1:\n        return redirect(url_for('index'))\n    if current_user.is_authenticated and current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = User.query.filter_by(username=form.username.data).first()\n        if user is None or not user.get_password(form.password.data):\n            flash('Invalid Username or Password!')\n            return redirect(url_for('index'))\n        login_user(user, remember=form.remember_me.data)\n        if current_user.tof == 1:\n            return redirect(url_for('index'))\n        elif current_user.tof == 2:\n            return redirect(url_for('Facindex'))\n    return render_template('login.html', title='Sign In', form=form)\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('login'))\n\n@app.route(\"/\", methods = [\"GET\", \"POST\"])\n@app.route(\"/index\", methods = [\"GET\", \"POST\"])\n@login_required\ndef index():\n    if current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n    elif current_user.status != 0:\n        return redirect(url_for('accepted'))\n    else:\n        form = ApplyForm()\n        reccoursess = Course.query.filter(Course.subject.like(current_user.major)) \n        coursess = Course.query.order_by(Course.title)\n        reccourses = reccoursess.all()\n        courses = coursess.all()\n\n        for app in current_user.appsinuser:\n            for crs in courses:\n                if app in crs.appsincourse:\n                    courses.remove(crs)\n        for app in current_user.appsinuser:\n            for crs in reccourses:\n                if app in crs.appsincourse:\n                    reccourses.remove(crs)\n\n        for crs in courses:\n            if crs.numslots < 1:\n                courses.remove(crs)\n        for crs in reccourses:\n            if crs.numslots < 1:\n                reccourses.remove(crs)\n\n        return render_template(\"index.html\", title='TA Homepage', courses=courses, reccourses=reccourses, form=form)\n\n@app.route('/accepted', methods = ['GET', 'POST'])\n@login_required\ndef accepted():\n    if current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n    elif current_user.status == 0:\n        return redirect(url_for('index'))\n    else:\n        crs = Course.query.filter_by(id=current_user.status).first()\n        return render_template('accepted.html', title='You are a TA!', crs=crs)\n\n@app.route('/leavecourse', methods = ['GET', 'POST'])\ndef leavecourse():\n    if current_user.status != 0:\n        current_user.status = 0\n        db.session.commit()\n        flash('You have removed yourself from the course, and can now apply for other courses.')\n        return redirect(url_for('index'))\n    else:\n        return redirect(url_for('index'))\n\n@app.route('/Facindex', methods = ['GET', 'POST'])\n@login_required\ndef Facindex():\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:    \n        form = ApplyForm()\n        idlist = []\n        for course in Course.query.filter_by(prof_id = current_user.id):  ## creates sqlObj of courses made by current professor\n            idlist.append(course.id)\n        applications = Apply.query.filter(Apply.course_id.in_(idlist))\n\n        return render_template('Facindex.html', title='Faculty Homepage', applications = applications.all(), form=form)\n\n@app.route('/apply/<course_id>', methods = ['GET', 'POST'])\ndef apply(course_id):\n    form = ApplyForm()\n    courseobj = Course.query.filter_by(id = course_id).first()\n    if form.validate_on_submit():\n        application = Apply(prevgrade = form.prevgrade.data, whentaken = form.whentaken.data, whenappfor = form.whenappfor.data,\n            statement = form.statement.data, _user = current_user, _course = courseobj)\n        db.session.add(application)\n        db.session.commit()\n        flash(\"You have successfully applied to a TA position!\")\n        return redirect(url_for('index'))\n    return render_template('apply.html', title='Application Page', courseobj=courseobj, form=form)\n\n@app.route('/viewapps', methods = ['GET', 'POST'])\n@login_required\ndef viewapps():\n    if current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n    else:\n        form = ApplyForm()\n        myapps = Apply.query.filter_by(user_id = current_user.id)\n    return render_template('viewapps.html', title='View Applications', myapps=myapps.all(), form=form)\n\n@app.route('/updateapp/<app_id>', methods = ['GET', 'POST'])\n@login_required\ndef updateapp(app_id):\n    if current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n    else:\n        form = ApplyForm()\n        app = Apply.query.filter_by(id = app_id).first()\n        if form.validate_on_submit():\n            app.prevgrade = form.prevgrade.data\n            app.whentaken = form.whentaken.data\n            app.whenappfor = form.whenappfor.data\n            app.statement = form.statement.data\n            db.session.commit()\n            flash(\"You have updated an application!\")\n            return redirect(url_for('viewapps'))\n        elif request.method == 'GET':\n            form.prevgrade.data = app.prevgrade\n            form.whentaken.data = app.whentaken\n            form.whenappfor.data = app.whenappfor\n            form.statement.data = app.statement\n    return render_template('updateapp.html', title='Update Application', app=app, form=form)\n\n@app.route('/deleteapp/<app_id>', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef deleteapp(app_id):\n    app = Apply.query.filter_by(id = app_id).first()\n    db.session.delete(app)\n    db.session.commit()\n    flash('You have successfully deleted your application.')\n    return redirect(url_for('viewapps'))\n\n@app.route('/accept/<app_id>', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef accept(app_id):\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        app = Apply.query.filter_by(id = app_id).first()\n        ta = User.query.filter_by(id = app._user.id).first()\n        crs = Course.query.filter_by(id = app._course.id).first()\n        ta.status = app._course.id\n        crs.numslots = crs.numslots - 1\n        db.session.delete(app)\n        db.session.commit()\n        for remapps in ta.appsinuser:\n            db.session.delete(remapps)\n            db.session.commit()\n        flash('You have accepted a TA to your course!')\n    return redirect(url_for('Facindex'))\n\n@app.route('/deny/<app_id>', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef deny(app_id):\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        app = Apply.query.filter_by(id = app_id).first()\n        db.session.delete(app)\n        db.session.commit()\n        flash('You have denied an application.')\n    return redirect(url_for('Facindex'))\n\n@app.route('/managecourses', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef managecourses():\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        students = User.query.filter(User.status == 0, User.tof == 1).all()\n        tas = User.query.filter(User.status>0).all()\n        courses = Course.query.filter_by(prof_id = current_user.id).all()\n        return render_template('managecourses.html', title='Manage Courses', students=students,\n            tas=tas, courses=courses)\n\n@app.route('/editcourse/<course_id>', methods = ['GET', 'POST'])\n@login_required\ndef editcourse(course_id):\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        form = CourseForm()\n        course = Course.query.filter_by(id=course_id).first()\n        if form.validate_on_submit():\n            course.subject = form.subject.data\n            course.number = form.number.data\n            course.title = form.title.data\n            course.gpareq = form.gpareq.data\n            course.prevgrade = form.prevgrade.data\n            course.numslots = form.numslots.data\n            course.addinfo = form.addinfo.data\n            db.session.commit()\n            flash(\"You have updated your course!\")\n            return redirect(url_for('managecourses'))\n        elif request.method == 'GET':\n            form.subject.data = course.subject\n            form.number.data = course.number\n            form.title.data = course.title\n            form.gpareq.data = course.gpareq\n            form.prevgrade.data = course.prevgrade\n            form.numslots.data = course.numslots\n            form.addinfo.data = course.addinfo\n        return render_template('editcourse.html', title='Edit Course', form=form, course=course)\n\n@app.route('/deletecourse/<course_id>', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef deletecourse(course_id):\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        course = Course.query.filter_by(id = course_id).first()\n        for apps in course.appsincourse:\n            db.session.delete(apps)\n            db.session.commit()\n        for ta in User.query.all():\n            if ta.status == course.id:\n                ta.status = 0\n        db.session.delete(course)\n        db.session.commit()\n        flash('You have successfully deleted this course, along with all applications within it.')\n        flash(\"If any TA's had been accepted to assist this course, they have been removed from the course as well.\")\n        return redirect(url_for('managecourses'))\n        \n@app.route('/assign/<user_id>', methods = ['GET', 'POST', 'DELETE'])\n@login_required\ndef assign(user_id):\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        form = AssignForm()\n        student = User.query.filter_by(id=user_id).first()\n        idlist = []\n        for crs in Course.query.filter_by(prof_id = current_user.id):\n            idlist.append(crs.id)\n        profcourses = Course.query.filter(Course.id.in_(idlist))\n        course = Course.query.filter_by(id = form.courseid.data).first()\n        if form.validate_on_submit():\n            if form.courseid.data in idlist:\n                student.status = form.courseid.data\n                course.numslots = course.numslots - 1\n                for apps in student.appsinuser:\n                    db.session.delete(apps)\n                    db.session.commit()\n                flash('You have assigned a student to your course!')\n                db.session.commit()\n                return redirect(url_for('managecourses'))\n            else:\n                flash('Must assign student to one of your courses!')\n                return redirect(url_for('managecourses'))\n        elif request.method == 'GET':\n            form.courseid.data = student.status\n    return render_template('assign.html', title='Assign TA', profcourses=profcourses.all(), student=student, form=form)\n\n\n@app.route(\"/editTA\", methods = [\"GET\", \"POST\"])\n@login_required\ndef editTA():\n    if current_user.tof == 2:\n        return redirect(url_for('Facindex'))\n    else:\n        form = editTAform()\n        if form.validate_on_submit():\n            current_user.username = form.username.data\n            current_user.fullname = form.fullname.data\n            current_user.phonenum = form.phonenum.data\n            current_user.major = form.major.data\n            current_user.cgpa = form.cgpa.data\n            current_user.egd = form.egd.data\n            db.session.commit()\n            flash(\"You have updated your profile!\")\n            return redirect(url_for('index'))\n        elif request.method == 'GET':\n            form.username.data = current_user.username\n            form.fullname.data = current_user.fullname\n            form.phonenum.data = current_user.phonenum\n            form.major.data = current_user.major\n            form.cgpa.data = current_user.cgpa\n            form.egd.data = current_user.egd\n        return render_template('editTA.html', title='Edit Profile', form=form)\n\n@app.route(\"/editfac\", methods = [\"GET\", \"POST\"])\n@login_required\ndef editfac():\n    if current_user.tof == 1:\n        return redirect(url_for('index'))\n    else:\n        form = editfacform()\n        if form.validate_on_submit():\n            current_user.username = form.username.data\n            current_user.fullname = form.fullname.data\n            current_user.phonenum = form.phonenum.data\n            db.session.commit()\n            flash(\"You have updated your profile!\")\n            return redirect(url_for('Facindex'))\n        elif request.method == 'GET':\n            form.username.data = current_user.username\n            form.fullname.data = current_user.fullname\n            form.phonenum.data = current_user.phonenum\n        return render_template('editfac.html', title='Edit Profile', form=form)\n\n@app.route(\"/addcourse\", methods = ['GET', 'POST'])\n@login_required\ndef addcourse():\n    form = CourseForm()\n    if form.validate_on_submit():\n        course = Course(subject = form.subject.data, number = form.number.data,\n            title = form.title.data, gpareq = form.gpareq.data,\n            prevgrade = form.prevgrade.data, numslots = form.numslots.data,\n            addinfo = form.addinfo.data, professor = current_user)\n        db.session.add(course)\n        db.session.commit()\n        flash(\"You have created a new course!\")\n        return redirect(url_for('Facindex'))\n    return render_template('addcourse.html', title='Create Course', form=form)\n","repo_name":"cjacobh/Assistant-Job-Finder-Web-Dev-Project","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":16982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5237265309","text":"from collections import defaultdict\nfrom typing import Optional\nfrom dataclasses import dataclass\nfrom dte_stand.data_structures.paths import GraphPathElement\nfrom typing import List\n\n@dataclass\nclass Bucket:\n    \"\"\"\n    Bucket represents and edge in the graph and its hash weight\n    Choosing a bucket means choosing its edge (GraphPathElement) as the nexthop in path\n    \"\"\"\n    edge: GraphPathElement\n    weight: int\n\n\ndef sort_buckets(b: Bucket):\n    \"\"\"\n    Function to use in python sort to sort buckets\n    Buckets are sorted by comparing its destination node's name as a string\n    \"\"\"\n    return b.edge.to_\n\n\nclass HashWeights:\n    \"\"\"\n    Class to store hash weights produced by the algorithm\n\n    Structure:\n    weights are different for each pair of source and destination node. For each such pair a dict is stored,\n        which contains resulted hash weights for each edge.\n    Edge is the key of this dict - it goes from \"start_node\" to \"neighbor_node\" and has index \"edge_index\".\n        Edge index is necessary because we are considering a MultiDiGraph\n        (edges can be duplicated and index is used to differentiate them)\n    The value of the dict is the weight of the edge\n    \"\"\"\n    def __init__(self):\n        self.weights: defaultdict[tuple[str, str], dict[tuple[str, int], Bucket]] = defaultdict(lambda: {})\n\n    def put(self, start_node: str, end_node: str, neighbor_node: str, edge_index: int, weight: int) -> None:\n        bucket = Bucket(\n                edge=GraphPathElement(\n                        from_=start_node,\n                        to_=neighbor_node,\n                        index=edge_index\n                ),\n                weight=weight\n        )\n        self.weights[(start_node, end_node)].update({(neighbor_node, edge_index): bucket})\n\n    def get_weight(self, start_node: str, end_node: str, neighbor_node: str, edge_index: int) -> Optional[int]:\n        try:\n            return self.weights.get((start_node, end_node), {}).get((neighbor_node, edge_index)).weight\n        except AttributeError:\n            # no such bucket\n            return None\n\n    def get_bucket_list(self, start_node: str, end_node: str) -> List[Bucket]:\n        \"\"\"\n        returns hash weights for a given pair of source and destination nodes, stored as buckets\n        buckets are sorted in an arbitrary way, for the sake of consistency\n        \"\"\"\n        return sorted(self.weights.get((start_node, end_node), {}).values(), key=sort_buckets)\n","repo_name":"OzerovaDaria/diplom","sub_path":"dte_stand/dte_stand/data_structures/hash_weights.py","file_name":"hash_weights.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"378377442","text":"from playwright.sync_api import Playwright, sync_playwright\n\n\ndef run(playwright: Playwright) -> None:\n    browser = playwright.chromium.launch(headless=False)\n    context = browser.new_context()\n\n    # Open new page\n    page = context.new_page()\n\n    # Go to https://momodel.cn/\n    page.goto(\"https://momodel.cn/\")\n\n    # Click text=数据集\n    # with page.expect_navigation(url=\"https://momodel.cn/dataset?&p=1\"):\n    with page.expect_navigation():\n        page.click(\"text=数据集\")\n    # assert page.url == \"https://momodel.cn/dataset\"\n\n    # Click [placeholder=\"搜索数据集\"]\n    page.click(\"[placeholder=\\\"搜索数据集\\\"]\")\n\n    # Fill [placeholder=\"搜索数据集\"]\n    # with page.expect_navigation(url=\"https://momodel.cn/dataset?&p=1&classification=all\"):\n    with page.expect_navigation():\n        page.fill(\"[placeholder=\\\"搜索数据集\\\"]\", \"\")\n\n    # Press CapsLock\n    page.press(\"[placeholder=\\\"搜索数据集\\\"]\", \"CapsLock\")\n\n    # Fill [placeholder=\"搜索数据集\"]\n    page.fill(\"[placeholder=\\\"搜索数据集\\\"]\", \"垃圾\")\n\n    # Click li[role=\"option\"]:has-text(\"垃圾分类\")\n    page.click(\"li[role=\\\"option\\\"]:has-text(\\\"垃圾分类\\\")\")\n    # assert page.url == \"https://momodel.cn/explore/6069143b1afd9438c4026345?type=dataset\"\n\n    # Click text=垃圾分类\n    page.click(\"text=垃圾分类\")\n\n    # Close page\n    page.close()\n\n    # ---------------------\n    context.close()\n    browser.close()\n\n\nwith sync_playwright() as playwright:\n    run(playwright)\n","repo_name":"WangYuJiee/Motest","sub_path":"ui_test/other/ MoSearch2.py","file_name":" MoSearch2.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"69826651130","text":"import os\nimport pickle\n\ntry:\n    os.remove('database.db')\n    os.remove('env.sh')\nexcept:\n    pass\n\nimport models\n\nos.system('msgfmt locale/ru/LC_MESSAGES/bot.po -o locale/ru/LC_MESSAGES/bot.mo')\n\n\ndef add(name: str, description: str = ''):\n    with open('env.sh', 'a+') as file:\n        file.write(f'export {name}='+input(f'{name}:\\n\\t' +\n                                        (f'description: {description}\\n\\t' if description else '')\n                                        + 'value: ') + '\\n')\n\n\ndef add_to_db(name: str, description: str = ''):\n    value = input(f'{name}:\\n\\t' + (f'description: {description}\\n\\t' if description else '') + 'value: ')\n    m = models.Setting()\n    m.name = name\n    m.value = pickle.dumps(value)\n    m.save()\n\n\nadd_to_db('qiwi_token', 'token from https://qiwi.com/api')\nadd_to_db('qiwi_phone', 'phone number from qiwi')\nadd('API_ID', 'api_id from https://my.telegram.org app page')\nadd('API_HASH', 'api_hash from https://my.telegram.org app page')\nadd('OWNER_ID', 'bot owner telegram id, from @userinfobot')\nadd('BOT_TOKEN', 'bot token from @botfather')\n","repo_name":"cofob/proxyBot","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40819337148","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\n        head = ListNode(None)\n        ans = head\n        \n        while True:\n            \n            if l1 is None:\n                ans.next = l2\n                return head.next\n            elif l2 is None:\n                ans.next = l1\n                return head.next\n            \n            if l1.val > l2.val:\n                ans.next = l2\n                l2 = l2.next\n            elif l1.val <= l2.val:\n                ans.next = l1\n                l1 = l1.next\n                \n            ans = ans.next\n","repo_name":"magic-of-gnu/leetcode_problems","sub_path":"group_a/e0021.py","file_name":"e0021.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72419707129","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport utool\n\n# DUPLICATE CODE, DELETE\nfrom wbia.plottool import draw_func2 as df2\nfrom wbia.plottool import interact_multi_image\n\n# import wbia\n\n\ndef _test_interact_multimage(imgpaths):\n    print('len: ', len(imgpaths))\n    bboxes_list = [[]] * len(imgpaths)\n\n    bboxes_list[0] = [(-200, -100, 400, 400)]\n    print(bboxes_list)\n    iteract_obj = interact_multi_image.MultiImageInteraction(\n        imgpaths, nPerPage=4, bboxes_list=bboxes_list\n    )\n    # def test_interact_multimage(imgpaths, gid_list=None, aids_list=None, bboxes_list=None):\n    #     img_list = imread_many(imgpaths)\n    #     iteract_obj = interact_multi_image.MultiImageInteraction(img_list +\n    #                                                              img_list,\n    #                                                              gid_list, aids_list, bboxes_list,\n    #                                                              nPerPage=6)\n    return iteract_obj\n\n\nif __name__ == '__main__':\n    TEST_IMAGES_URL = 'https://wildbookiarepository.azureedge.net/data/testdata.zip'\n    test_image_dir = utool.grab_zipped_url(TEST_IMAGES_URL, appname='utool')\n    imgpaths = utool.list_images(\n        test_image_dir, fullpath=True, recursive=False\n    )  # test image paths\n    iteract_obj = _test_interact_multimage(imgpaths)\n    exec(df2.present())\n","repo_name":"WildMeOrg/wildbook-ia","sub_path":"wbia/plottool/tests/test_interact_multi_image.py","file_name":"test_interact_multi_image.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"77"}
+{"seq_id":"28474577400","text":"import logging\nfrom enum import Enum\nfrom math import sqrt\n\nimport fbprophet\nimport matplotlib.pylab as plt\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import TimeSeriesSplit\nfrom sklearn.preprocessing import QuantileTransformer\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.tsa.vector_ar.vecm import coint_johansen\n\nfrom model_util import facebook_prophet_filter, Callbacks, lstm_conv1d_model\nfrom utility import normalize, find_index, \\\n    explore_data\n\n\nclass Constants(Enum):\n    TRAIN_TEST_SPLIT_RATIO = 0.8\n    CUTOFF_DATE = pd.to_datetime('2013-12-01')  # to trim data\n    FORECASTED_TEMPERATURE_FILE = 'data/temp_interpolated.pickle'  # to save/load interpolated result\n    FORECASTED_POWER_FILE = 'data/power_interpolated.pickle'  # to save/load interpolated result\n    DEFAULT_FUTURE_PERIODS = 4 * 24 * 30  # with freq = 15 * 60  that is  1 day\n    DEFAULT_FUTURE_FREQ = '15T'  # frequency of recording power, 15 minutes\n    # define model configuration\n    SARIMAX_ORDER = (7, 1, 7)\n    SARIMAX_SEASONAL_ORDER = (0, 0, 0, 0, 12)\n    # the following is for lstm model\n    SLIDING_WINDOW_SIZE_OR_TIME_STEPS = 30\n    RESAMPLING_FREQ = '8H'\n    SHIFT_IN_TIME_STEP_TO_PREDICT = 3 * 7  # corresponding to 24 hours in future\n    FEATURE_SIZE = 2\n    EPOCHS = 100\n    NEURONS = 30\n    INITIAL_EPOCH = 0\n    BATCH_SIZE = 20\n    MODEL_NAME = 'lstm'\n\n\nclass ColumnNames(Enum):\n    POWER = 'actual_kwh'\n    TEMPERATURE = 'actual_temperature'\n    LABEL = 'y'  # FbProphet & VAR use this name and it should be numeric\n    FORECAST = 'yhat'  # FbProphet & VAR use this name and it should be numeric\n    DATE = 'date'\n    TIME = 'time'\n    MONTH = 'month'\n    DAY_OF_WEEK = 'dow'\n    DATE_STAMP = 'ds'  # Facebook Prophet requires this name\n    FEATURES = [LABEL, TEMPERATURE]\n    LABELS = [LABEL]\n    ORIGINAL_FEATURES = [POWER, TEMPERATURE]\n\n\nclass Models(Enum):\n    PROPHET = fbprophet.Prophet(changepoint_prior_scale=0.10, yearly_seasonality=True)\n    LSTM = lstm_conv1d_model(Constants.NEURONS.value,\n                             (Constants.SLIDING_WINDOW_SIZE_OR_TIME_STEPS.value, Constants.FEATURE_SIZE.value))\n    ARIMA = sm.tsa.statespace.SARIMAX\n    VAR = VAR\n\n\nclass PowerForecaster:\n    \"\"\"\n    Check out the class spec at\n    https://docs.google.com/document/d/1-ceuHfJ2bNbgmKddLTUCS0HJ1juE5t0042Mts_yEUD8v\n    sample data is in\n    https://drive.google.com/uc?export=download&id=1z2MBYJ8k4M5J3udlFVc2d8opE_f-S4BK\n    \"\"\"\n\n    def __init__(self, df, model=Models.PROPHET,\n                 upsample_freq=None,\n                 train_test_split_ratio=Constants.TRAIN_TEST_SPLIT_RATIO.value,\n                 epochs=Constants.EPOCHS.value,\n                 initial_epoch=Constants.INITIAL_EPOCH.value,\n                 batch_size=Constants.BATCH_SIZE.value,\n                 sliding_window_size_or_time_steps=Constants.SLIDING_WINDOW_SIZE_OR_TIME_STEPS.value,\n                 do_shuffle=True):\n        logging.info(\"resample: {}. future_prediction: {}, epochs: {}, batch_size: {},\"\n                     \" window_size: {}, eurons: {}\"\n                     .format(Constants.RESAMPLING_FREQ.value\n                             , Constants.SHIFT_IN_TIME_STEP_TO_PREDICT.value\n                             , epochs\n                             , batch_size\n                             , sliding_window_size_or_time_steps\n                             , Constants.NEURONS.value\n                             ))\n        if logging.getLogger().isEnabledFor(logging.INFO):\n            explore_data(df)\n        # first step is to create a timestamp column as index to turn it to a TimeSeries data\n        df.index = pd.to_datetime(df[ColumnNames.DATE.value] + df[ColumnNames.TIME.value],\n                                  format='%Y-%m-%d%H:%M:%S', errors='raise')\n        if 'Unnamed: 0' in df.columns:\n            df.drop('Unnamed: 0', axis=1, inplace=True)\n\n        # keep a copy of original dataset for future comparison\n        self.df_original = df.copy()\n\n        # we interpolate temperature using prophet to use it in a multivariate forecast\n        temperature = ColumnNames.TEMPERATURE.value\n        interpolated_df = facebook_prophet_filter(df, temperature,\n                                                  Constants.FORECASTED_TEMPERATURE_FILE.value)\n        interpolated_df.index = df.index\n        df[[temperature]] = interpolated_df[[ColumnNames.FORECAST.value]]\n\n        # lets also interpolate missing kwh using facebook prophet (or we could simply drop them)\n\n        # now turn to kwh and make the format compatible with prophet\n        power = ColumnNames.POWER.value\n        interpolated_df = facebook_prophet_filter(df, power,\n                                                  Constants.FORECASTED_POWER_FILE.value)\n        interpolated_df.index = df.index\n        df[[power]] = interpolated_df[[ColumnNames.FORECAST.value]]\n\n        df = df.rename(columns={power: ColumnNames.LABEL.value})\n        df.drop(columns=[ColumnNames.DATE.value,\n                         ColumnNames.TIME.value,\n                         ColumnNames.DAY_OF_WEEK.value,\n                         ColumnNames.MONTH.value],\n                inplace=True\n                )\n        if upsample_freq is not None:\n            df = df.resample(upsample_freq).mean()\n\n        # for any regression or forecasting it is better to work with normalized data\n        self.transformer = QuantileTransformer()  # handle outliers better than MinMaxScalar\n        features = ColumnNames.FEATURES.value\n        normalized = normalize(df, features, transformer=self.transformer)\n\n        # we use the last part (after 12/1/2013) that doesnt have temperature for testing\n        cutoff_date = Constants.CUTOFF_DATE.value\n        self.df = normalized[normalized.index < cutoff_date]\n        self.testing = normalized[normalized.index >= cutoff_date]\n\n        self.df[ColumnNames.DATE_STAMP.value] = self.df.index\n        self.df_blocked = None\n        self.train_test_split_ratio = train_test_split_ratio\n        self.model_type = model\n        self.train_X, self.test_X, self.train_test_split_index = self.train_test_split(self.df[features])\n        self.train_y, self.test_y, _ = self.train_test_split(self.df[ColumnNames.LABELS.value])\n        self.model_fit = None\n        self.epochs = epochs\n        self.initial_epoch = initial_epoch\n        self.batch_size = batch_size\n        self.history = None\n        # following is defines in sliding_window\n        self.do_shuffle = do_shuffle\n        self.val_idx = None\n        self.shuffled_X = None\n        self.shuffled_y = None\n        self.train = None\n        self.label = None\n        self.train_size = None\n        self.val_size = None\n\n        if logging.getLogger().isEnabledFor(logging.INFO):\n            explore_data(self.df)\n\n    def train_test_split(self, df):\n        split_index = int(self.train_test_split_ratio * df.shape[0])\n        train = df.iloc[:split_index, :]\n        test = df.iloc[split_index:, :]\n        return train, test, split_index\n\n    def stationary_test(self):\n        dataset = self.test_y.dropna()\n        seasonal_dataset = sm.tsa.seasonal_decompose(dataset, freq=365)\n        fig = seasonal_dataset.plot()\n        fig.set_figheight(8)\n        fig.set_figwidth(15)\n        fig.show()\n\n        def p_value(dataset):\n            # ADF-test(Original-time-series)\n            dataset.dropna()\n            p_value = sm.tsa.adfuller(dataset, regression='ct')\n            logging.debug('p-value:{}'.format(p_value))\n            p_value = sm.tsa.adfuller(dataset, regression='c')\n            logging.debug('p-value:{}'.format(p_value))\n\n        p_value(self.train_y)\n        p_value(self.test_y)\n\n        # Test works for only 12 variables, check the eigenvalues\n        johnsen_test = coint_johansen(self.df[ColumnNames.FEATURES.value].dropna(), -1, 1).eig\n        return johnsen_test\n\n    def seasonal_prediction(self):\n        from statsmodels.tsa.api import SimpleExpSmoothing\n        y_hat_avg = self.test_y.copy()\n        fit2 = SimpleExpSmoothing(np.asarray(self.train_y['Count'])).fit(smoothing_level=0.6, optimized=False)\n        y_hat_avg['SES'] = fit2.forecast(len(self.test_y))\n        plt.figure(figsize=(16, 8))\n        plt.plot(self.train_y['Count'], label='Train')\n        plt.plot(self.test_y['Count'], label='Test')\n        plt.plot(y_hat_avg['SES'], label='SES')\n        plt.legend(loc='best')\n        plt.show()\n\n    def fit(self):\n        if self.model_type == Models.PROPHET:\n            self.prophet_fit()\n        elif self.model_type == Models.ARIMA:\n            self.arima_fit()\n        elif self.model_type == Models.VAR:\n            self.var_fit()\n        elif self.model_type == Models.LSTM:\n            self.lstm_fit()\n        else:\n            raise ValueError(\"{} is not defined\".format(self.model_type))\n\n    def evaluate(self):\n        self.loss_metrics = self.model_type.value.evaluate(\n            self.val_X,\n            self.val_y,\n            batch_size=self.batch_size,\n            verbose=0\n        )\n\n        logging.info(\"Metric names:{}\".format(self.model_type.value.metrics_names))\n        logging.info(\"Loss Metrics:{}\".format(self.loss_metrics))\n\n    def resultToDataFrame(self, data, start_index, end_index, do_scale_back=False):\n        label_column = ColumnNames.LABEL.value\n        df = self.df.iloc[start_index:end_index]\n        df[label_column] = data\n        if do_scale_back:\n            features = ColumnNames.FEATURES.value\n            df[features] = self.transformer.inverse_transform(df[features])\n        return df[[label_column]]\n\n\n    def block_after_date(self, start_block_date_st):\n        index, _ = find_index(self.df, start_block_date_st)\n        logging.debug(\"Index of block is {} with length of {}\".format(index, len(self.df) - index))\n        self.df_blocked = self.df.iloc[index:]\n        self.df_blocked.reindex()\n        logging.info(\"Blocked from {} to {} fromo training and validation\"\n                     .format(self.df_blocked.index[0], self.df_blocked.index[-1]))\n\n\n    def adjust_index_and_training_shift(self, start_date_in_labeling_st\n                                        , training_duration_in_frequency = None\n                                        , start_date_training_st = None\n                                        ):\n        logging.debug(\"Original range data of data: [{}-{}]\".format(self.df.index[0], self.df.index[-1]))\n        index_start_labeling, _ = find_index(self.df, start_date_in_labeling_st)\n        if start_date_training_st is not None:\n            index_start_training, _ = find_index(self.df, start_date_training_st)\n            if index_start_labeling < index_start_training:\n                raise ValueError(\"Labeling should be after training\")\n            self.shift = index_start_labeling - index_start_training\n        else:\n            index_start_training = 0\n            self.shift = index_start_labeling\n\n        if training_duration_in_frequency is None:\n            logging.info(\"Shift is set to be {}\".format(self.shift))\n        else:\n            final_index = index_start_training + training_duration_in_frequency + self.shift\n            logging.debug(\"start index: {}, final_index: {}\".format(index_start_training, final_index))\n            self.df = self.df.iloc[index_start_training:index_start_training + training_duration_in_frequency + self.shift]\n\n            logging.info(\"Shift is set to be {}, we picked the slice of [{} : {}] for trainig\".format(\n                self.shift, self.df.index[0]\n                , self.df.index[-1]\n            ))\n\n    def lstm_predict(self, model\n                     , start_date_to_predict_st=None\n                     , duration_in_freq = None\n                     , do_scale_back = False\n                     ):\n        X, true_y = self.get_whole()\n\n        if start_date_to_predict_st is not None:\n            y_index_i, _ = find_index(self.df, start_date_to_predict_st)\n            x_index_i = 0 if y_index_i <= self.shift else y_index_i - self.shift\n            x_index_f = x_index_i + duration_in_freq\n            y_index_f = y_index_i + duration_in_freq\n\n            logging.info(\"Predicting time slice [{} : {}] from [{} : {}]\".format(\n                self.df.index[y_index_i],self.df.index[y_index_f]\n                , self.df.index[x_index_i], self.df.index[x_index_f]\n            ))\n\n            X = X[x_index_i:x_index_f]\n            true_y = true_y[y_index_i:y_index_f]\n\n        predicted = model.predict(X)\n        logging.debug(\"Predicted Labels shape: {}\".format(predicted.shape))\n\n        plt.plot(predicted, 'r')\n        plt.plot(true_y, 'b')\n        plt.show()\n        df_predicted = self.resultToDataFrame(predicted, x_index_i + self.shift\n                                              , x_index_f + self.shift, do_scale_back)\n        return df_predicted\n\n    def scale_back(self, df_predicted, start_index, end_index):\n        label_column = ColumnNames.LABEL.value\n        features = ColumnNames.FEATURES.value\n        df = self.df[features].iloc[start_index:end_index]\n        df[label_column] = df_predicted[label_column]\n        scaled_predicted = self.transformer.inverse_transform(df[features])\n        df[features] = scaled_predicted\n        return df\n\n    def prophet_fit(self):\n        past = self.train_y.copy()\n        past[ColumnNames.DATE_STAMP.value] = self.train_y.index\n        self.model_type.value.fit(past)\n\n    def arima_fit(self):\n        model = sm.tsa.statespace.SARIMAX(self.train_y,\n                                          order=Constants.SARIMAX_ORDER.value,\n                                          seasonal_order=Constants.SARIMAX_SEASONAL_ORDER.value)\n        # ,enforce_stationarity=False, enforce_invertibility=False, freq='15T')\n        logging.debug(\"SARIMAX fitting ....\")\n        self.model_fit = self.model_type.value.fit()\n        self.model_fit.summary()\n        logging.debug(\"SARIMAX forecast\", self.model_fit.forecast())\n\n    def var_fit(self):\n        logging.debug(\"making VAR model\")\n        model = VAR(endog=self.train_X[ColumnNames.FEATURES.value].dropna())\n        logging.debug(\"VAR fitting ....\")\n        self.model_fit = model.fit()\n        print(self.model_fit.summary())\n\n    def lstm_fit(self):\n        if logging.getLogger().isEnabledFor(logging.INFO):\n            print(self.model_type.value.summary())\n\n        callbacks = Callbacks(Constants.MODEL_NAME.value, self.batch_size, self.epochs)\n        X, y = self.get_shuff_train_label()\n\n        self.history = self.model_type.value.fit(\n            X,\n            y,\n            epochs=self.epochs,\n            batch_size=self.batch_size,\n            validation_split=0.35,\n            verbose=0,\n            callbacks=callbacks.getDefaultCallbacks(),\n            initial_epoch=self.initial_epoch,\n\n        )\n        logging.debug(\"history of performance:{}\".format(self.history.history))\n\n    def predict(self, feature_set=None):\n        future = feature_set if feature_set is not None \\\n            else Constants.DEFAULT_FUTURE_PERIODS.value\n        if self.model_type == Models.PROPHET:\n            self.future = self.model_type.value.make_future_dataframe(periods=future,\n                                                                      freq=Constants.DEFAULT_FUTURE_FREQ.value,\n                                                                      include_history=False)\n\n        if self.model_type == Models.PROPHET:\n            predicted = self.model_type.value.predict(self.future)\n            predicted[ColumnNames.LABEL.value] = predicted[ColumnNames.FORECAST.value]\n        elif self.model_type == Models.ARIMA:\n            predicted = self.arima_predict(future)\n        elif self.model_type == Models.VAR:\n            predicted = self.var_predict(future)\n        elif self.model_type == Models.LSTM:\n            return self.lstm_predict(self.model.value,\n                                     start_date_to_predict_st=\"2013-6-01\",\n                                                    duration_in_freq=3 * 30)\n        else:\n            raise ValueError(\"{} is not defined\".format(self.model_type))\n        df_predicted = self.resultToDataFrame(predicted, self.train_test_split_index\n                                              , self.train_test_split_index + len(predicted))\n\n        return df_predicted\n\n    def arima_predict(self, future):\n        end = str(self.train_y.index[-1])\n        start = str(self.train_y.index[-future])\n        print(start, end)\n        predicted = self.model_fit.predict(start=start[:10], end=end[:10], dynamic=True)\n        return predicted\n\n    def var_predict(self, future):\n        predicted_array = self.model_fit.forecast(self.model_fit.y, future)\n        predicted = pd.DataFrame(predicted_array)\n        predicted.columns = ColumnNames.FEATURES.value\n        predicted.index = self.test_y.index[:len(predicted)]\n        return predicted\n\n    def sliding_window(self):\n        # Generate the data matrix\n        length0 = self.df.shape[0]\n        window_size = Constants.SLIDING_WINDOW_SIZE_OR_TIME_STEPS.value\n        future_time_steps = Constants.SHIFT_IN_TIME_STEP_TO_PREDICT.value\n        features_column = ColumnNames.FEATURES.value\n        label_column = ColumnNames.LABEL.value\n\n        sliding_window_feature = np.zeros((length0 - window_size - future_time_steps,\n                                           window_size, len(features_column)))\n        sliding_window_label = np.zeros((length0 - window_size - future_time_steps, 1))\n\n        for counter in range(length0 - window_size - future_time_steps):\n            sliding_window_label[counter, :] = self.df[label_column][counter + window_size + future_time_steps]\n\n        for counter in range(length0 - window_size - future_time_steps):\n            sliding_window_feature[counter, :] = self.df[features_column][\n                                                 counter: counter + window_size]\n        if self.do_shuffle:\n            logging.debug('Random shuffeling')\n        length = sliding_window_feature.shape[0]\n        if self.df_blocked is not None:\n            length -= len(self.df_blocked)\n            logging.info(\"length of data reduced by {} due to blocking. The last date is {}\"\n                         .format(len(self.df_blocked), self.df.index[length]))\n\n        logging.debug(\"sliding window length: {}\".format(length))\n\n        split_ratio = Constants.TRAIN_TEST_SPLIT_RATIO.value\n        idx = np.random.choice(length, length, replace=False) if self.do_shuffle else np.arange(length)\n        self.val_idx = idx[int(split_ratio * length):]\n\n        feature_window_shuffled = sliding_window_feature[idx, :]\n        label_window_shuffled = sliding_window_label[idx, :]\n\n        self.shuffled_X = feature_window_shuffled\n        self.shuffled_y = label_window_shuffled\n        self.train = sliding_window_feature\n        self.label = sliding_window_label\n\n        self.train_X = self.shuffled_X[:int(split_ratio * length), :]\n        self.train_y = self.shuffled_y[:int(split_ratio * length), :]\n        self.train_size = int(split_ratio * length)\n\n        self.val_X = self.shuffled_X[int(split_ratio * length):, :]\n        self.val_y = self.shuffled_y[int(split_ratio * length):, :]\n        self.val_size = length - self.train_size\n\n    def get_shuff_train_label(self):\n        X = self.shuffled_X  # np.expand_dims(self.shuffled_X, axis=-1)\n        Y = self.shuffled_y\n        return X, Y\n\n    def evaluate_performance(self):\n        # make a prediction\n        X = self.test_X  # np.expand_dims(self.test_X, axis=-1)\n        yhat = self.model_type.value.predict(X)\n        test_X = self.test_X.reshape((self.test_X.shape[0], self.test_X.shape[2]))\n        # invert scaling for forecast\n        inv_yhat = pd.concatenate((yhat, test_X[:, 1:]), axis=1)\n        inv_yhat = self.transformer.inverse_transform(inv_yhat)\n        inv_yhat = inv_yhat[:, 0]\n        # invert scaling for actual\n        test_y = self.test_y.reshape((len(self.test_y), 1))\n        inv_y = pd.concatenate((test_y, test_X[:, 1:]), axis=1)\n        inv_y = self.transformer.inverse_transform(inv_y)\n        inv_y = inv_y[:, 0]\n        # calculate RMSE\n        rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n        logging.debug('Test RMSE: %.3f' % rmse)\n\n    def plot_future(self, predicted):\n        self.model_type.value.plot(predicted, xlabel='Date', ylabel='KWH')\n        self.model_type.value.plot_components(predicted)\n\n    #        by_dow.plot(xticks=ticks, style=style, title='Averaged on Days of the Week')\n    #        plt.show()\n\n    def visual_inspection(self):\n        style = [':', '--', '-']\n        pd.plotting.register_matplotlib_converters()\n        df = self.df\n\n        self.df_original[ColumnNames.ORIGINAL_FEATURES.value].plot(style=style, title='Original Data')\n        plt.show()\n\n        self.df[ColumnNames.FEATURES.value].plot(style=style, title='Normalized Data')\n        plt.show()\n\n        sampled = df.resample('M').sum()[ColumnNames.FEATURES.value]\n        sampled.plot(style=style, title='Aggregated Monthly')\n        plt.show()\n\n        sampled = df.resample('W').sum()[ColumnNames.FEATURES.value]\n        sampled.plot(style=style, title='Aggregated Weekly')\n        plt.show()\n\n        sampled = df.resample('D').sum()[ColumnNames.FEATURES.value]\n        sampled.rolling(30, center=True).sum().plot(style=style, title='Aggregated Daily')\n        plt.show()\n\n        by_time = df.groupby(by=df.index.time).mean()[ColumnNames.FEATURES.value]\n        ticks = 4 * 60 * 60 * np.arange(6)\n        by_time.plot(xticks=ticks, style=style, title='Averaged Hourly')\n        plt.show()\n\n        days = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n\n        def tick(x):\n            if x % 24 == 12:\n                return days[int(x) // 24]\n            else:\n                return \"\"\n\n        #        ax.xaxis.set_major_formatter(NullFormatter())\n        #        ax.xaxis.set_minor_formatter(FuncFormatter(tick))\n        #        ax.tick_params(which=\"major\", axis=\"x\", length=10, width=1.5)\n\n        #by_dow = df.groupby(by=df.dow).mean()[ColumnNames.FEATURES.value]\n        #ticks = 4 * 60 * 60 * np.arange(6)\n\n    def plot_prediction(self, start_index, end_index):\n        style = [':', '--', '-']\n        pd.plotting.register_matplotlib_converters()\n        label_column = ColumnNames.LABELS.value\n        # import pdb; pdb.set_trace()\n\n        t = self.train.index.iloc[start_index:end_index]\n        X = self.train.iloc[start_index: end_index]\n        true_y = self.label.iloc[start_index, end_index]\n        y = self.model_type.value.predict(X)\n\n        plt.plot(t, y, true_y, style=style)\n        plt.show()\n\n    def plot_history(self):\n        plt.plot(np.arange(self.epochs - self.initial_epoch),\n                 self.history.history['loss'], label='train')\n        plt.plot(np.arange(self.epochs - self.initial_epoch),\n                 self.history.history['val_loss'], label='validation')\n        plt.legend()\n        plt.title('model accuracy')\n        plt.ylabel('accuracy')\n        plt.xlabel('epoch')\n        plt.legend(['train', 'test'], loc='upper left')\n        plt.show()\n\n    def get_next_train_batch(self):\n        # getting the next train batch\n        if self.pointer + self.batchsize >= self.train_size:\n            end = self.train_size\n            start = self.pointer\n            self.pointer = 0\n            self.epoch += 1\n        else:\n            end = self.pointer + self.batchsize\n            start = self.pointer\n            self.pointer += self.batchsize\n        X = self.train_data[start:end, :]\n        Y = self.train_label[start:end, :]\n        return X, Y\n\n    def get_val(self):\n        X = np.expand_dims(self.val_data, axis=-1)\n\n        return X, self.val_label[:]\n\n    def get_whole(self):\n        # get whole, for validation set\n        X = self.train[:, :]  # np.expand_dims(self.train[:, :], axis=-1)\n        Y = self.label[:, :]\n        return X, Y\n\n    def reset(self):\n        self.pointer = 0\n        self.epoch = 0\n\n\nclass ModelEvaluator:\n\n    def cross_k_validation(self, model):\n        tscv = TimeSeriesSplit(n_splits=10)\n        for train_index, test_index in tscv.split(self.df_normalized):\n            print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n            y_column = self.df_normalized[ColumnNames.LABEL.value]\n            y_train, y_test = y_column[train_index], y_column[test_index]\n            self.model.value.fit(pd.DataFrame(y_train))\n            forecast = self.model.value.forecast(None)\n            logging.debug(y_test.shape)\n            logging.debug(forecast.shape)\n            mean_squared_error(y_test, forecast)\n            plt.plot(y_test, 'g')\n            plt.plot(forecast, 'b')\n            size = len(y_test)\n            plt.xlim(size - 1000, size)\n            plt.show()\n\n\n\"\"\"\n\n    def lstm_preprocess(self, df, freq=None):\n        upsampled = df if freq is None else df.resample(freq).sum()[ColumnNames.FEATURES.value]\n\n        # frame as supervised learning\n        reframed = series_to_supervised(upsampled[ColumnNames.FEATURES.value],\n                                        ColumnNames.FEATURES.value, ColumnNames.LABEL.value, 1, 1)\n        logging.debug(reframed.head())\n        # split into train and test sets\n        train, test = self.train_test_split(reframed)\n\n        # split into input and outputs\n        _train_X, self.train_y = train.iloc[:, :-1], train.iloc[:, -1]\n        _test_X, self.test_y = test.iloc[:, :-1], test.iloc[:, -1]\n        # reshape input to be 3D [samples, timesteps, features]\n        self.train_X = _train_X.values.reshape(\n            (_train_X.shape[0], Constants.SLIDING_WINDOW_SIZE_OR_TIME_STEPS.value, _train_X.shape[1]))\n        self.test_X = _test_X.values.reshape((_test_X.\n                                              shape[0], Constants.SLIDING_WINDOW_SIZE_OR_TIME_STEPS.value, _test_X.shape[1]))\n        logging.debug(self.train_X.shape, self.train_y.shape, self.test_X.shape, self.test_y.shape)\n\"\"\"\n","repo_name":"hossein20s/kwh_prediction","sub_path":"power_predictor.py","file_name":"power_predictor.py","file_ext":"py","file_size_in_byte":26054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74826884087","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,py\n#     text_representation:\n#       extension: .py\n#       format_name: light\n#       format_version: '1.5'\n#       jupytext_version: 1.9.1+dev\n#   kernelspec:\n#     display_name: Python [conda env:common_microbe] *\n#     language: python\n#     name: conda-env-common_microbe-py\n# ---\n\n# # Train VAE model\n#\n# This notebook will first try to train the current VAE model before modifying the loss function to work with count data. Specifically, the loss function uses the loss from defined in [Eraslan et al.](https://www.nature.com/articles/s41467-018-07931-2). This publication uses the zero-inflated negative binomial (ZINB) distribution, which models highly sparse and overdispersed count data. ZINB is a mixture model that is composed of\n#    1. A point mass at 0 to represent the excess of 0's\n#    2. A NB distribution to represent the count distribution\n#\n# Params of ZINB conditioned on the input data are estimated. These params include the mean and dispersion parameters of the NB component (μ and θ) and the mixture coefficient that represents the weight of the point mass (π)\n#\n# We adopted code from: https://github.com/theislab/dca/blob/master/dca/loss.py\n#\n# More details about the new model can be found: https://docs.google.com/presentation/d/1Q_0BUbfg51OicxY4MdI0IwhdhFfJmzX0f8VyuyGNXrw/edit#slide=id.ge45eb3c133_0_56\n\n# +\n# %load_ext autoreload\n# %autoreload 2\n# %matplotlib inline\nimport os\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom cm_modules import paths, utils, train_vae_modules\nimport scanpy as sc\nimport anndata\n\nimport tensorflow\n\nprint(tensorflow.__version__)\n\n# +\n# Set seeds to get reproducible VAE trained models\n# train_vae_modules.set_all_seeds()\n\n# +\nbase_dir = os.path.abspath(os.path.join(os.getcwd(), \"../\"))\n\n# Read in config variables\nconfig_filename = os.path.abspath(\n    os.path.join(base_dir, \"test_vae_training\", \"config_current_vae.tsv\")\n)\n\nparams = utils.read_config(config_filename)\n\ndataset_name = params[\"dataset_name\"]\n\nraw_compendium_filename = params[\"raw_compendium_filename\"]\nnormalized_compendium_filename = params[\"normalized_compendium_filename\"]\n# -\n\nraw_compendium = pd.read_csv(raw_compendium_filename, sep=\"\\t\", index_col=0, header=0)\n\nprint(raw_compendium.shape)\nraw_compendium.head()\n\nraw_compendium.T.to_csv(\n    os.path.join(paths.LOCAL_DATA_DIR, \"raw_microbiome_transposed.tsv\"), sep=\"\\t\"\n)\n\n# +\n# TO DO:\n# In the DCA paper, they log2 transformed and z-score normalized their data\n\n# Try normalzing the data\n# Here we are normalizing the microbiome count data per taxon\n# so that each taxon is in the range 0-1\ntrain_vae_modules.normalize_expression_data(\n    base_dir, config_filename, raw_compendium_filename, normalized_compendium_filename\n)\n# -\n\n# test1 = pd.read_csv(raw_compendium_filename, sep=\"\\t\")\ntest2 = pd.read_csv(normalized_compendium_filename, sep=\"\\t\", index_col=0, header=0)\n\n# +\n# test1.head()\n# -\n\ntest2.shape\n\ntest2.head()\n\n# Convert input to anndata object\ntest2_anndata = anndata.AnnData(test2)\n\n# Save\nraw_compendium_anndata_filename = os.path.join(\n    paths.LOCAL_DATA_DIR, \"raw_microbiome_transposed_anndata.h5ad\"\n)\ntest2_anndata.write(raw_compendium_anndata_filename)\n\n# +\n# Create VAE directories if needed\noutput_dirs = [\n    os.path.join(base_dir, dataset_name, \"models\"),\n    os.path.join(base_dir, dataset_name, \"logs\"),\n]\n\nNN_architecture = params[\"NN_architecture\"]\n\n# Check if NN architecture directory exist otherwise create\nfor each_dir in output_dirs:\n    sub_dir = os.path.join(each_dir, NN_architecture)\n    os.makedirs(sub_dir, exist_ok=True)\n# -\n\n# Train VAE on new compendium data\ntrain_vae_modules.train_vae(config_filename, raw_compendium_anndata_filename)\n\n# +\n# Plot training and validation loss separately\n# stat_logs_filename = \"logs/DCA/tybalt_2layer_30latent_stats.tsv\"\n\n# stats = pd.read_csv(stat_logs_filename, sep=\"\\t\", index_col=None, header=0)\n\n# +\n# plt.plot(stats[\"loss\"])\n\n# +\n# plt.plot(stats[\"val_loss\"], color=\"orange\")\n","repo_name":"greenelab/common-microbes","sub_path":"test_vae_training/1_train_vae.py","file_name":"1_train_vae.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30308046428","text":"from flask import Flask,request,render_template\nfrom werkzeug.utils import secure_filename\nimport pandas as pd\nimport csv\nimport os\n\napp=Flask(__name__)\n\n@app.route('/')\ndef upload_this():\n    return render_template('upload.html')\n\n@app.route('/uploader',methods=['GET','POST'])\ndef upload_files():\n    if request.method=='POST':\n        f=request.files['file']\n        filename=secure_filename(f.filename)\n        f.save(os.path.join(r'C:\\Users\\keith\\flask_csvupload',filename))\n\n        return 'file upload successfully'\n#app.config[\"UPLOAD_PATH\"]=r\"C:\\Users\\keith\\flask_csvupload\"\n\n\n\nif __name__==\"__main__\":\n    app.run(debug=True)","repo_name":"keithferns98/Flask","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27103769537","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nfrom PyQt5.QtCore import QBasicTimer, Qt, pyqtSignal\nfrom PyQt5.QtGui import QPainter, QColor\nfrom PyQt5.QtWidgets import QFrame\nfrom Pentominoes import Pentominoes\nimport pentix\n\n\nclass Board(QFrame):\n\n    statusbar_msg = pyqtSignal(str)\n\n    def __init__(self, parent, game):\n        super().__init__()\n\n        self.setStyleSheet(\"background-color: rgb(220, 220, 220); \"\n                           \"margin:5px; \"\n                           \"border:1px solid rgb(47, 79, 79); \")\n        self.timer = QBasicTimer()\n        self.game = game\n        self.square = 20\n        self.width = self.game.width * self.square\n        self.height = self.game.height * self.square\n        self.parent = parent\n        self.resize(self.width, self.height)\n\n        self.setFocusPolicy(Qt.StrongFocus)\n\n    def restart(self, game):\n        self.game = game\n        self.update()\n\n    def square_width(self):\n        return self.square\n\n    def square_height(self):\n        return self.square\n\n    def timerEvent(self, event):\n        if event.timerId() == self.timer.timerId():\n            if self.game.is_paused:\n                self.timer.stop()\n            elif self.game.is_over:\n                self.statusbar_msg.emit('Game over')\n                self.timer.stop()\n                self.parent.highscores = \\\n                    pentix.update_records(self.parent.highscores,\n                                          self.parent.game.score)\n            else:\n                self.timer.start(self.game.speed, self)\n                self.game.one_line_down()\n        self.parent.on_board_signal()\n        self.parent.next_figure.update()\n        self.update()\n\n    def keyPressEvent(self, event):\n        key = event.key()\n        if key == Qt.Key_P:\n            self.game.pause()\n            if self.game.is_paused:\n                self.statusbar_msg.emit('Pause')\n            else:\n                self.timer.start(self.game.speed, self)\n                self.statusbar_msg.emit('Play')\n            return\n\n        if self.game.is_paused \\\n                or self.game.current_piece.shape() == Pentominoes.NoShape:\n            return\n        elif key == Qt.Key_Left:\n            self.game.try_move(self.game.current_piece,\n                               self.game.cur_x - 1,\n                               self.game.cur_y)\n        elif key == Qt.Key_Right:\n            self.game.try_move(self.game.current_piece,\n                               self.game.cur_x + 1,\n                               self.game.cur_y)\n        elif key == Qt.Key_Down:\n            self.game.one_line_down()\n        elif key == Qt.Key_Up:\n            self.game.try_move(self.game.current_piece.rotate_left(),\n                               self.game.cur_x,\n                               self.game.cur_y)\n        elif key == Qt.Key_Space:\n            self.game.drop_down()\n        self.parent.next_figure.update()\n        self.update()\n\n    def paintEvent(self, event):\n\n        painter = QPainter(self)\n        rect = self.contentsRect()\n        self.drawFrame(painter)\n\n        boardTop = rect.bottom() - self.game.height * self.square_height()\n        boardTop1 = rect.bottom()\n        boardLeft = rect.left()\n        boardRight = rect.right()\n        boardBottom = rect.top()\n\n        for i in range(self.game.height):\n            for j in range(self.game.width):\n                shape = self.game.shape_at(j, self.game.height - i - 1)\n\n                if shape != Pentominoes.NoShape:\n                    self.draw_square(painter,\n                                     rect.left() + j * self.square_width(),\n                                     boardTop + i * self.square_height(),\n                                     shape)\n\n        if self.game.current_piece.shape() != Pentominoes.NoShape:\n\n            for i in range(5):\n\n                x = self.game.cur_x + self.game.current_piece.x(i)\n                y = self.game.cur_y - self.game.current_piece.y(i)\n                self.draw_square(painter,\n                                 rect.left() + x * self.square_width(),\n                                 boardTop + (self.game.height - y - 1)\n                                 * self.square_height(),\n                                 self.game.current_piece.shape())\n        self.draw_line(painter)\n\n    def draw_line(self, painter):\n        rect = self.contentsRect()\n        y = rect.bottom() - self.game.height // 2 * self.square\n        x1 = rect.left() + (self.game.width // 3 + 1) * self.square\n        x2 = rect.left() + self.game.width // 3 * 2 * self.square\n        painter.drawLine(x1, y, x2, y)\n\n    def draw_square(self, painter, x, y, shape):\n        color_table = [0x000000, 0xCC6666, 0x66CC66, 0x0F66CC,\n                       0xCCCC66, 0xCC66A4, 0x66CCCC, 0xDAAA00,\n                       0x66CDAA, 0x83471D, 0x4682B4, 0x800080,\n                       0xF4A460]\n        color = QColor(color_table[shape])\n        painter.fillRect(x + 1, y + 1, self.square_width() - 2,\n                         self.square_height() - 2, color)\n\n        painter.setPen(color.lighter())\n        painter.drawLine(x, y + self.square_height() - 1, x, y)\n        painter.drawLine(x, y, x + self.square_width() - 1, y)\n\n        painter.setPen(color.darker())\n        painter.drawLine(x + 1, y + self.square_height() - 1,\n                         x + self.square_width() - 1,\n                         y + self.square_height() - 1)\n        painter.drawLine(x + self.square_width() - 1,\n                         y + self.square_height() - 1,\n                         x + self.square_width() - 1,\n                         y + 1)\n","repo_name":"Kaprican/Pentix_2.0","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":5633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7055266787","text":"import pandas as pd\nimport os\nimport datetime as dt\nimport numpy as np\nimport portfoliooptimization.plots as plots\n#Reportlab dependencies\nfrom reportlab.lib.pagesizes import letter, landscape\nfrom reportlab.platypus import *\nfrom reportlab.lib import colors\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.units import mm, inch\nimport portfoliooptimization.performance as performance\nfrom dateutil.relativedelta import relativedelta\nimport portfoliooptimization.exposure as exposure\nimport portfoliooptimization.metrics as metrics\nimport matplotlib.pyplot as plt\n\n#RF Syntax: 6 MO, 2 YR etc.\nrate = '1 YR'\n#CUR, AVG\nmethod = \"AVG\"\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nroot_path = BASE_DIR+\"/static/Portfolio_Tracker\"\n\n#Create Reports Folder\nif not os.path.exists(root_path + '/Reports'):\n    os.mkdir(root_path + '/Reports')\n\nend_date = dt.date.today()\n\nclass rep():\n\n    def __init__(self, fname, fund_name,portfolioobj, logo_path=0):\n        self.width, self.height = landscape(letter)\n        self.logo_path = logo_path\n        self.c = canvas.Canvas(fname)\n        self.fund_name = fund_name\n        self.portfolioobj = portfolioobj\n\n    def cover(self):\n        fund_name = self.fund_name\n        c = self.c\n        logo = self.logo_path\n        c.setPageSize(landscape(letter))\n\n        #Title\n        c.setFontSize(size=24) #choose your font type and font size\n        c.setFillColor(aColor=colors.black)\n        c.drawString(x=72,y=420,text=fund_name)\n\n        #Subtitle\n        c.setFontSize(size=18)  # choose your font type and font size\n        c.setFillColor(aColor=colors.cadetblue)\n        c.drawString(x=72, y=395, text=\"Portfolio Daily Report: \" + str(end_date))\n\n        #Draw Company Logo\n        #if self.logo_path != 0:\n           # c.drawImage(logo, x=250, y=500)\n\n        #Line\n        c.setFillColor(colors.cadetblue)\n        c.rect(x=0, y=385, height=\"3\", width=\"842\", stroke=0, fill=1)\n\n        #Move to next page\n        c.showPage()\n    \n    \n    def perf(self,poobj):\n        c = self.c\n        logo = self.logo_path\n        portobj= poobj\n        print(\"###########PERFORMANCE Company Type#########\")\n        print(portobj.Company_Type)\n        #Get Data from module\n        port_rets, bench_rets = performance.portfolio(portobj)\n        port_rets= port_rets.sort_index(axis=0,ascending=True)\n        bench_rets= bench_rets.sort_index(axis=0,ascending=True)\n        print(\"############# port_rets ######\")\n        print(port_rets) \n        print(\"############# bench_rets ######\")\n        print(bench_rets) \n        #Header\n        c.setFontSize(size=18)\n        c.setFillColor(aColor=colors.cadetblue)\n        c.drawString(x=72, y=505, text=\"Performance\")\n        c.setFillColor(colors.cadetblue)\n        c.rect(x=0, y=500, height=\"3\", width=\"842\", stroke=0, fill=1)\n\n        if self.logo_path != 0:\n            c.drawImage(logo,x=250, y=505)\n\n        #Draw price chart\n        c.drawImage(root_path + '/Figures/'+portobj.Portfolio_Name+'_port_perf.png',x=50, y=50,width=400,height=400,preserveAspectRatio=1)\n\n        #Draw periodic returns table\n        dates = ['2018-01-02']\n        pperf =  \"{0:.2f}%\".format(float(port_rets.iloc[-1:, -1]) * 100)\n        bperf = \"{0:.2f}%\".format(float(bench_rets.iloc[-1:, -1]) * 100)\n\n        port_val = pd.read_csv(root_path+'/Daily_Data/Portfolio/'+portobj.Portfolio_Name+'_Portfolio_Value.csv',index_col=0)\n        bench_data = pd.read_csv(root_path+'/Daily_Data/Benchmark/Benchmark_Price_Data.csv' ,index_col=0)\n        bench_data = bench_data.iloc[::-1]\n        bench_data= bench_data.sort_index(axis=0,ascending=True)\n        port_val = port_val['Portfolio Value']\n        port_val.index = pd.to_datetime(port_val.index)\n        bench_data.index = pd.to_datetime(bench_data.index)\n        print(\"########port_val###########3\")\n        print(port_val)      \n        praw = \"{0:.2f}%\".format((port_val.iloc[-1] / port_val.iloc[0] - 1) * 100)\n        pytd = \"{0:.2f}%\".format((port_val.iloc[-1] / float(port_val[port_val.index.isin(dates)]) - 1) * 100)\n        braw = \"{0:.2f}%\".format((float(bench_data.iloc[-1]) / float(bench_data.iloc[0]) - 1) * 100)\n        bytd = \"{0:.2f}%\".format((float(bench_data.iloc[-1]) / float(bench_data[bench_data.index.isin(dates)].iloc[0]) - 1) * 100)\n\n        prets = []\n        brets = []\n        nums = [1,3,6,12]\n        start_date = dt.date(2017,1,3)\n        for x in nums:\n            month_add = start_date + relativedelta(months=+x)\n            pret = port_val.iloc[port_val.index.get_loc(month_add, method='nearest')]/port_val.iloc[0] - 1\n            prets.append(\"{0:.2f}%\".format(pret * 100))\n            bret = float(bench_data.iloc[bench_data.index.get_loc(month_add, method='nearest')]) / float(bench_data.iloc[0]) - 1\n            brets.append(\"{0:.2f}%\".format(bret * 100))\n        print(brets)\n        #Labels for Asset Returns\n        c.setFontSize(size=12)\n        c.setFillColor(aColor=colors.black)\n        c.drawString(x=500, y=400, text=\"Total Return Asset\")\n        c.drawString(x=625, y=400, text=\"1 Day Return Asset\")\n\n        #Data for table\n        data = [['',\"YTD\",\"Today's Performance\", \"1 Month Change\", \"3 Month Change\",\"6 Month Change\", \"1 Year Change\", \"Total Raw Return\"],\n                ['Portfolio',pytd,pperf, prets[0],prets[1],prets[2],prets[3],praw],\n                ['Benchmark',bytd,bperf,brets[0],brets[1],brets[2],brets[3],braw]]\n\n        #Create table with styles\n        t1 = Table(data)\n\n        t1.setStyle(TableStyle([(\"BOX\", (0, 0), (-1, -1), 0.25, colors.black),\n                               ('INNERGRID', (0, 0), (-1, -1), 0.25, colors.black),\n                               ('BACKGROUND', (0, 0), (0, 0), colors.black),\n                                ('ALIGN', (1, 1), (-1, -1), 'CENTER')]))\n\n        #Draw on Canvas\n        t1.wrapOn(self.c, self.width, self.height)\n        t1.drawOn(c, x=72, y=440)\n\n\n        #Draw asset Perfomance Tables\n        df = performance.asset_performance(portobj)\n        df = pd.DataFrame(df)\n        df.reset_index(level=0, inplace=True)\n        df.columns = [\"Symbol\", \"Return\"]\n\n        if len(df) > 21:\n            df = df[:21]\n\n        df = df.sort_values(['Return'], ascending=False)\n        df['Return'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in df['Return']], index=df.index)\n\n        data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n        t = Table(data)\n\n        data_len = len(data)\n\n        x = 390\n\n        #Need to do for alternating colors\n        for each in range(data_len):\n\n            x -= .25 * inch\n\n            if each % 2 == 0:\n                bg_color = colors.cadetblue\n            else:\n                bg_color = colors.white\n\n            t.setStyle(TableStyle([('BACKGROUND', (0, each), (-1, each), bg_color)]))\n\n        t.setStyle(TableStyle([('BACKGROUND', (0, 0), (0, 0), colors.black),\n                               ('BACKGROUND', (1, 0), (1, 0), colors.black),\n                               ('TEXTCOLOR',(0,0),(0, 0),colors.white),\n                               ('LINEBELOW', (0, -1), (-1, -1), 1.5, colors.black),\n                               ('TEXTCOLOR', (1, 0), (1, 0), colors.white)\n                               ]))\n\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=500, y=x)\n\n        df = pd.read_csv(root_path+'/Daily_Data/Portfolio/'+portobj.Portfolio_Name+'_Portfolio_Returns.csv' )\n        data = df.iloc[-1,1:len(df.columns)-1]\n        df = pd.DataFrame(data)\n\n        if len(df) > 21:\n            df = df[:21]\n\n        df.reset_index(level=0, inplace=True)\n        df.columns = [\"Symbol\", \"Return\"]\n        df = df.sort_values(by='Return',ascending=False)\n        df['Return'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in df['Return']], index=df.index)\n        data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n\n        t = Table(data)\n\n        data_len = len(data)\n\n        x = 390\n        #Need to do for alternating colors\n        for each in range(data_len):\n\n            x -= .25 * inch\n\n            if each % 2 == 0:\n                bg_color = colors.cadetblue\n            else:\n                bg_color = colors.white\n\n            t.setStyle(TableStyle([('BACKGROUND', (0, each), (-1, each), bg_color)]))\n\n        t.setStyle(TableStyle([('BACKGROUND', (0, 0), (0, 0), colors.black),\n                               ('BACKGROUND', (1, 0), (1, 0), colors.black),\n                               ('TEXTCOLOR',(0,0),(0, 0),colors.white),\n                               ('LINEBELOW', (0, -1), (-1, -1), 1.5, colors.black),\n                               ('TEXTCOLOR', (1, 0), (1, 0), colors.white)\n                               ]))\n\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=635, y=x)\n\n        c.showPage()\n    def exposurePieChart(self,poobj):\n        c = self.c\n        exposure.info(poobj)\n        c.drawImage(root_path + '/Figures/'+poobj.Portfolio_Name+'_ind_pie.png',x=372, y=200,width=250,height=350,preserveAspectRatio=1)\n        c.showPage()\n        \n    def diversification(self,poobj):\n        c = self.c\n        logo = self.logo_path\n\n        #Header\n        c.setFontSize(size=18)  # choose your font type and font size\n        c.setFillColor(aColor=colors.cadetblue)\n        c.drawString(x=72, y=505, text=\"Diversification\")\n        c.setFillColor(colors.cadetblue)\n        c.rect(x=0, y=500, height=\"3\", width=\"842\", stroke=0, fill=1)\n\n        if self.logo_path != 0:\n            c.drawImage(logo,x=250, y=505)\n\n        #Correlation plot\n        plots.correl(poobj)\n        c.drawImage(root_path + '/Figures/'+poobj.Portfolio_Name+'_port_correl.png',x=72, y=200,width=250,height=350,preserveAspectRatio=1)\n\n        #Get exposure plots/data\n        exposure.info(poobj)\n\n        #Draw Sector Table\n        df = pd.read_csv(root_path+'/Daily_Data/Portfolio/'+poobj.Portfolio_Name+'_Industrial_Weights.csv')\n        \n        if len(df) > 22:\n            df = df[:22]\n\n        df.columns = [\"Industry\", \"Weight\"]\n        df['Weight'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in df['Weight']], index=df.index)\n\n        data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n        t = Table(data)\n        t.setStyle(TableStyle([(\"BOX\", (0, 0), (-1, -1), 0.25, colors.black),\n                               ('INNERGRID', (0, 0), (-1, -1), 0.25, colors.black)]))\n        data_len = len(data)\n\n        #Start position on canvas\n        x = 280\n        for each in range(data_len):\n            x -= .25 * inch\n            if each % 2 == 0:\n                bg_color = colors.cadetblue\n            else:\n                bg_color = colors.white\n\n            t.setStyle(TableStyle([('BACKGROUND', (0, each), (-1, each), bg_color)]))\n\n        t.setStyle(TableStyle([('BACKGROUND', (0, 0), (-1, 0), colors.black),\n                               ('TEXTCOLOR',(0,0),(-1, 0),colors.white),\n                               ('ALIGN', (0, 0), (-1, 0), 'CENTER')]))\n\n        #draw table\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=72, y=x)\n        #c.drawImage(root_path + '/Figures/'+poobj.Portfolio_Name+'_ind_weight.png',x=72, y=200,width=250,height=350,preserveAspectRatio=1)\n        \n        plt.table(cellText=data,\n                      rowColours=None,\n                      colLabels=None,\n                      loc='bottom')\n        \n        figure=plt.gcf()\n        figure.savefig(root_path + '/Figures/'+poobj.Portfolio_Name+'_ind_weight.png')\n        #Draw industry table\n        #df = pd.read_csv(root_path+'/Daily Data/Portfolio/Asset Exposure.csv',index_col=0)\n        #df.reset_index(level=0, inplace=True)\n        #df.columns = [\"Symbol\", \"Sector\", \"Industry\"]\n        #data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n\n        #t = Table(data)\n        #t.setStyle(TableStyle([(\"BOX\", (0, 0), (-1, -1), 0.25, colors.black),\n#                               ('INNERGRID', (0, 0), (-1, -1), 0.25, colors.black)]))\n        #data_len = len(data)\n\n        #Start position on canvas\n        #x = 440\n        #for each in range(data_len):\n        #   x -= .25 * inch\n        #    if each % 2 == 0:\n        #        bg_color = colors.cadetblue\n        #    else:\n        #        bg_color = colors.white\n\n#            t.setStyle(TableStyle([('BACKGROUND', (0, each), (-1, each), bg_color)]))\n\n #       t.setStyle(TableStyle([('BACKGROUND', (0, 0), (-1, 0), colors.black),\n  #                             ('TEXTCOLOR',(0,0),(-1, 0),colors.white),\n   #                            ('ALIGN', (0, 0), (-1, 0), 'CENTER')]))\n\n        #draw table\n    #    t.wrapOn(self.c, self.width, self.height)\n     #   t.drawOn(c, x=360, y=x)\n     \n    def mets(self,portfolioobj):\n        c = self.c\n        logo = self.logo_path\n\n        #Header\n        c.setFontSize(size=18)  # choose your font type and font size\n        c.setFillColor(aColor=colors.cadetblue)\n        c.drawString(x=72, y=505, text=\"Metrics\")\n        c.setFillColor(colors.cadetblue)\n        c.rect(x=0, y=500, height=\"3\", width=\"842\", stroke=0, fill=1)\n\n        if self.logo_path != 0:\n            c.drawImage(logo,x=250, y=505)\n\n        c.setFontSize(size=12)  # choose your font type and font size\n        c.setFillColor(aColor=colors.black)\n        c.drawString(x=425, y=475, text=\"Risk-Adjusted Metrics\")\n\n        c.setFontSize(size=12)  # choose your font type and font size\n        c.setFillColor(aColor=colors.black)\n        c.drawString(72, 475, text=\"Weighted Fundamental Metrics\")\n\n        data = metrics.fundis(rate=rate,method=method,portfolioobj=portfolioobj)\n\n        t = Table(data)\n        t.setStyle(TableStyle([('TEXTCOLOR', (0, 0), (-1, 0), colors.white),\n                               ('BACKGROUND', (0, 0), (-1, 0), colors.black),\n                               ('LINEBELOW', (0, -1), (-1, -1), 1.5, colors.black),\n                               ('BACKGROUND', (0, 1), (-1, 1), colors.cadetblue),\n                               ('ALIGN', (1, 1), (-1, -1), 'CENTER')]))\n\n        #draw table\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=425, y=400)\n\n        #Load data\n        df = pd.read_csv(root_path + '/Daily_Data/Portfolio/'+portfolioobj.Portfolio_Name+'_Portfolio_Fundis.csv',header=None)\n        df.columns = [\"Metric\", \"Weighted Sum\"]\n\n        #String formatting\n        for i, trial in df.iterrows():\n            if i == 5:\n                df.loc[i, \"Weighted Sum\"] = \"{0:.2f}%\".format(df.iloc[i,1] * 100)\n            else:\n                df.loc[i, \"Weighted Sum\"] = \"{0:.2f}\".format(df.iloc[i, 1])\n\n        #Convert to format for reportlab\n        data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n\n        t = Table(data)\n        t.setStyle(TableStyle([\n                                ('TEXTCOLOR',(0,0),(1, 0),colors.white),\n                                ('BACKGROUND', (0, 0), (1, 0), colors.black),\n                               ('BACKGROUND', (0, 1), (1, 1), colors.cadetblue),\n                               ('BACKGROUND', (0, 3), (1, 3), colors.cadetblue),\n                                ('BACKGROUND', (0, 5), (1, 5), colors.cadetblue),\n                                ('LINEBELOW', (0, -1), (1, -1), 1.5, colors.black),\n                               ('ALIGN', (1, 1), (-1, -1), 'CENTER')]))\n\n        #draw table\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=72, y=335)\n\n        df = pd.read_csv(root_path + '/Daily_Data/Portfolio/'+portfolioobj.Portfolio_Name+'_Asset_Fundis.csv')\n\n        if len(df) > 15:\n            df = df[:15]\n\n        df['Dividend Yield'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in df['Dividend Yield']], index=df.index)\n        df.columns = ['Symbol','Trailing P/E', 'Forward P/E', 'PEG', 'Price/Book', 'Beta', 'Dividend Yield']\n\n        data = [df.columns[:, ].values.astype(str).tolist()] + df.values.tolist()\n\n        c.setFontSize(size=12)  # choose your font type and font size\n        c.setFillColor(aColor=colors.black)\n        c.drawString(72, 315, text=\"Asset Fundamental Metrics\")\n\n        t = Table(data)\n\n        data_len = len(data)\n\n        x = 300\n\n        #Need to loop for alternating colors\n        for each in range(data_len):\n            x -= .25 * inch\n            if each % 2 == 0:\n                bg_color = colors.white\n            else:\n                bg_color = colors.cadetblue\n\n            t.setStyle(TableStyle([('BACKGROUND', (0, each), (-1, each), bg_color)]))\n\n        t.setStyle(TableStyle([('TEXTCOLOR',(0,0),(-1, 0),colors.white),\n                                ('BACKGROUND', (0, 0), (-1, 0), colors.black),\n                               ('LINEBELOW', (0, -1), (-1, -1), 1.5, colors.black),\n                               ('ALIGN', (1, 1), (-1, -1), 'CENTER')]))\n\n        t.wrapOn(self.c, self.width, self.height)\n        t.drawOn(c, x=72, y=x)\n        c.showPage()\n \n\n    def savePDF(self):\n        self.c.save()","repo_name":"AYUSHOPSHARMA/wealthadvisory","sub_path":"portfoliooptimization/wealthmanagementreport.py","file_name":"wealthmanagementreport.py","file_ext":"py","file_size_in_byte":16962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2939975672","text":"\"\"\"\nffs.formats\n\"\"\"\nimport collections\nimport csv\n\nimport six\nfrom six.moves import StringIO\n\nWriterType = csv.writer(StringIO()).__class__\nReaderType = csv.reader(StringIO()).__class__\n\nclass CSV(object):\n    \"\"\"\n    The Quantum CSV file operates like both a csv.reader and a csv.writer,\n    until we observe you doing something with it that lets us know what it\n    is.\n\n    If you try to iterate through it, the CSV collapses into a reader.\n    If you try to writerow() it, the CSV collapses into a writer.\n\n    If HEADER is True, we consume the frist row, and use it to generate a\n    namedtuple based on the fieldnames, returning rows as an instance of this\n    class.\n    \"\"\"\n    # !!! add strip= True\n    def __init__(self, path, delimiter=',', header=False):\n        self.path = path\n        self.delimiter = delimiter\n        self.header = header\n        self.resolved = None\n        self.fh = None\n        self.rowklass = None\n        # Later we should figure out what to do with writers with headers?\n        if header:\n            self._resolve_reader()\n            self._generate_rowklass()\n\n    def __repr__(self):\n        rpr = '<{0} CSV {1}>'.format('Unresolved', self.path)\n        return rpr\n\n    def __enter__(self):\n        \"\"\"\n        Pass and return self\n        \"\"\"\n        return self\n\n    def __exit__(self, msg, exc, tb):\n        if self.fh:\n            self._close()\n\n    def _close(self):\n        \"\"\"\n        Close whatever it is we want to close.\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        self.fh.close()\n\n    def _resolve_reader(self):\n        \"\"\"\n        Resolve SELF to a Reader\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        self.fh = self.path.fs.open(self.path, 'rU')\n        self.resolved = csv.reader(self.fh, delimiter=self.delimiter)\n\n    def _resolve_writer(self):\n        \"\"\"\n        Resolve SELF to a Writer\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        self.fh = self.path.fs.open(self.path, 'w')\n        self.resolved = csv.writer(self.fh, delimiter=self.delimiter)\n\n    def _generate_rowklass(self):\n        \"\"\"\n        Create a namedtuple based on the frist row of the CSV.\n\n        Store this at self.rowklass\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        header = six.next(self.resolved)\n        clean = []\n        for h in header:\n            underscoreless = h.strip().lower().replace(' ', '_').replace('.', '_')\n            specialless = underscoreless.replace('(', '').replace(')', '').replace('?', '').replace('-', '')\n            if specialless == '':\n                clean.append(specialless)\n                continue\n            try:\n                num = int(specialless[0])\n                numbers = {1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five',\n                           6: 'six', 7: 'seven', 8: 'eight', 9: 'nine', 10: 'ten'}\n                numless = numbers[num] + specialless[1:]\n                cleaned = numless\n            except ValueError:\n                cleaned = specialless\n\n            more = 1\n            while cleaned in clean:\n                more += 1\n                cleaned += str(more)\n\n            clean.append(cleaned)\n\n        for i, v in enumerate(clean):\n            if v == '':\n                clean[i] = 'field_' + str(i)\n        self.rowklass = collections.namedtuple('RowKlass', clean)\n\n    def __iter__(self):\n        \"\"\"\n        If we are unresolved, resolve to a reader, and return an Iterable.\n        If we are resolved, raise TypeError\n\n        Return: iterable\n        Exceptions: TypeError\n        \"\"\"\n        if not self.resolved:\n            self._resolve_reader()\n\n        if isinstance(self.resolved, WriterType):\n            raise TypeError('Writer is not iterable')\n\n        def gen():\n            for row in self.resolved:\n                if self.header and self.rowklass:\n                    row = self.rowklass(*row)\n                yield row\n\n        return gen()\n\n    @property\n    def line_num(self):\n        \"\"\"\n        If we're unresolved, resolve to a reader then, pass through.\n        If we're resolved to a reader, pass through.\n        If we're resolved to a writer, raise AttributeError\n\n        Return: None\n        Exceptions: AttributeError\n        \"\"\"\n        if not self.resolved:\n            self._resolve_reader()\n\n        if isinstance(self.resolved, WriterType):\n            raise AttributeError('CSV Writer object has no attribute line_num')\n\n        return self.resolved.line_num\n\n    def next(self):\n        \"\"\"\n        If we're unresolved, resolve to a reader then, pass through.\n        If we're resolved to a reader, pass through.\n        If we're resolved to a writer, raise AttributeError\n\n        Return: list[str]\n        Exceptions: AttributeError\n        \"\"\"\n        if not self.resolved:\n            self._resolve_reader()\n\n        if isinstance(self.resolved, WriterType):\n            raise AttributeError('CSV Writer object has no attribute next')\n\n        row = self.resolved.next()\n        if self.header and self.rowklass:\n            row = self.rowklass(*row)\n        return row\n\n    def writerow(self, row):\n        \"\"\"\n        Construct and write a CSV record from a sequence of fields.\n        Non-string elements will be converted to a string.\n\n        Arguments:\n        - `row`: iterable\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        if not self.resolved:\n            self._resolve_writer()\n\n        if isinstance(self.resolved, ReaderType):\n            raise AttributeError('Object CSV has no attribute writerow')\n\n        self.resolved.writerow(row)\n\n    def writerows(self, row):\n        \"\"\"\n        Construct and write a CSV record from a sequence of sequences\n        Non-string elements will be converted to a string.\n\n        Arguments:\n        - `row`: iterable of iterables\n\n        Return: None\n        Exceptions: None\n        \"\"\"\n        if not self.resolved:\n            self._resolve_writer()\n\n        if isinstance(self.resolved, ReaderType):\n            raise AttributeError('Object CSV has no attribute writerows')\n        self._resolve_writer()\n        self.resolved.writerows(row)\n","repo_name":"davidmiller/ffs","sub_path":"ffs/formats.py","file_name":"formats.py","file_ext":"py","file_size_in_byte":6170,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29706223537","text":"s = 'sishfbbegha'\nans= ''\nansList=[]\nif len(s) <= 1:\n    ans = s[:]\n    ansList.append(ans)\nelse:\n    ans +=  s[0]\n    for i in range(1,len(s)):\n        if s[i-1] <= s[i]:\n            if s[i] <= s[i+1]:\n                ans += s[i]\n                ansList+= ans\n            else:\n                ansList=s[i:]\n        else:\n            ansList = ans\nprint (ansList)","repo_name":"pureboy8/sun1","sub_path":"Other/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"185017481","text":"import pymongo\n\nclient = pymongo.MongoClient(\"mongodb://localhost:27017\")\ndb = client[\"lib\"]\nbooks = db[\"books\"]\n\"\"\" print(\n    books.update_one({\n        \"name\":\"autobiography of a yogi\"\n    },{\n        \"$set\":{\n           \"author\":\"krishna\" \n        }\n    }).modified_count\n)\n\"\"\"\nbooks.update_many({\"author\":\"yogi\"},{\n    \"$set\":{\n        \"name\":\"yogi a life\"\n    }\n})\n\nprint([*books.find()])\n\n","repo_name":"jaydutt31/MyNotes","sub_path":"mongoDB/updation.py","file_name":"updation.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15253345693","text":"import json\nimport os\n\nimport pygame\n\nfrom snake.managers.game_state import GameState\nfrom snake.settings import SCREEN_WIDTH, SCREEN_HEIGHT, LEFT, UP, RIGHT, DOWN\n\n\nclass PlayerCommands:\n\n    def __init__(self, game_manager):\n        self.joysticks = []\n        self.button_keys = None\n        # 0: Left analog horizonal, 1: Left Analog Vertical, 2: Right Analog Horizontal\n        # 3: Right Analog Vertical 4: Left Trigger, 5: Right Trigger\n        self.analog_keys = {0: 0, 1: 0, 2: 0, 3: 0, 4: -1, 5: -1}\n        self.initialize_controllers()\n        self.game_manager = game_manager\n\n    def initialize_controllers(self):\n        for i in range(pygame.joystick.get_count()):\n            self.joysticks.append(pygame.joystick.Joystick(i))\n\n        for joystick in self.joysticks:\n            joystick.init()\n\n        with open(os.path.join(\"./snake/assets/controllers/ps4.json\"), 'r+') as file:\n            self.button_keys = json.load(file)\n\n    def check_events(self, player=None):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                self.quit()\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    self.quit()\n                if event.key == pygame.K_SPACE:\n                    if self.game_manager.state == GameState.GAME_INTRO:\n                        self.new_game()\n                    elif self.game_manager.state == GameState.GAME_OVER:\n                        self.intro()\n                    elif self.game_manager.state == GameState.GAME_VICTORY:\n                        self.intro()\n                if event.key == pygame.K_f:\n                    if not self.game_manager.full_screen:\n                        pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT),\n                                                pygame.FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n                        self.game_manager.full_screen = True\n                    else:\n                        pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT),\n                                                pygame.RESIZABLE | pygame.HWSURFACE | pygame.DOUBLEBUF)\n                        self.game_manager.full_screen = False\n                if self.game_manager.state == GameState.GAME_RUNNING or \\\n                        self.game_manager.state == GameState.LEVEL_RUNNING:\n                    if event.key == pygame.K_UP:\n                        player.turn(UP)\n                    elif event.key == pygame.K_DOWN:\n                        player.turn(DOWN)\n                    elif event.key == pygame.K_LEFT:\n                        player.turn(LEFT)\n                    elif event.key == pygame.K_RIGHT:\n                        player.turn(RIGHT)\n            if event.type == pygame.JOYBUTTONDOWN:\n                if event.button == self.button_keys[\"circle\"]:\n                    self.quit()\n                if event.button == self.button_keys[\"x\"]:\n                    self.new_game()\n                if self.game_manager.state == GameState.GAME_RUNNING or \\\n                        self.game_manager.state == GameState.LEVEL_RUNNING:\n                    if event.button == self.button_keys['left_arrow']:\n                        player.turn(LEFT)\n                    if event.button == self.button_keys['right_arrow']:\n                        player.turn(RIGHT)\n                    if event.button == self.button_keys['down_arrow']:\n                        player.turn(DOWN)\n                    if event.button == self.button_keys['up_arrow']:\n                        player.turn(UP)\n            if event.type == pygame.JOYAXISMOTION:\n                if self.game_manager.state == GameState.GAME_RUNNING or \\\n                        self.game_manager.state == GameState.LEVEL_RUNNING:\n                    self.analog_keys[event.axis] = event.value\n                    if abs(self.analog_keys[0]) > .4:\n                        if self.analog_keys[0] < -.7:\n                            player.turn(LEFT)\n                        if self.analog_keys[0] > .7:\n                            player.turn(RIGHT)\n                    # Vertical Analog\n                    if abs(self.analog_keys[1]) > .4:\n                        if self.analog_keys[1] < -.7:\n                            player.turn(UP)\n                        if self.analog_keys[1] > .7:\n                            player.turn(DOWN)\n\n    def quit(self):\n        if self.game_manager.state == GameState.GAME_RUNNING or self.game_manager.state == GameState.LEVEL_RUNNING:\n            self.game_manager.state = GameState.GAME_INTRO\n        else:\n            self.game_manager.state = GameState.QUIT\n\n    def new_game(self):\n        self.game_manager.state = GameState.GAME_RUNNING\n\n    def intro(self):\n        self.game_manager.state = GameState.GAME_INTRO\n","repo_name":"eballo/snake-pygame","sub_path":"snake/managers/player_commands.py","file_name":"player_commands.py","file_ext":"py","file_size_in_byte":4822,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"17497924846","text":"import numpy as np\nimport cv2\nfrom skimage.feature import hog\nimport matplotlib.image as mpimg\nimport os\n\n\"\"\"\nImplementation of functions which are necessary to extract features from an image.\n\"\"\"\n\n\ndef get_color_hist_features(img, nbins=32, bin_range=(0, 256)):\n    \"\"\"\n    Given an image of shape (w, h, c=3),\n    return the concatenated histogram of all color channels.\n    So, the length of the returned array will be 3 * nbins\n    \"\"\"\n\n    # Make sure that the image has three color channels\n    assert (img.shape[2] == 3)\n\n    ch0_hist = np.histogram(img[:, :, 0], bins=nbins)\n    ch1_hist = np.histogram(img[:, :, 1], bins=nbins)\n    ch2_hist = np.histogram(img[:, :, 2], bins=nbins)\n\n    color_hist_features = np.concatenate((ch0_hist[0],\n                                          ch1_hist[0],\n                                          ch2_hist[0]))\n    return color_hist_features\n\n\ndef get_spatial_pixel_value_features(img, resize_shape=(32, 32)):\n    \"\"\"\n    Rescale the input image to resize_shape and\n    subsequently return the array of pixel values\n    \"\"\"\n    image = np.copy(img)\n    return np.resize(image, resize_shape).ravel()\n\n\ndef get_hog_features(img, num_orientations,\n                     pix_per_cell, cells_per_block,\n                     vis=False, feature_vec=True):\n    \"\"\"\n    Return the hog features of a 2D image.\n    \"\"\"\n    assert (len(img.shape) == 2)\n\n    if vis:\n        features, hog_image = hog(image=img,\n                                  pixels_per_cell=(pix_per_cell, pix_per_cell),\n                                  cells_per_block=(cells_per_block, cells_per_block),\n                                  orientations=num_orientations,\n                                  visualise=vis, feature_vector=feature_vec)\n        return features, hog_image\n    else:\n        features = hog(image=img,\n                       pixels_per_cell=(pix_per_cell, pix_per_cell),\n                       cells_per_block=(cells_per_block, cells_per_block),\n                       orientations=num_orientations,\n                       visualise=vis, feature_vector=feature_vec)\n    return features\n\n\ndef convert_color(img, conv='RGB2YCrCb'):\n    if conv == 'RGB2YCrCb':\n        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\n    if conv == 'RGB2LUV':\n        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\n\n\ndef get_single_image_feature(img,\n                             color_hist_params,\n                             spatial_pixel_value_params,\n                             hog_params,\n                             color_space=\"YCrCb\"):\n    \"\"\"\n    :param img:\n    :param color_hist_params:\n    :param spatial_pixel_value_params:\n    :param hog_params:\n    :param color_space:\n    :return: Return the feature of a single image\n    \"\"\"\n    image_features = []\n\n    # Convert color according to color space\n    feature_image = np.copy(img)\n    if color_space != \"RGB\":\n        feature_image = convert_color(img, \"RGB2\" + color_space)\n\n    # Get color hist features\n    color_hist_features = get_color_hist_features(feature_image, color_hist_params[\"nbins\"],\n                                                  color_hist_params[\"bin_range\"])\n    # Get spatial pixel value features\n    spatial_pixel_value_features = get_spatial_pixel_value_features(feature_image,\n                                                                    spatial_pixel_value_params[\"resize_shape\"])\n\n    # Get hog features\n    # Do not extract features with visualization on\n    assert (hog_params[\"vis\"] == False)\n    hog_features = []\n    if (hog_params[\"channels\"] == \"ALL\") & (len(img.shape) == 3):\n        for ch in range(img.shape[2]):\n            hf = get_hog_features(feature_image[:, :, 1],\n                                  hog_params[\"num_orientations\"],\n                                  hog_params[\"pix_per_cell\"],\n                                  hog_params[\"cells_per_block\"],\n                                  hog_params[\"vis\"])\n            hog_features.extend(hf)\n    elif len(img.shape) == 3:\n        hog_features = get_hog_features(feature_image[:, :, hog_params[\"channel\"]],\n                                        hog_params[\"num_orientations\"],\n                                        hog_params[\"pix_per_cell\"],\n                                        hog_params[\"cells_per_block\"],\n                                        hog_params[\"vis\"])\n    else:\n        hog_features = get_hog_features(feature_image,\n                                        hog_params[\"num_orientations\"],\n                                        hog_params[\"pix_per_cell\"],\n                                        hog_params[\"cells_per_block\"],\n                                        hog_params[\"vis\"])\n\n    image_features = np.concatenate((color_hist_features,\n                                     spatial_pixel_value_features,\n                                     hog_features))\n    return image_features\n\n\ndef get_features_from_list_of_images(image_files_paths,\n                                     color_hist_params,\n                                     spatial_hist_params,\n                                     hog_params,\n                                     color_space=\"RGB\"):\n    \"\"\"\n    Define a function to extract features from a list of images\n    Have this function call get_single_image_feature\n    \"\"\"\n\n    features = []\n    for img_file in image_files_paths:\n        image = mpimg.imread(img_file)\n\n        feature_image = np.copy(image)\n        file_feature = get_single_image_feature(feature_image,\n                                                color_hist_params,\n                                                spatial_hist_params,\n                                                hog_params,\n                                                color_space)\n        features.append(file_feature)\n\n    # Returned array has dim: (num_samples x num_features)\n    return np.vstack(features)\n","repo_name":"spookyQubit/VehicleDetection","sub_path":"src/featureExtractor.py","file_name":"featureExtractor.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6091345687","text":"#!/usr/bin/python\n\nimport numpy as np\nimport pandas as pd\nimport pyspark\nfrom pyspark import SparkContext\nfrom pyspark.sql import SQLContext, SparkSession\nfrom pyspark.sql.types import StructType, StructField, IntegerType, DoubleType\nfrom pyspark.ml.feature import VectorAssembler\nimport mleap.pyspark\nfrom mleap.pyspark.spark_support import SimpleSparkSerializer\nfrom pyspark.ml import PipelineModel\nfrom pyspark.ml.evaluation import RegressionEvaluator\n\n\nimport os\nimport sys\nimport argparse\nimport tempfile\nimport warnings\n\n\ndef read_data_csv(spark, inputPath_CSV):\n    \n    '''\n    Function to load data in the Spark Session \n    :param spark: spark session \n    :param inputPath: path to get the data \n    :return: df\n    '''\n    \n    print('Trying to read the data...')\n    \n    try:\n        schema = StructType([\n          StructField('crim',DoubleType(),True),\n          StructField('zn',DoubleType(),True),\n          StructField('indus',DoubleType(),True),\n          StructField('chas',IntegerType(),True),\n          StructField('nox',DoubleType(),True),\n          StructField('rm',DoubleType(),True),\n          StructField('age',DoubleType(),True),\n          StructField('dis',DoubleType(),True),\n          StructField('rad',IntegerType(),True),\n          StructField('tax',IntegerType(),True),\n          StructField('ptratio',DoubleType(),True),\n          StructField('b',DoubleType(),True),\n          StructField('lstat',DoubleType(),True),\n          StructField('medv',DoubleType(),True)]\n        )\n        \n        df = (spark.read\n          .option(\"HEADER\", True)\n          .schema(schema)\n          .csv(inputPath_CSV))\n    \n    except ValueError:\n        print('At least, one variable format is wrong! Please check the data')\n      \n    else:\n        print('Data to score have been read successfully!')\n        return df\n\ndef preprocessing(df):\n\n    '''\n    Function to preprocess data \n    :param df: A pyspark DataFrame \n    :return: abt_to_score\n    '''\n    \n    print('Data preprocessing...')\n\n    features = df.schema.names[:-1]\n    assembler_features = VectorAssembler(inputCols=features, outputCol=\"features\")\n    abt_to_score = assembler_features.transform(df)\n    \n    print('Data have been processed successfully!')\n    return abt_to_score\n\ndef score_data(abt_to_score, modelPath):\n    \n    '''\n    Function to score data \n    :param abt_to_score: A pyspark DataFrame to score\n    :param modelPath: The modelpath associated to .zip mleap flavor\n    :return: scoredData\n    '''\n    print('Scoring process starts...')\n    \n    deserializedPipeline = PipelineModel.deserializeFromBundle(\"jar:file:{}\".format(modelPath))\n    scoredData = deserializedPipeline.transform(abt_to_score)\n    return scoredData  \n  \ndef write_output_csv(scoredData, outputPath_CSV):\n    '''\n    Function to write predictions\n    :param scoredData: A pyspark DataFrame of predictions\n    :param outputPath: The path to write the ouput table\n    :return: scoredData\n    '''\n    print('Writing Prediction in {}'.format(outputPath_CSV))\n    scoredData.toPandas().to_csv(outputPath_CSV, sep=',', index=False)\n    return scoredData.toPandas().to_dict()\n\ndef evaluator(predictions):\n    \n    '''\n    Function to produce some evaluation stats\n    :param predictions: A pyspark DataFrame of predictions\n    :return: rmse, mse, r2, mae\n    '''\n    evaluator = RegressionEvaluator(predictionCol=\"prediction\", labelCol=\"medv\")\n    rmse = evaluator.evaluate(predictions)\n    mse = evaluator.evaluate(predictions, {evaluator.metricName: \"mse\"})\n    r2 = evaluator.evaluate(predictions, {evaluator.metricName: \"r2\"})\n    mae = evaluator.evaluate(predictions, {evaluator.metricName: \"mae\"})\n    \n    return rmse, mse, r2, mae\n\ndef main():\n    \n    parser = argparse.ArgumentParser(description='Score')\n    \n    parser.add_argument('--input', dest=\"inputpath_CSV\",\n                        required=True, help='Provide the input path of data to score')\n    \n    #Mleap deserializeFromBundle method does not work with URL on GCP\n    \n    # parser.add_argument('--model', dest=\"modelPath\",\n    #                     required=True, help='Provide the model path to score')\n    \n    parser.add_argument('--output', dest=\"outputpath_CSV\",\n                        required=True, help='Provide the model path to score')\n\n    args = parser.parse_args()\n    input_path_CSV = args.inputpath_CSV\n    # modelPath = args.modelPath\n    modelPath = '/tmp/model.zip'\n    output_path_CSV = args.outputpath_CSV\n  \n    try:\n#         spark = SparkSession \\\n#         .builder \\\n#         .master(SPARK_MASTER) \\\n#         .config('spark.executor.memory', TOTAL_MEMORY) \\\n#         .config('spark.cores.max', TOTAL_CORES) \\\n#         .config('spark.jars.packages',\n#                 'ml.combust.mleap:mleap-spark-base_2.11:0.15.0,ml.combust.mleap:mleap-spark_2.11:0.15.0') \\\n#         .appName(\"ClassifierTraining\") \\\n#         .getOrCreate()\n        spark = SparkSession.builder.appName('RegressionScoring').getOrCreate()\n        spark.sparkContext.setLogLevel(\"OFF\")\n        print('Created a SparkSession')\n    \n    except ValueError:\n        warnings.warn('Check')\n  \n    #Read data\n    data_to_process = read_data_csv(spark, input_path_CSV)\n    #Preprocessing\n    abt = preprocessing(data_to_process)\n    #Scoring\n    abt_scored = score_data(abt, modelPath)\n    #Write data\n    write_output_csv(abt_scored, output_path_CSV)\n    #Evaluate Model\n    evalstats = evaluator(abt_scored)\n    return evalstats\n    \n    \nif __name__==\"__main__\":\n    \n    stats = main()\n    print('-'*20)\n    print('Process Log')\n    print('-'*20)\n    print('Scoring Job ends successfully!')\n    print(\"RMSE for the model: {}\".format(stats[0]))\n    print(\"MSE for the model: {}\".format(stats[1]))\n    print(\"R2 for the model: {}\".format(stats[2]))\n    print(\"MAE for the model: {}\".format(stats[3]))\n    print('Look at the Storage Bucket to get predictions!')\n    \n","repo_name":"mindis/Databricks_MLflow_GCP","sub_path":"2_notebooks/output/score.py","file_name":"score.py","file_ext":"py","file_size_in_byte":5906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1937988266","text":"\"\"\"\n# Definition for a Node.\nclass Node(object):\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\"\"\"\n\nclass Codec:\n    def serialize(self, root: 'Node') -> str:\n        \"\"\"Encodes a tree to a single string.\n        \n        :type root: Node\n        :rtype: str\n        \"\"\"\n        if not root:\n            return \"\"\n        q = collections.deque()\n        q.append(root)\n        res = []\n        while q:\n            node = q.popleft()\n            chs = len(node.children) if node.children else 0\n            res.append(str(node.val)+' '+str(chs))\n            for ch in node.children:\n                q.append(ch)\n        return \",\".join(res)\n\t\n    def deserialize(self, data: str) -> 'Node':\n        \"\"\"Decodes your encoded data to tree.\n        \n        :type data: str\n        :rtype: Node\n        \"\"\"\n        if not data:\n            return None\n        data = data.split(',')\n        r, c = data[0].split(' ')\n        root = Node(int(r), [])\n        q = collections.deque()\n        q.append((root,int(c)))\n        idx = 1\n        while q:\n            node, count = q.popleft()\n            for _ in range(count):\n                val, cnt = data[idx].split(' ')\n                n = Node(int(val),[])\n                node.children.append(n)\n                q.append((n, int(cnt)))\n                idx+=1\n\n        return root\n\n# Your Codec object will be instantiated and called as such:\n# codec = Codec()\n# codec.deserialize(codec.serialize(root))","repo_name":"zengtian006/LeetCode","sub_path":"Tree/Leetcode 428. Serialize and Deserialize N-ary Tree.py","file_name":"Leetcode 428. Serialize and Deserialize N-ary Tree.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"77"}
+{"seq_id":"35585309993","text":"import unittest\nimport shutil\nimport os\nfrom API.readmeProvider.LocalREADMEProvider import LocalREADMEProvider\n\n\nclass LocalREADMEProviderTestCase(unittest.TestCase):\n    first_markdown_string_content = \"\"\"\n# File Title\n> impressive tagline\n![header image](image/path.png)\n\n## Section 4\nsome great description\n\"\"\"\n\n    second_markdown_string_content = \"\"\"\n## Section 2\nREADMEs are tight!\n    \n### Section 3 child\n```\n# cool usage example\nprint('hello, world!')\n```\n\"\"\"\n\n    readmes_trees = {\n        '1.md':\n            [{\n                'level': 1,\n                'id': 'file-title',\n                'name': 'File Title',\n                'children': [\n                    {\n                        'level': 2,\n                        'id': 'section-4',\n                        'name': \"Section 4\",\n                        'children': []\n                    }\n                ]\n            }],\n        '2.md':\n            [{\n                'level': 2,\n                'id': 'section-2',\n                'name': 'Section 2',\n                'children': [\n                    {\n                        'level': 3,\n                        'id': 'section-3-child',\n                        'name': 'Section 3 child',\n                        'children': []\n                    }\n                ]\n            }]\n    }\n\n    def setUp(self):\n        self.provider = LocalREADMEProvider()\n        self.readmes_folder = 'API/readmeProvider/testreadmes'\n\n        os.mkdir(self.readmes_folder)\n\n        with open(f'{self.readmes_folder}/1.md', mode='w') as markdown_file:\n            markdown_file.write(self.first_markdown_string_content)\n\n        with open(f'{self.readmes_folder}/2.md', mode='w') as markdown_file:\n            markdown_file.write(self.second_markdown_string_content)\n\n    def test_fetch_readmes_trees(self):\n        res = self.provider.fetch_readmes_trees(self.readmes_folder)\n        self.assertEqual(res, self.readmes_trees)\n\n    def tearDown(self):\n        shutil.rmtree(self.readmes_folder)\n","repo_name":"hpbl/WRITEME","sub_path":"API/readmeProvider/test_LocalREADMEProvider.py","file_name":"test_LocalREADMEProvider.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"12070311847","text":"# pylint: disable=superfluous-parens,relative-import,old-style-class,useless-else-on-loop\n# pylint: disable=too-many-locals\n\"\"\"\nClase usada como controlador de comunicacion\n\"\"\"\nfrom time import  sleep\nfrom binascii import unhexlify\nfrom logger import transa_log\n\ndef previa_decorator(function):\n    \"\"\"\n    Funcion decorador para obtener cual es el codigo\n    de campo en el que se envia/recibe la tarjeta.\n    Con la finalidad de reducir costo de procesamiento\n    unicamente se realiza una vez antes de iniciar el flujo.\n    \"\"\"\n    def wrapper(*args, **kwargs):\n        \"\"\"\n        Como toda funcion decorador, es necesario un wrapper;\n        obtiene cual es el codigo de campo para enviar/recibir\n        el nro de tarjeta.\n        \"\"\"\n        controller = list(args).pop()\n        business_conf = controller.business_conf\n        if(controller.previa_activated):\n            fields = business_conf.get_fields_definition()\n            card_field = [k for k, v in fields.iteritems() if v['description'] == 'tarjeta'].pop()\n            kwargs.update(card_field=card_field)\n        return function(*args, **kwargs)\n    return wrapper\n\nclass CommnController:\n    \"\"\"\n    Clase encargada de enviar y recibir la comunicacion con\n    el datafono.\n    \"\"\"\n    def __init__(self, **kwargs):\n        self.message_rdg = kwargs.get('message_rdg')\n        self.message_bldg = kwargs.get('message_bldg')\n        self.business_conf = kwargs.get('business_conf')\n        self.user_conf = kwargs.get('user_conf')\n        self.file_action = kwargs.get('file_action')\n        self.previa_activated = self.user_conf.get_previa()\n        self.connection = None\n\n    def initializer(self, connection, data):\n        \"\"\"\n        Metodo que inicializa la comunicacion con el datafono,\n        * Recibe los mensajes enviados por el datafono\n        * Valida el tipo de flujo (Unificado o no unificado)\n        * Envia los mensajes al datafono\n\n        Parametros\n        ----------\n\n        connection: type(COMConnection) -> Instancia del tipo de comunicacion definida.\n        \"\"\"\n        result = None\n        built_fields = self.built_fields(data)\n        self.connection = connection\n        understood = self.business_conf.get_understood_value()\n        understood = self.message_bldg.get_byte_from_hex(understood, is_hexa=True)\n        not_understood = self.business_conf.get_not_understood_value()\n        not_understood = self.message_bldg.get_byte_from_hex(not_understood, is_hexa=True)\n        transa_log(\"INICIO TRANSACCION\")\n        transa_log(\"CONFIGURACION\", user_conf=self.user_conf)\n        # Se obtiene los datos enviados por el datafono\n        dataphone_msg = self.connection.read(who_send=\"DATAFONO\")\n        # Si hubo respuesta se procesa el mensaje\n        if(dataphone_msg != ''):\n            try:\n            # para enviar el codigo de operacion\n            # Si es flujo unificado, obtiene cual es el campo utilizado\n                is_unified, field_code, disassembled_msg = self.identify_flow(dataphone_msg)\n                if(is_unified):\n                # Transmision de mensajes para flujo unificado\n                # Retorna los ultimos valores enviados por el datafono\n                    kwargs = dict()\n                    kwargs.update([('message', disassembled_msg), ('op_type', data[0])])\n                    kwargs.update([('field_code', field_code), ('built_fields', built_fields)])\n                    kwargs.update(understood=understood, not_understood=not_understood)\n                    result = self.unified_flow(**kwargs)\n                    transa_log(\"TRANSACCION COMPLETADA\")\n                else:\n                    cod = disassembled_msg[\"HEADER_FIELDS\"][\"cod_trans\"]\n                    msg = \"99, Flujo no unificado sin implementar. Cod. Trans '{}'\".format(cod)\n                    self.connection.write(not_understood, is_hexa=True, who_send=\"CAJA\")\n                    transa_log(msg)\n                    transa_log(\"TRANSACCION COMPLETADA\")\n                    raise Exception(msg)\n            except (Exception, ValueError) as error:\n                transa_log(error)\n                transa_log(\"FIN TRANSACCION\")\n                raise error\n        else:\n            # Si se agota el tiempo de espera y no hubo respuesta se interrumpe la ejecucion\n            err_msg = \"99, No se establecio conexion con el datafono. Tiempo de espera agotado\"\n            transa_log(err_msg)\n            transa_log(\"FIN TRANSACCION\")\n            raise Exception(err_msg)\n\n        return result\n\n    @previa_decorator\n    def unified_flow(self, **kwargs):\n        \"\"\"\n            Funcion que corresponde a la secuencia de una transaccion\n            con flujo unificado. Esta secuenca de transmision de mensajes\n            termina cuando el datafono responde con el valor \"1\" en el campo\n            req_res.\n\n            Parametros (kwargs):\n            --------------------\n            message: type(dict) -> Mensaje recibido por el datafono desensamblado.\n            Por ejemplo: {\n                'LRC': '\\\\x02',\n                'BCD': '\\\\x007',\n                'DATA': [('C33', ''), ('C40', ''), ('C69', ''), ('C93', '')],\n                'ETX': '\\\\x03',\n                'CONFIRM_MESSAGE': '\\\\x06',\n                'HEADER_FIELDS': {u'cod_trans': '99', u'indicator': '0', ...},\n                'STX': '\\\\x02'\n            }\n\n            built_fields: type(list) -> Datos de entrada previamente procesados.\n            Cada campo cumple con una longitud y tipo de dato especifico, definido\n            en el archivo de configuracion del negocio.\n            Por ejemplo:\n            [\n                ('C33', 'cajero1   '),\n                ('C40', '000000080000'),\n                ('C41', '000000000000'),\n                ...\n            ]\n            field_code: type(str) -> Corresponde al campo el cual sera utilizado\n            para enviar el tipo de operacion.\n            Por ejemplo: \"C69\"\n\n            op_type: type(str) -> Tipo de operacion a realizar.\n            Por ejemplo: \"00\"\n\n            card_field: type(str) -> (*Opcional) Codigo de campo en el que se recibe\n            la tarjeta, el decorador \"previa_decorator\" aniade el campo si el \"Descuento\n            con BIN\" esta activado.\n\n            understood: type(Bytearray) -> Valor hexadecimal para indicar que el mensaje\n            fue entendido.\n\n            not_understood: type(Bytearray) -> Valor hexadecimal para indicar que el mensaje\n            no fue entendido.\n        \"\"\"\n        dataphone_msg = kwargs.get('message')\n        card_field = kwargs.get('card_field', None)\n        understood = kwargs.pop(\"understood\")\n        not_understood = kwargs.pop(\"not_understood\")\n\n        kwargs.pop(\"card_field\", None)\n        # Mientras la respuesta del datafono no sea 1, se ejecutara lo siguiente\n        while dataphone_msg[\"HEADER_FIELDS\"][\"req_res\"] == '0':\n            if(self.previa_activated and card_field != None):\n                msg_fields = dataphone_msg[\"DATA\"]\n                card_value = [value for field, value in msg_fields if card_field == field]\n                if(card_value):\n                    card_value = card_value.pop()\n                    kwargs['built_fields'] = self.bin_discount(card_value)\n\n            data_values, operation = self.message_rdg.generate_inputs(**kwargs)\n            op_type = kwargs.get(\"op_type\")\n            _, message = self.message_bldg.build_message(data_values, operation, op_type)\n            # Envia el mensaje y registra en el log\n            self.connection.write(message, is_hexa=True, who_send=\"CAJA\")\n            # Recibe el mensaje y registra en el log\n            dataphone_res = self.connection.read(who_send=\"DATAFONO\")\n\n            if(len(dataphone_res) <= 1):\n                msg = \"99, Transaccion incompleta, tiempo de espera agotado.\"\n                self.connection.write(not_understood, is_hexa=True, who_send=\"CAJA\")\n                raise Exception(msg)\n\n            have_field = self.have_field_understood(dataphone_res)\n            dataphone_msg = self.message_rdg.read_message(dataphone_res, have_field)\n            kwargs['message'] = dataphone_msg\n        else:\n            if(dataphone_msg[\"HEADER_FIELDS\"][\"req_res\"] == '1'):\n                self.connection.write(understood, is_hexa=True, who_send=\"CAJA\")\n                return dataphone_msg[\"DATA\"]\n\n    def identify_flow(self, dataphone_msg):\n        \"\"\"\n        Identifica el tipo de flujo a partir de un mensaje recibido.\n\n        Parametros\n        ----------\n        dataphone_msg: type(str) -> Mensaje sin procesar enviado por el datafono.\n        Por ejemplo: \"..760000000001099..033..40..69..93...\"\n\n        Return\n        ------\n        tuple (bool, str, dict) -> El primer valor representa si es flujo unificado,\n        el segundo valor regresa cual es el campo para indicar el tipo de operacion\n        (es usado unicamente cuando es flujo unificado), el tercer valor representa\n        el mensaje enviado por el datafono pero desensamblado.\n        Por ejemplo:\n        (\n            True, -> es flujo unificado\n            \"C69\", -> campo utilizado para enviar el tipo de operacion\n            dict -> {\n            'LRC': '\\\\x02',\n            'BCD': '\\\\x007',\n            'DATA': ['33\\\\x00\\\\x00', ...],\n            'HEADER_FIELDS': ...\n        )\n\n        \"\"\"\n        try:\n            # Se evalua si el mensaje contiene el campo \"CONFIRM_MESSAGE\",\n            have_field = self.have_field_understood(dataphone_msg)\n            disassembled_msg = self.message_rdg.read_message(dataphone_msg, have_field)\n        except ValueError as error:\n            raise ValueError(str(error))\n        # Se ejecuta si no se presenta ningun error.\n        else:\n            # Se obtiene el cod_trans del desensamble\n            cod_transaction = disassembled_msg[\"HEADER_FIELDS\"][\"cod_trans\"]\n            # Del campo cod_trans se conoce si es flujo unificado o no.\n            is_unified, field_code = self.business_conf.get_flow_type(cod_transaction)\n            return is_unified, field_code, disassembled_msg\n\n    def have_field_understood(self, dataphone_msg):\n        \"\"\"\n            Funcion que identifica si el mensaje contiene el campo \"CONFIRM_MESSAGE\".\n\n            Parametros\n            ----------\n            dataphone_msg: type(str) -> Mensaje sin procesar enviado por el datafono\n\n            Return\n            ------\n            bool -> Retorna verdadero (True) si el mensaje cuenta con el campo \"CONFIRM_MESSAGE\",\n            de lo contrario retornara falso (False)\n        \"\"\"\n        message_definition = self.business_conf.get_message_definition()\n        understoods_items = self.business_conf.get_understoods()\n        # Se convierten valores como \"06\" en hexa \"\\x06\".\n        understoods_items = [unhexlify(value) for value in understoods_items]\n        structure = message_definition[\"structure\"]\n        us_idx = structure.index(\"CONFIRM_MESSAGE\")\n        min_idx = sum([message_definition[f][\"max_length\"]  for f in structure[0:us_idx]])\n        max_idx = min_idx + message_definition[\"CONFIRM_MESSAGE\"][\"max_length\"]\n        understood_value = dataphone_msg[min_idx: max_idx]\n        return understood_value in understoods_items\n\n    def built_fields(self, data):\n        \"\"\"\n        Funcion encargada de relacionar los datos\n        de entrada con los campos de una operacion.\n        Esto ocurre con todas las operaciones que hagan\n        match con la logintud de campos de entrada.\n\n        data: type(list) -> Lista de datos que componen una operacion.\n\n        Return\n        ------\n        list -> Datos de entrada previamente procesados.\n        Cada campo cumple con una longitud y tipo de dato especifico, definido\n        en el archivo de configuracion del negocio.\n            Por ejemplo:\n            [\n                ('C33', 'cajero1   '),\n                ('C40', '000000080000'),\n                ('C41', '000000000000'),\n                ...\n            ]\n        \"\"\"\n        is_cashier_incl = self.user_conf.get_cashier_flag()\n        # Se obtiene la operacion enviada por la caja\n        operation = self.business_conf.get_operation(data, is_cashier_incl)\n        is_list_of_lists = [isinstance(oper, list) for oper in operation].pop()\n        # Se construye los campos posibles a enviar\n        if(is_list_of_lists):\n            temp = list()\n            for oper in operation:\n                built_fields, _ = self.message_bldg.build_message(data, oper, data[0])\n                temp += built_fields\n            built_fields = temp\n        else:\n            built_fields, _ = self.message_bldg.build_message(data, operation, data[0])\n\n        return set(built_fields)\n\n    def bin_discount(self, card_value):\n        \"\"\"\n        Funcion utilizada para el descuento bin, se encarga\n        de escribir el nro de la tarjeta (card_value) en el\n        archivo de salida, espera el tiempo definido en las config.\n        de usuario y vuelve a leer los datos de entrada.\n\n        Antes de realizar el return construye los datos de entrada\n        con el codigo de campo correspondiente a una operacion.\n        \"\"\"\n        input_file = self.user_conf.get_input_file_name()\n        output_file = self.user_conf.get_output_file_name()\n        time_sleep = self.user_conf.get_sleep_time()\n        self.file_action.write_output_data(card_value, output_file)\n        sleep(time_sleep)\n        data = self.file_action.get_input_data(input_file)\n        return self.built_fields(data)\n","repo_name":"diana1ariza1/IntegracionCaja","sub_path":"redeco_core/utils/commn_controller.py","file_name":"commn_controller.py","file_ext":"py","file_size_in_byte":13460,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71239182329","text":"n = input(\"enter the number\")\npw = len(n)\ns = 0\nfor i in n:\n    s += int(i)**pw\n \nif int(n) == s:\n    print(\"Armstrong number\", s)\nelse:\n    print(\"Not a armstrong number\")\n","repo_name":"Gokul58/python-program","sub_path":"armstrong1.py","file_name":"armstrong1.py","file_ext":"py","file_size_in_byte":173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36808652058","text":"import nextcord\r\nfrom nextcord.ext import commands, tasks\r\nfrom nextcord.abc import GuildChannel\r\nfrom nextcord import Interaction, ChannelType\r\nfrom config import TOKEN, PREFIX, OWNER\r\nimport os\r\nimport aiosqlite\r\n\r\nintents = nextcord.Intents().all()\r\nbot = commands.Bot(command_prefix=PREFIX, intents=intents, case_insensitive=True, allowed_mentions=nextcord.AllowedMentions(\r\n        users=True,\r\n        everyone=False,\r\n        roles=False,\r\n        replied_user=True,\r\n    )\r\n)\r\n\r\nfor fn in os.listdir(\"./cogs\"):\r\n    if fn.endswith(\".py\"):\r\n        bot.load_extension(f\"cogs.{fn[:-3]}\")\r\n\r\n@bot.event\r\nasync def on_ready():\r\n    print(f\"Bot ID: {bot.user.id}\\nBot Name: {bot.user.name}\")\r\n    setattr(bot, \"db\", await aiosqlite.connect(\"main.db\"))\r\n\r\n@bot.command()\r\n@commands.is_owner()\r\nasync def load(ctx, extension):\r\n    try:\r\n        bot.load_extension(f\"cogs.{extension}\")\r\n    except commands.ExtensionAlreadyLoaded:\r\n        return await ctx.send(\"Cog is already loaded\")\r\n    except commands.ExtensionNotFound:\r\n        return await ctx.send(\"Cog is not found\")\r\n    await ctx.send(\"Cog loaded\")\r\n\r\n@bot.command()\r\n@commands.is_owner()\r\nasync def reload(ctx, extension):\r\n    try:\r\n        bot.reload_extension(f\"cogs.{extension}\")\r\n    except commands.ExtensionNotFound:\r\n        return await ctx.send(\"Cog is not found\")\r\n    await ctx.send(\"Cog reloaded\")\r\n\r\n@bot.command()\r\n@commands.is_owner()\r\nasync def unload(ctx, extension):\r\n    try:\r\n        bot.unload_extension(f\"cogs.{extension}\")\r\n    except commands.ExtensionNotFound:\r\n        return await ctx.send(\"Cog is not found\")\r\n    await ctx.send(\"Cog unloaded\")\r\n\r\n@bot.command()\r\n@commands.is_owner()\r\nasync def check(ctx, cog_name):\r\n    try:\r\n        bot.load_extension(f\"cogs.{cog_name}\")\r\n    except commands.ExtensionAlreadyLoaded:\r\n        await ctx.send(\"Cog is loaded\")\r\n    except commands.ExtensionNotFound:\r\n        await ctx.send(\"Cog not found\")\r\n    else:\r\n        await ctx.send(\"Cog is unloaded\")\r\n        bot.unload_extension(f\"cogs.{cog_name}\")\r\n\r\n@bot.event\r\nasync def on_application_command_error(ctx, error):\r\n        if isinstance(error, commands.NotOwner):\r\n            em = nextcord.Embed(\r\n                title=\"Owner Only Command\",\r\n                description=f\"Only the bot owner `{OWNER}` is allowed to run this command.\",\r\n            )\r\n            await ctx.send(embed=em, delete_after=20)\r\n            return\r\n        else:\r\n            em = nextcord.Embed(title=\"Error Occured\", description=f\"```{error}```\")\r\n            await ctx.send(embed=em, delete_after=30)\r\n            return\r\n\r\nbot.run(TOKEN)\r\n","repo_name":"Glowstik-YT/giveawayBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"38233222142","text":"import os, time\r\nfrom threading import Thread\r\n\r\ndef flag_remover():\r\n    while True:\r\n        try:\r\n            time.sleep(3)\r\n            os.system(\"rm -rf /app/uploads/admin/*\")\r\n            os.system(\"rm -rf /app/static/uploads/admin/*\")\r\n        except:\r\n            continue\r\n\r\ndef userfile_remover():\r\n    while True:\r\n        try:\r\n            time.sleep(600)\r\n            os.system(\"rm -rf /app/uploads/*/*\")\r\n            os.system(\"rm -rf /app/static/uploads/*/*\")\r\n        except:\r\n            continue\r\n\r\nth1 = Thread(target=flag_remover)\r\nth2 = Thread(target=userfile_remover)\r\nth1.start()\r\nth2.start()\r\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/WACON/2023/Quals/web/mosaic/file_remover.py","file_name":"file_remover.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"21677571415","text":"\"\"\"Base Dataset Loader Class.\n\"\"\"\nimport os\nimport sys\nimport pickle\nimport numpy as np\nfrom glob import glob\nfrom typing import List\nfrom pathlib import Path\nfrom collections import namedtuple\nfrom datasets.model_parser import ModelParser\n\nsys.path.insert(0, 'datasets/')\n\n\nreconstruction_data = namedtuple('reconstruction_data',\n    'intrinsics distortion_coefficients points_2D points_3D')\n\n\nclass BaseDataset():\n    \"\"\"Dataset Base Loader Class.\n    \"\"\"\n    def __init__(self, root: str,\n                       image_folder: str,\n                       reference_sequences: List[str],\n                       query_sequences: List[str],\n                       nvm_model: str=None,\n                       binary_model: str=None,\n                       bundler_out_model: str=None,\n                       bundler_txt_model: str=None,\n                       triangulation_data_file: str=None,\n                       name: str=None):\n        \"\"\"Initialize base class attributes.\n\n        Args:\n            root: The root to the dataset folder.\n            image_folder: The folder containing the images.\n            reference_sequences: The name of the reference sequences (should\n                match the folder names).\n            query_sequences: The name of the query sequences (should\n                match the folder names).\n            nvm_model: The path to a NVM reconstruction from VisualSfM.\n            binary_model: The path to a binary COLMAP reconstruction file.\n            bundler_out_model: The path to a bundler .out file.\n            bundler_txt_model: The path to the matching bundler .list.txt file.\n            triangulation_data: The path to the triangulated .npz file.\n            name: The dataset name.\n        \"\"\"\n        self._data = {\n            'name': name,\n            'root': root,\n            'image_folder': image_folder,\n            'reference_sequences': reference_sequences,\n            'query_sequences': query_sequences,\n            'nvm_model': nvm_model,\n            'binary_model': binary_model,\n            'bundler_out_model': bundler_out_model,\n            'bundler_txt_model': bundler_txt_model,\n            'triangulation_data_file': triangulation_data_file\n        }\n        self.load_reference_image_names()\n        self.load_query_image_names()\n        self.load_reference_camera_poses()\n        self.load_triangulation_data()\n\n    def _glob_fn(self, root: str):\n        \"\"\"Glob pattern to fetch images.\"\"\"\n        return glob(str(Path(root, '**/*.jpg')))\n\n    def _assemble_intrinsics(self, focal, cx, cy, distortion):\n        \"\"\"Assemble intrinsics matrix from parameters.\"\"\"\n        intrinsics = np.eye(3)\n        intrinsics[0,0] = float(focal)\n        intrinsics[1,1] = float(focal)\n        intrinsics[0,2] = float(cx)\n        intrinsics[1,2] = float(cy)\n        distortion_coefficients = np.array([float(distortion), 0.0, 0.0, 0.0])\n        return intrinsics, distortion_coefficients\n\n    def load_reference_image_names(self):\n        \"\"\"Load reference image names.\"\"\"\n\n        image_roots = [Path(self._data['root'], self._data['image_folder'], f)\n            for f in self._data['reference_sequences']]\n        reference_image_names = [self._glob_fn(r) for r in image_roots]\n        reference_image_names = [i for s in reference_image_names for i in s]\n        if not len(reference_image_names):\n            path = str(Path(self._data['root'], self._data['image_folder']))\n            raise Exception('No reference image found at {}'.format(path))\n        print('>> Found {} reference images.'.format(\n            len(reference_image_names)))\n        self._data['reference_image_names'] = reference_image_names\n\n    def load_query_image_names(self):\n        \"\"\"Load query image names.\"\"\"\n\n        image_roots = [Path(self._data['root'], self._data['image_folder'], f)\n            for f in self._data['query_sequences']]\n        query_image_names = [self._glob_fn(r) for r in image_roots]\n        query_image_names = [i for s in query_image_names for i in s]\n        if not len(query_image_names):\n            path = str(Path(self._data['root'], self._data['image_folder']))\n            raise Exception('No query image found at {}'.format(path))\n        print('>> Found {} query images.'.format(len(query_image_names)))\n        self._data['query_image_names'] = query_image_names\n\n    def load_reference_camera_poses(self):\n        \"\"\"Load dataset ground truth camera poses from the reconstructions.\"\"\"\n\n        if self._data['nvm_model']:\n            print('>> Loading poses from {}'.format(self._data['nvm_model']))\n            self._data['reconstruction_data'] = ModelParser.from_nvm(\n                self._data['nvm_model'])\n        elif self._data['binary_model']:\n            print('>> Loading poses from {}'.format(self._data['binary_model']))\n            self._data['reconstruction_data'] = ModelParser.from_binary(\n                self._data['binary_model'])\n        elif self._data['bundler_out_model'] and self._data['bundler_txt_model']:\n            print('>> Loading poses from {}'.format(\n                self._data['bundler_out_model']))\n            self._data['reconstruction_data'] = ModelParser.from_bundler(\n                self._data['bundler_out_model'],\n                self._data['bundler_txt_model'])\n        else:\n            pass\n            raise Exception('No reconstruction model was provided! Please' \\\n                            'provide a link to a [.out|.nvm|.bin] file.')\n\n    def load_triangulation_data(self):\n        \"\"\" Load triangulation data.\n\n        Returns:\n            A dictionary mapping reference image filenames to triangulation\n            data (intrinsics matrix, distortion coefficients, 2D and 3D points)\n        \"\"\"\n        assert os.path.isfile(self._data['triangulation_data_file'])\n        triangulation_data = np.load(self._data['triangulation_data_file'], allow_pickle=True)\n        filename_to_local_reconstruction = dict()\n        for filename in self._data['reference_image_names']:\n            key = self.key_converter(filename)\n            if key in triangulation_data.files:\n                local_reconstruction = triangulation_data[key].item()\n                intrinsics, distortion_coefficients = self._assemble_intrinsics(\n                    *local_reconstruction['K'].params)\n                points_3D = local_reconstruction['points3D']\n                points_2D = local_reconstruction['points2D']\n                filename_to_local_reconstruction[filename] = \\\n                    reconstruction_data(intrinsics, distortion_coefficients,\n                        points_2D, points_3D)\n        self._data['filename_to_local_reconstruction'] = \\\n            filename_to_local_reconstruction\n\n    def key_converter(self, filename: str):\n        \"\"\"Convert an absolute filename the keys format in the 3D files.\"\"\"\n        return '/'.join(filename.split('/')[-3:])\n\n    def output_converter(self, filename: str):\n        \"\"\"Convert an absolute filename the output prediction format.\"\"\"\n        return '/'.join(filename.split('/')[-2:])\n\n    @property\n    def data(self):\n        return self._data\n","repo_name":"germain-hug/S2DHM","sub_path":"s2dhm/datasets/base_dataset.py","file_name":"base_dataset.py","file_ext":"py","file_size_in_byte":7120,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"77"}
+{"seq_id":"20640969153","text":"import bpy\nimport bmesh\nimport os\nimport time\nimport re\n\n\n\"\"\"\nTips\n    run within blender 2.8.2!!!\n\nTish script aims to reduce the vertex number of an obj file\nwhen its vertex number is bigger than a threshold, otherwise, \ndo nothing. And rename the exported filename with a format:\n    \"{}-{}k.obj\".format(original_filename, vertex_count//1000)\n    \nVariables:\n    1. verts_thres: exported obj with max vertex count, usually, 200K\n    2. verts_max  : reduce vertex number by DECIMATE to verts_thres.   \n\n\"\"\"\n\ndef preprocess(root_path, dir_names, verts_thres):\n\n\n    bpy.ops.object.select_all(action='SELECT')\n    bpy.ops.object.delete(use_global=False)\n    print(f\"start to process preparation for mesh operation...\")\n    \n    \"\"\"\n    dir_names = ['bear', 'bird', 'buddha', 'bull', 'bunny', 'butterfly', \\\n               'cat', 'cock', 'cow', 'crocodile', \\\n               'deer', 'dog', 'dolphin', 'dragon', \n               'elephant','fish', 'fox', 'frog', \\\n               'giraffe', 'goose', 'gorilla', \\\n               'head', 'horse', 'human_body', 'kangaroo', \\\n               'lion', 'lizard', 'mix', 'mouse', \\\n               'panda', 'parrot', 'penguin', 'pig', 'pillow', \\\n               'rabbit', 'sheep', 'snake', 'squirrel', \\\n               'tiger', 'turtle', 'whale', 'wolf']\n    \"\"\"\n    verts_max = verts_thres * 4 // 3  \n    \n    file_root = []\n    for name in dir_names:\n        file_root.append(os.path.join(root_path, name)+\"/\")\n    #print(file_root)\n    start_time = time.time()\n    for path in file_root:\n        for subdir, dirs, files in os.walk(path):\n            for file in files:\n          \n               if file.endswith(\".obj\") or file.endswith(\".OBJ\"):\n                   if not re.search(r'-(.*)k.obj', file, re.M|re.I):\n                       print('file:', file) \n             \n                       bpy.ops.object.select_all(action='SELECT')\n                       bpy.ops.object.delete(use_global=False)\n                       file_in = os.path.join(subdir, file) \n                       print(file_in)\n\n                       bpy.ops.import_scene.obj(filepath=file_in)\n  \n                       # active the imported object\n                       obj = bpy.context.scene.objects[0]\n             \n                       bpy.context.view_layer.objects.active = obj\n                       # get the original vertex count of selected object\n                       bm_ori = bmesh.new()\n                       depsgraph = bpy.context.evaluated_depsgraph_get()\n                       bm_ori.from_object(bpy.context.object, depsgraph)\n             \n                       v_count_ori = len(bm_ori.verts)\n                       print('  ori verts:',v_count_ori)\n             \n\n                       if v_count_ori < verts_thres:\n                          # rename the name \n                          name = v_count_ori // 1000\n                          file_out = file_in[:-4] + \"-\" + str(name) + \"k.obj\"\n                          print(f\"    file_out:{file_out}\")\n                          #assert 1 == 0\n                          if not os.path.exists(file_out):   \n                              bpy.ops.export_scene.obj(filepath=file_out)\n                          else:\n                              print('  file exists.') \n                       elif v_count_ori < verts_max: #66k,132k,199k,266k\n                          # reduce the vertex number\n                          bpy.ops.object.modifier_add(type='DECIMATE')\n                          # process separately according to vertex number\n                \n                \n                          ratio = verts_thres / v_count_ori\n                     \n                          if ratio > 0.75 and ratio <= 1:\n                              bpy.context.object.modifiers[\"Decimate\"].ratio = ratio \n                \n                \n                          #mod.use_collapse_triangulate=True\n                          bm_mod = bmesh.new()\n                          depsgraph = bpy.context.evaluated_depsgraph_get()\n                          bm_mod.from_object(bpy.context.object, depsgraph)\n                          v_count_mod = len(bm_mod.verts)\n                          print('  mod verts:',v_count_mod)\n                          rough_count = v_count_mod // 1000\n                          # save decimated object\n                          file_out = file_in[:-4] + \"-\" + str(rough_count) + \"k.obj\"\n                          if not os.path.exists(file_out):\n                             bpy.ops.export_scene.obj(filepath=file_out)\n                          else:\n                             print('  file exists.')\n                \n                \n    print('run time : {:4f}m'.format((time.time() - start_time)/60))\n    print('done!')\n    \ndef main():\n    path_to_data = \"./data/\"\n    # change the following variables\n    dir_names = ['bear']\n    #verts_min = 150000 #50k,100k,150k,200k\n    verts_thres = 50000\n\n    preprocess(root_path, dir_names, verts_thres)\n       \n    \n    \n    \nif __name__ == \"__main__\":\n    main()\n    \n\n","repo_name":"Alibabade/Mesh-preprocessor-in-blender-with-python","sub_path":"lib/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10349050743","text":"import pyautogui as pg\n\n##GET SCREEN SIZE\nprint(pg.size())\n\n##MOVE MOUSE TO POSITION\npg.click()\npg.moveTo(1320,660,duration=4)\n\n##CLICK THE MOUSE\ni=1\nwhile i<1000:\n    pg.click(1320,711)\n    i=i+1\n","repo_name":"bpc5604/autoclickpy","sub_path":"autoclicker.py","file_name":"autoclicker.py","file_ext":"py","file_size_in_byte":197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2538668022","text":"import random\r\n\r\n\r\nlist = []\r\n\r\nfor i in range(random.randint(1, 1000)):\r\n    list.append(random.randint(1, 1000))\r\n\r\nlist.sort()\r\n\r\nprint(\"Массив: \" + str(list))\r\n\r\ntry:\r\n    check = int(input(\"Введите искомое число: \"))\r\nexcept:\r\n    print(\"Эээ, ты что-то не то ввел. Программа закрывается\")\r\n    exit()\r\n\r\nwhile len(list) != 1:\r\n    if check >= list[len(list) // 2]:\r\n        list = list[len(list) // 2:]\r\n        print(list)\r\n    else:\r\n        list = list[:len(list) // 2]\r\n        print(list)\r\n\r\nprint(list)\r\nif list[0] != check:\r\n    print(\"Числа нет в списке\")\r\n","repo_name":"GrandPLAY/Martin_Yaroslav_213","sub_path":"Binarka.py","file_name":"Binarka.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41703050519","text":"from pyspark.sql import *\nfrom pyspark.sql.functions import lit, col\nfrom pyspark.sql.types import StringType\nimport os\n\n\ndef aggregate_trip_distance(df):\n    return df.groupBy(\"trip_distance\").count()\n\n\ndef combine_taxi_types(green_df, yellow_df):\n    return green_df.withColumn(\"taxi_type\", lit(\"green\")).union(\n        yellow_df.withColumn(\"taxi_type\", lit(\"yellow\"))\n    )\n\n\nif __name__ == \"__main__\":\n    source_location = os.environ[\"SOURCE_LOCATION\"]\n    target_location = os.environ[\"TARGET_LOCATION\"]\n    spark = SparkSession.builder.getOrCreate()\n    green_df = spark.read.parquet(f\"{source_location}/green*\")\n    yellow_df = spark.read.parquet(f\"{source_location}/yellow*\")\n\n    green_agg_df = aggregate_trip_distance(green_df)\n    yellow_agg_df = aggregate_trip_distance(yellow_df)\n\n    combined_df = combine_taxi_types(green_agg_df, yellow_agg_df)\n    combined_df.write.partitionBy(\"taxi_type\").mode(\"overwrite\").parquet(\n        f\"{target_location}/nyc_taxis/agg_trip_distance/\"\n    )\n","repo_name":"awslabs/data-solutions-framework-on-aws","sub_path":"examples/spark-data-lake/spark/src/agg_trip_distance.py","file_name":"agg_trip_distance.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"38545724886","text":"import numpy as np\nimport cv2\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\nfrom scipy.optimize import least_squares\nfrom scipy.optimize import minimize\nfrom scipy.linalg import rq\n\nimport time\n\ndef objective_func(x, **kwargs):\n    \"\"\"\n        Calculates the difference in image (pixel coordinates) and returns \n        it as a 2*n_points vector\n\n        Args: \n        -        x: numpy array of 11 parameters of P in vector form \n                    (remember you will have to fix P_34=1) to estimate the reprojection error\n        - **kwargs: dictionary that contains the 2D and the 3D points. You will have to\n                    retrieve these 2D and 3D points and then use them to compute \n                    the reprojection error.\n        Returns:\n        -     diff: A 2*N_points-d vector (1-D numpy array) of differences between \n                    projected and actual 2D points. (the difference between all the x\n                    and all the y coordinates)\n\n    \"\"\"\n    \n    ##############################\n    pts2d = kwargs['pts2d']\n    pts3d = kwargs['pts3d']\n    x = np.append(x, 1)\n    projection_matrix = np.reshape(x, (3,4))\n    proj = projection(projection_matrix, pts3d)\n    diff = np.reshape(proj - pts2d, (1,pts2d.shape[0]*2))[0]\n    ##############################\n      \n    return diff\n\ndef projection(P: np.ndarray, points_3d: np.ndarray) -> np.ndarray:\n    \"\"\"\n        Computes projection from [X,Y,Z,1] in homogenous coordinates to\n        (x,y) in non-homogenous image coordinates.\n\n        Args:\n        -  P: 3x4 projection matrix\n        -  points_3d : n x 4 array of points [X_i,Y_i,Z_i,1] in homogenouos coordinates\n                       or n x 3 array of points [X_i,Y_i,Z_i]\n\n        Returns:\n        - projected_points_2d : n x 2 array of points in non-homogenous image coordinates\n    \"\"\"\n    \n    ##############################\n    Xi = points_3d[:,0]\n    Yi = points_3d[:,1]\n    Zi = points_3d[:,2]\n    xi = (P[0,0]* Xi +P[0,1]* Yi +P[0,2]* Zi +P[0,3])/(P[2,0]* Xi +P[2,1]* Yi +P[2,2]* Zi +P[2,3])\n    yi = (P[1,0]* Xi +P[1,1]* Yi +P[1,2]* Zi +P[1,3])/(P[2,0]* Xi +P[2,1]* Yi +P[2,2]* Zi +P[2,3])\n    projected_pts = []\n    point = 0\n    while point < xi.size:\n        x = xi[point]\n        y = yi[point]\n        projected_pts.append([x , y])\n        point += 1        \n    projected_points_2d = np.asarray(projected_pts)\n    ##############################\n    \n    return projected_points_2d\n\ndef estimate_camera_matrix(pts2d: np.ndarray, \n                           pts3d: np.ndarray, \n                           initial_guess: np.ndarray) -> np.ndarray:\n    '''\n        Calls least_squres form scipy.least_squares.optimize and\n        returns an estimate for the camera projection matrix\n\n        Args:\n        - pts2d: n x 2 array of known points (x_i, y_i) in image coordinates \n        - pts3d: n x 3 array of known points in 3D, (X_i, Y_i, Z_i, 1) \n        - initial_guess: 3x4 projection matrix initial guess\n\n        Returns:\n        - P: 3x4 estimated projection matrix \n\n        Note: Because of the requirements of scipy.optimize.least_squares\n              you will have to pass the projection matrix P as a vector.\n              Since we will fix P_34 to 1 you will not need to pass all 12\n              matrix parameters. \n            \n              You will also have to put pts2d and pts3d into a kwargs dictionary\n              that you will add as an argument to least squares.\n              \n              We recommend that in your call to least_squares you use\n              - method='lm' for Levenberg-Marquardt\n              - verbose=2 (to show optimization output from 'lm')\n              - max_nfev=50000 maximum number of function evaluations\n              - ftol \\\n              - gtol  --> convergence criteria\n              - xtol /\n              - kwargs -- dictionary with additional variables \n                          for the objective function\n    '''\n\n    start_time = time.time()\n     \n    ##############################\n    init_guess = initial_guess.flatten()[:-1]\n    estimate = least_squares(fun=objective_func, x0=init_guess, method = 'lm', max_nfev = 50000, kwargs = {'pts2d': pts2d, 'pts3d': pts3d})\n\n    M = np.append(estimate.x, 1.).reshape((3, 4))\n    ##############################\n    \n    print(\"Time since optimization start\", time.time() - start_time)\n\n    return M\n\ndef decompose_camera_matrix(P: np.ndarray) -> (np.ndarray, np.ndarray):\n    '''\n        Decomposes the camera matrix into the K intrinsic and R rotation matrix\n        \n        Args:\n        -  P: 3x4 numpy array projection matrix\n        \n        Returns:\n\n        - K: 3x3 intrinsic matrix (numpy array)\n        - R: 3x3 orthonormal rotation matrix (numpy array)\n\n        hint: use scipy.linalg.rq()\n    '''\n    \n    ##############################\n    K, R = rq(P[:,:3])\n    ##############################\n    \n    return K, R\n\ndef calculate_camera_center(P: np.ndarray,\n                            K: np.ndarray, \n                            R_T: np.ndarray) -> np.ndarray:\n    \"\"\"\n    Returns the camera center matrix for a given projection matrix.\n\n    Args:\n    -   P: A numpy array of shape (3, 4) representing the projection matrix\n\n    Returns:\n    -   cc: A numpy array of shape (1, 3) representing the camera center\n            location in world coordinates\n    \"\"\"\n\n    ##############################\n    dot_inner = np.dot(-P[:,0:3].T, -P[:,0:3])\n    invert = np.linalg.inv(dot_inner)\n    dot_outer = np.dot(-P[:,0:3].T, P[:,3])\n    cc = np.dot(invert, dot_outer)\n    ##############################\n\n    return cc\n\n\n\n\n\n\n","repo_name":"ARathie/Computer-Vision","sub_path":"proj3_v3 copy/proj3_code/projection_matrix.py","file_name":"projection_matrix.py","file_ext":"py","file_size_in_byte":5625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8820700386","text":"import pytest\n\nfrom container.request_context import current_session\nfrom src.places.db.place_dbo import PlaceDBO\nfrom tests.integration.helpers import get_default_place_dbo\n\n\nclass TestRepository:\n\n    @pytest.mark.asyncio\n    async def test_insert(self, repository, db_session):\n        current_session.set(db_session)\n        dbo = get_default_place_dbo()\n        async with repository.db_session.begin():\n            r = await repository.insert(dbo)\n        assert r == dbo\n        get_response = await repository.get_by_id(PlaceDBO, dbo.id)\n        assert get_response == dbo\n\n    @pytest.mark.asyncio\n    async def test_upsert(self, repository, db_session):\n        current_session.set(db_session)\n        dbo = get_default_place_dbo()\n        async with repository.db_session.begin():\n            r = await repository.insert(dbo)\n        assert r == dbo\n        dbo.name = \"updated_name\"\n        await repository.upsert(PlaceDBO, dbo.id, dbo)\n        upsert_response = await repository.get_by_id(PlaceDBO, dbo.id)\n        assert upsert_response.name == \"updated_name\"\n        get_response = await repository.get_by_id(PlaceDBO, dbo.id)\n        assert get_response.id == dbo.id\n        assert get_response.name == \"updated_name\"\n","repo_name":"wfq2/fastapi-starter","sub_path":"tests/integration/test_repository.py","file_name":"test_repository.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14156995905","text":"import hashlib\nimport base64\nfrom bs4 import BeautifulSoup\n\n'''\nDescribes a web site\n\nWe are not interested in the entire content of the HTML page. The CSS selector allows us considering only specific\nblocks.\n'''\nclass WebSite:\n    def __init__(self, url, categories, content, text_selector, title_selector=\"h1\"):\n        self.id = base64.b64encode(bytes(url, 'utf-8')).decode()\n        self.url = url\n        self.checksum = hashlib.md5(content.encode('utf-8')).hexdigest()\n        self.categories = categories\n        self.content = content\n        self.title = self._to_text(title_selector)\n        self.text = self._to_text(text_selector)\n        self.segments = self._extract_segments(text_selector)\n\n    '''\n    Converts the relevant (selected) content of the site into plain text\n    '''\n    def _to_text(self, selector):\n        result = None\n        soup = BeautifulSoup(self.content, \"html.parser\")\n        html = soup.select_one(selector)\n\n        if html:\n            result = html.text\n\n        return result\n\n    '''\n    Extracts text segments from the content\n    '''\n    def _extract_segments(self, main_selector, seperator=\"h2\"):\n        pre_soup = BeautifulSoup(self.content, \"html.parser\")\n        html = str(pre_soup.select_one(main_selector))\n        soup = BeautifulSoup(html, \"html.parser\")\n        headings = soup.find_all(seperator)\n        segments = {}\n        for h in headings:\n            text = \"\"\n            for sibling in h.find_next_siblings():\n                if sibling.name == \"h2\":\n                    break\n                text = text + sibling.text\n            segments[h.text] = text\n        return segments","repo_name":"redis-learn/site-indexer","sub_path":"src/website.py","file_name":"website.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20588457731","text":"import odrive\nfrom odrive.enums import *\nimport time\nimport hid\nimport controlParse\nimport controllerInputs # these two are pretty messy\n\ndef init():\n    odrv0 = odrive.find_any()\n    print(str(odrv0.vbus_voltage))\n\n    if (odrv0.axis0.motor.config.pre_calibrated != True): \n        # calibrate\n        print(\"not calibrated\")\n        pass \n\n    print(\"setting defaults:\")\n\n    odrv0.axis0.motor.config.current_lim = 9.4\n    odrv0.axis0.motor.config.pole_pairs = 3\n    odrv0.axis0.motor.config.torque_constant = 0.05\n    odrv0.axis0.encoder.config.cpr = 4096\n    # odrv0.axis0.motor.config.current_lim = ?\n    odrv0.axis0.controller.config.input_filter_bandwidth = 2.0\n    odrv0.axis0.controller.config.vel_gain = 0.045\n    odrv0.axis0.controller.config.vel_limit = 40\n    odrv0.axis0.requested_state = AXIS_STATE_CLOSED_LOOP_CONTROL\n    odrv0.axis0.controller.config.control_mode = CONTROL_MODE_VELOCITY_CONTROL\n\n    return odrv0\n\n\nif __name__ == \"__main__\":\n\n    odrv0 = init()\n\n    myDeviceName = \"Mayflash WiiU Pro Game Controller Adapter\"\n    myReader = controllerInputs.ControllerReader()\n    myReader.openController(myDeviceName)\n\n    while(True):\n        output = controlParse.mapControllsTwoStick(myReader.updateInputs())\n        odrv0.axis0.controller.input_vel = output['leftMotorTargetSpeed']\n        #odrv0.axis1.controller.input_vel = output['rightMotorTargetSpeed']\n        print(output)\n        time.sleep(0.1)\n        \n\n    # note that maximum speed is set to 30 in controlParse.py\n    # 40 speed caused motor to draw more than the max set current","repo_name":"rutgers/IGVC21-22","sub_path":"electronics/odrv_manual_control.py","file_name":"odrv_manual_control.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35173349899","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 18 16:33:38 2022\n\n@author: dhyeybm\n\"\"\"\n\nimport numpy as np\ndef Pixel_adj(Im, adj, V, p):\n    n4_x = [1,-1,0,0]\n    n4_y = [0,0,1,-1]\n    n4 = []\n    for i in range(4):\n        nx = p[0]+n4_x[i]\n        ny = p[1]+n4_y[i]\n        if V.count(Im[nx-1][ny-1]) >=1 :\n            n4.append([nx,ny])\n    if adj == \"4-adjacency\":\n        return n4\n    nd_x = [1,-1,1,-1]\n    nd_y = [1,1,-1,-1]\n    nd = []\n    for i in range(4):\n        nx = p[0]+nd_x[i]\n        ny = p[1]+nd_y[i]\n        if nx>=0 and ny>=0 and nx<len(Im) and ny<len(Im[0]) and V.count(Im[nx-1][ny-1])>=1:\n            nd.append([nx,ny])\n    if adj == \"8-adjacency\":\n        return n4+nd\n    madj = n4\n    for q in nd:\n        n4_q = Pixel_adj(Im,\"4-adjacency\",V,q)\n        print(n4_q,n4)\n        if len(list(set(n4_q)&set(n4))) == 0:\n            madj.append(q)\n    return madj\n\ndef find_Path(Im,P,S,V,vis,curr,adj):\n    # print(P)\n    if P == S:\n        print(curr)\n        return\n    neighbors = Pixel_adj(Im, adj, V, P)\n    # print(neighbors)\n    for neighbor in neighbors:\n        if vis[neighbor[0]-1][neighbor[1]-1] == 0:\n            # print(\"From\",P,\"to \",neighbor)\n            vis[neighbor[0]-1][neighbor[1]-1] = 1\n            curr.append(neighbor)\n            find_Path(Im,neighbor,S,V,vis,curr,adj)\n            vis[neighbor[0]-1][neighbor[1]-1] = 0\n            curr.pop()\n    \n            \n    \n    \n    \n    \n\nIm = [[3,5, 3, 5, 2, 4, 3, 2],\n[4 ,6 ,6 ,7 ,3 ,3 ,7 ,6] ,\ne[6 ,3 ,0 ,3, 1, 3, 2, 3],\n[5 ,4 ,2 ,1 ,2 ,3 ,6 ,7],\n[2, 7, 3, 0, 0, 1, 2, 5],\n[7, 4, 1, 0, 2, 3, 3, 4],\n[4, 2, 7, 3, 5, 7, 2, 5],\n[2, 6, 5, 4, 2, 7, 5, 6]]\n\nP = [3,5]\nS = [5,5]\nV = [0,1]\n\nprint(\"4 Adjacency:\")\nvis = np.zeros((len(Im),len(Im[0])))\nvis[P[0]-1][P[1]-1] = 1\ncurr = [P]\nfind_Path(Im,P,S,V,vis,curr,\"4-adjacency\")\n\nprint(\"8 Adjacency:\")\nvis = np.zeros((len(Im),len(Im[0])))\nvis[P[0]-1][P[1]-1] = 1\ncurr = [P]\nfind_Path(Im,P,S,V,vis,curr,\"8-adjacency\")\n\n\nprint(\"m Adjacency:\")\nvis = np.zeros((len(Im),len(Im[0])))\nvis[P[0]-1][P[1]-1] = 1\ncurr = [P]\nfind_Path(Im,P,S,V,vis,curr,\"m-adjacency\")\n","repo_name":"KritikGarg1/image-lab","sub_path":"18JE0292_A2/Q2.py","file_name":"Q2.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32073660142","text":"import unittest\nimport os\nfrom mlpot.nnpotentials import SMATBpotential\nfrom mlpot.nnpotentials.utils import calculate_eam_maps\nimport numpy as np\nimport tensorflow as tf\nimport pickle\n\n\nclass BPpotentialTest(unittest.TestCase):\n\n    def test_gold_dataset(self):\n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        with open(\n                os.path.join(dir_path, \"Au_EAM_testdata.pickle\"), \"rb\") as fin:\n            try:\n                (Gs_train, types_train, E_train,\n                 Gs_test, types_test, E_test) = pickle.load(fin)\n            except UnicodeDecodeError:  # For Python3.6\n                (Gs_train, types_train, E_train,\n                 Gs_test, types_test, E_test) = pickle.load(\n                    fin, encoding='latin1')\n\n        initial_params = {}\n        initial_params[(\"A\", \"Au\", \"Au\")] = 0.2061\n        initial_params[(\"xi\", \"Au\", \"Au\")] = 1.7900\n        initial_params[(\"p\", \"Au\", \"Au\")] = 10.229\n        initial_params[(\"q\", \"Au\", \"Au\")] = 4.036\n        initial_params[(\"r0\", \"Au\", \"Au\")] = 2.88\n\n        pot = SMATBpotential([\"Au\"], initial_params=initial_params)\n\n        ([[r_Au_Au]], [[b_map_Au_Au]], [Au_map]) = calculate_eam_maps(\n            1, Gs_test, types_test)\n        test_dict = {\n            pot.ANNs[\"Au\"].inputs[\"Au\"]: r_Au_Au,\n            pot.ANNs[\"Au\"].b_maps[\"Au\"]: b_map_Au_Au,\n            pot.atom_maps[\"Au\"]: Au_map,\n            pot.target: E_test,\n            pot.rmse_weights: 1.0/np.array(list(map(len, Gs_test)))**2}\n\n        with tf.compat.v1.Session() as sess:\n            sess.run(tf.compat.v1.variables_initializer(pot.variables))\n\n            np.testing.assert_array_almost_equal(\n                sess.run(pot.E_predict, test_dict),\n                np.array([-4.94568,     -2.6547625,   -0.6748188,\n                          -0.013763459, -0.012277357, -0.16663098,\n                          -4.6700187,   -0.019066766, -0.13481946,\n                          -0.09345694,  -3.4406862,   -1.5484986,\n                          -0.06844438,  -0.8315902,   -1.349937,\n                          -0.22047868,  -0.35986638,  -6.9231143,\n                          -0.034387566, -0.037684362, -0.15533087,\n                          -0.04122702,  -1.6580167,   -4.4032536,\n                          -0.058135785, -7.503875,    -0.015384224,\n                          -0.191679,    -0.23645946,  -0.77570075,\n                          -0.95553195]), decimal=5)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"hauser-group/mlpot","sub_path":"test/test_nnpotentials/test_eam_potential.py","file_name":"test_eam_potential.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"29419222059","text":"# completa el código de la función\ndef amigos(a,b):\n    suma_a = 0\n    suma_b = 0\n    lista_a = []\n    lista_b = []\n    a=int(a)\n    b=int(b)\n    for i in range(1,a):\n        if a%i==0:\n            lista_a.append(i)\n    print(lista_a)\n    for i in range(1,b):\n        if b%i==0:\n            lista_b.append(i)\n    print(lista_b)\n    for x in lista_a:\n        suma_a+=x\n    for n in lista_b:\n        suma_b+=n\n\n    print(suma_a)\n    print(suma_b)\n\n    if suma_a==b and suma_b==a:\n        return True\n    else:\n        return False","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej2/tema2_ej2_53f88a8844c911fe6baea34007153bb3.py","file_name":"tema2_ej2_53f88a8844c911fe6baea34007153bb3.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6882974915","text":"from lxml import html\nfrom tqdm import tqdm\nfrom station import PrixStation\nfrom fetch import get_txt_departement, get_liste_departement, get_txt_station_par_departement\n\ncurrentFolder = 'departement/'\nurlWebSite = \"https://www.carburants.org/prix-carburants/\"\nfichierFinalName = \"stations.dart\"\n\n\ndef main():\n    get_txt_departement(urlWebSite)  # get all the departement in a txt file\n    # read all the lines in the file and saves them in a list\n    lines = get_liste_departement()\n\n    with open(fichierFinalName, \"w\") as file:\n        file.write(\"final List<Map<String, dynamic>> items = [\\n\")\n\n    file.close()\n\n    print(\"Start...\")\n    # iterate over the list of departement\n    for line in tqdm(lines, desc=\"Loading…\",\n                     ascii=False, ncols=75):\n        # get all the station in a departement\n        get_txt_station_par_departement(line.strip(), fichierFinalName)\n    print(\"Complete.\")\n\n    with open(fichierFinalName, \"a\") as file:\n        file.write(\"];\\n\")\n\n    file.close()\n\n    station = PrixStation()\n    station.urlWebSite = urlWebSite\n\n    # Try to get the price of a station\n    print(\"--------------------------\")\n    print(\"Test gaz station :\")\n    print(station.station(\n        \"https://www.carburants.org/prix-carburants/aisne.02/bruyeres-et-montberault.kwTwT7/total_garage-petetin.qBAzEj\"))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"EnzoPENISSON/station-data-extractor","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6797914487","text":"\n\nimport numpy as np\nimport datastock as ds\n\n\n# #############################################################################\n# #############################################################################\n#               ddata - special case: roman to int (SpectralLines)\n# #############################################################################\n\n\ndef roman2int(ss):\n    \"\"\"\n    :type s: str\n    :rtype: int\n\n    source: https://www.tutorialspoint.com/roman-to-integer-in-python\n    \"\"\"\n    roman = {\n        'I': 1,\n        'V': 5,\n        'X': 10,\n        'L': 50,\n        'C': 100,\n        'D': 500,\n        'M': 1000,\n        'IV': 4,\n        'IX': 9,\n        'XL': 40,\n        'XC': 90,\n        'CD': 400,\n        'CM': 900,\n    }\n    i = 0\n    num = 0\n    while i < len(ss):\n        if i+1 < len(ss) and ss[i:i+2] in roman:\n            num += roman[ss[i:i+2]]\n            i += 2\n        else:\n            num += roman[ss[i]]\n            i += 1\n    return num\n\n\ndef int2roman(num):\n    roman = {\n        1000: \"M\",\n        900: \"CM\",\n        500: \"D\",\n        400: \"CD\",\n        100: \"C\",\n        90: \"XC\",\n        50: \"L\",\n        40: \"XL\",\n        10: \"X\",\n        9: \"IX\",\n        5: \"V\",\n        4: \"IV\",\n        1: \"I\",\n    }\n\n    def roman_num(num):\n        for r in roman.keys():\n            x, y = divmod(num, r)\n            yield roman[r] * x\n            num -= (r * x)\n            if num <= 0:\n                break\n\n    return \"\".join([a for a in roman_num(num)])\n\n\n# #############################################################################\n# #############################################################################\n#               element - ion - charge equivalence\n# #############################################################################\n\n\ndef _check_elementioncharge(\n    ION=None, ion=None,\n    element=None, charge=None,\n    warn=None,\n):\n    \"\"\" Specific to SpectralLines \"\"\"\n\n    if warn is None:\n        warn = True\n\n    # Assess if relevant\n    lc = [\n        ION is not None,\n        ion is not None,\n        element is not None and charge is not None,\n    ]\n    if not any(lc):\n        if warn is True:\n            msg = (\n                \"\"\"\n                To determine ION, ion, element and charge, provide either:\n                - ION:  {}\n                - ion:  {}\n                - element and charge: {}, {}\n                \"\"\".format(ION, ion, element, charge)\n            )\n            warnings.warn(msg)\n        return None, None, None, None\n\n    # Get element and charge from ION if any\n    if lc[0] or lc[1]:\n        indc = 1\n        if (lc[0] and ION[1].islower()) or (lc[1] and ion[1].islower()):\n            indc = 2\n\n        # Infer element\n        elementi = ION[:indc] if lc[0] else ion[:indc]\n        if element is not None and element != elementi:\n            msg = (\n                \"\"\"\n                Inconsistent ION ({}) vs element ({})\n                \"\"\".format(element, elementi)\n            )\n            raise Exception(msg)\n\n        # Infer charge\n        if lc[0]:\n            chargei = roman2int(ION[indc:]) - 1\n        else:\n            chargei = int(ion[indc:].replace('+', ''))\n        if charge is not None and charge != chargei:\n            msg = (\n                \"\"\"\n                Inconsistent ION ({}) vs charge ({})\n                \"\"\".format(charge, chargei)\n            )\n            raise Exception(msg)\n        element = elementi\n        charge = chargei\n        if lc[0]:\n            ioni = '{}{}+'.format(element, charge)\n            if lc[1] and ioni != ion:\n                msg = (\n                    \"\"\"\n                    Inconsistent ION ({}) vs ion ({})\n                    \"\"\".format(ION, ion)\n                )\n                raise Exception(msg)\n            ion = ioni\n\n        elif lc[1]:\n            IONi = '{}{}'.format(element, int2roman(charge+1))\n            if lc[0] and IONi != ION:\n                msg = (\n                    \"\"\"\n                    Inconsistent ion ({}) vs ION ({})\n                    \"\"\".format(ion, ION)\n                )\n                raise Exception(msg)\n            ION = IONi\n\n    # ion provided -> element and charge\n    elif lc[2]:\n        ioni = '{}{}+'.format(element, charge)\n        IONi = '{}{}'.format(element, int2roman(charge+1))\n        if ion is not None and ion != ioni:\n            msg = (\n                \"\"\"\n                Inconsistent (element, charge) ({}, {}) vs ion ({})\n                \"\"\".format(element, charge, ion)\n            )\n            raise Exception(msg)\n        if ION is not None and ION != IONi:\n            msg = (\n                \"\"\"\n                Inconsistent (element, charge) ({}, {}) vs ION ({})\n                \"\"\".format(element, charge, ION)\n            )\n            raise Exception(msg)\n        ion = ioni\n        ION = IONi\n\n    return ION, ion, element, charge\n\n\ndef _check_elementioncharge_dict(dstatic):\n    \"\"\" Specific to SpectralLines \"\"\"\n\n    # Assess if relevant\n    lk = [kk for kk in ['ion', 'ION'] if kk in dstatic.keys()]\n    if len(lk) == 0:\n        return\n    kion = lk[0]\n    kION = 'ION' if kion == 'ion' else 'ion'\n    if kion == 'ION':\n        dstatic['ion'] = {}\n\n    lerr = []\n    for k0, v0 in dstatic[kion].items():\n        try:\n            if kion == 'ION':\n                ION, ion, element, charge = _check_elementioncharge(\n                    ION=k0,\n                    ion=v0.get('ion'),\n                    element=v0.get('element'),\n                    charge=v0.get('charge'),\n                )\n            else:\n                ION, ion, element, charge = _check_elementioncharge(\n                    ION=v0.get('ION'),\n                    ion=k0,\n                    element=v0.get('element'),\n                    charge=v0.get('charge'),\n                )\n\n            if ION is None:\n                continue\n            if kion == 'ION':\n                dstatic['ion'][ion] = {\n                    'ION': ION,\n                    'element': element,\n                    'charge': charge,\n                }\n            else:\n                dstatic['ion'][k0]['ION'] = ION\n                dstatic['ion'][k0]['element'] = element\n                dstatic['ion'][k0]['charge'] = charge\n\n        except Exception as err:\n            lerr.append((k0, str(err)))\n\n    if kion == 'ION':\n        del dstatic['ION']\n\n    if len(lerr) > 0:\n        lerr = ['\\t- {}: {}'.format(pp[0], pp[1]) for pp in lerr]\n        msg = (\n            \"\"\"\n            The following entries have non-conform ion / ION / element / charge\n            {}\n            \"\"\".format('\\n'.join(lerr))\n        )\n        raise Exception(msg)\n\n\n# #############################################################################\n# #############################################################################\n#               Check for computing pec / intensity\n# #############################################################################\n\n\ndef _check_compute_pec(\n    # check keys\n    key=None,\n    dlines=None,\n    ddata=None,\n    _quant_ne=None,\n    _quant_Te=None,\n    # check ne, te\n    ne=None,\n    Te=None,\n    return_params=None,\n):\n\n    # Check data conformity\n    lc = [\n        k0 for k0 in key\n        if (\n            dlines[k0].get('pec') is None\n            or [\n                ddata[pp]['quant']\n                for pp in ddata[dlines[k0]['pec']]['ref']\n            ] != [_quant_ne, _quant_Te]\n        )\n    ]\n    if len(lc) > 0:\n        msg = (\n            \"The following lines have non-conform pec data:\\n\"\n            + \"\\t- {}\\n\\n\".format(lc)\n            + \"  => pec data should be tabulated vs (ne, Te)\"\n        )\n        warnings.warn(msg)\n        key = [kk for kk in key if kk not in lc]\n\n    # Check ne, Te\n    ltype = (int, float, np.integer)\n    dnTe = {'ne': ne, 'Te': Te}\n    for k0, v0 in dnTe.items():\n        if isinstance(v0, ltype):\n            dnTe[k0] = np.r_[v0]\n        if isinstance(dnTe[k0], list) or isinstance(dnTe[k0], tuple):\n            dnTe[k0] = np.array([dnTe[k0]])\n        if not (isinstance(dnTe[k0], np.ndarray) and dnTe[k0].ndim == 1):\n            msg = (\n                \"Arg {} should be a 1d np.ndarray!\".format(k0)\n            )\n            raise Exception(msg)\n\n    # -----------\n    # return_neTe\n\n    return_params = ds._generic_check._check_var(\n        return_params, 'return_params',\n        types=bool,\n        default=False,\n    )\n\n    return key, dnTe, return_params\n\n\ndef _check_compute_intensity(\n    # check keys\n    key=None,\n    concentration=None,\n    shape=None,\n):\n\n    if np.isscalar(concentration):\n        concentration = {k0: np.full(shape, concentration) for k0 in key}\n\n    if isinstance(concentration, np.ndarray):\n        concentration = {k0: concentration for k0 in key}\n\n    c0 = (\n        isinstance(concentration, dict)\n        and all([k0 in concentration.keys() for k0 in key])\n        and all([\n            k0 in key\n            and isinstance(cc, np.ndarray)\n            and cc.shape == shape\n            and np.all((cc > 0.) & (cc <= 1.))\n            for k0, cc in concentration.items()\n        ])\n    )\n    if not c0:\n        msg = (\n            \"Arg concentration is non-conform:\\n\"\n            f\"\\t- Expected: dict of shape {shape} arrays in [0, 1]\\n\"\n            f\"\\t- Provided: {concentration}\"\n        )\n        raise Exception(msg)\n\n    return concentration\n","repo_name":"ToFuProject/tofu","sub_path":"tofu/data/_spectrallines_checks.py","file_name":"_spectrallines_checks.py","file_ext":"py","file_size_in_byte":9349,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"77"}
+{"seq_id":"6482802444","text":"# -*- coding: utf-8 -*-\nfrom helpers.views import ExpertRequiredApiView\n\nimport json\nimport os\nfrom django.http import JsonResponse\nimport urllib.parse\n\n\nfrom helpers.helpers import decode_base64_file, unique_filename_in_path\n\n\nfrom config.settings import MEDIA_ROOT, MEDIA_URL\n\n# https://github.com/django-ckeditor/django-ckeditor/blob/master/ckeditor_uploader/views.py\n\n\nclass CkEditorUpload(ExpertRequiredApiView):\n    def post(self, request, *args, **kwargs):\n        json_data = json.loads(request.body)\n\n        filename = json_data[\"filename\"]\n        picfile = decode_base64_file(json_data[\"file\"]).read()\n\n        try:\n            group_pk = request.user.group.pk\n        except AttributeError:\n            group_pk = \"anonymous\"\n\n        dir_path = os.path.join(MEDIA_ROOT, \"ckeditor_uploaded\", str(group_pk))\n        partial_path = urllib.parse.urljoin(MEDIA_URL, f\"ckeditor_uploaded/{group_pk}/\")\n\n        if not os.path.isdir(dir_path):\n            os.makedirs(dir_path)\n\n        valid_filename = unique_filename_in_path(dir_path, filename)\n\n        path = os.path.join(dir_path, valid_filename)\n\n        with open(path, \"wb\") as fpic:\n            fpic.write(picfile)\n\n        url = urllib.parse.urljoin(partial_path, valid_filename)\n\n        retdata = {\"url\": url, \"uploaded\": \"1\", \"fileName\": valid_filename}\n        return JsonResponse(retdata, safe=False)\n","repo_name":"alexandrenorman/mixeur","sub_path":"ckeditor_custom/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73825265848","text":"#!/bin/python\nimport argparse\nimport requests\nfrom html.parser import HTMLParser\n\nclass AurParser(HTMLParser):\n    def __init__(self):\n        HTMLParser.__init__(self)\n        self.name = False\n        self.desc = False\n        self.names = []\n        self.descs = []\n\n    def handle_starttag(self, tag, attr):\n        if tag != 'td' and tag != 'a':\n            return\n        if tag == 'td':\n            for name, val in attr:\n                if name == 'class' and val == 'wrap':\n                    self.desc = True\n                    return\n        if tag == 'a':\n            for name, val in attr:\n                if name == 'href' and val.find('?') == -1:\n                    self.name = True\n                    return\n\n    def handle_endtag(self, tag):\n        if tag == 'td' and self.desc:\n            self.desc = False\n            return\n        if tag == 'a' and self.name:\n            self.name = False\n            return\n\n    def handle_data(self, data):\n        if self.name:\n            self.names.append(data)\n            return\n        if self.desc:\n            self.descs.append(data)\n            return\n\nparser = argparse.ArgumentParser(description='Searches the AUR')\nparser.add_argument(\"string\", metavar='<search>', type=str, nargs='*', help=\"the search string\")\n\nargs = parser.parse_args()\nsearch = ' '.join(args.string)\nurl = 'https://aur.archlinux.org/packages/'\n\nif search != '':\n    url += '?K=' + search\n\nr = requests.get(url)\nres = r.text\nres = res[res.index('<tbody>'):res.index('</tbody>')]\np = AurParser()\np.feed(res)\n\nfor i in range(len(p.names)):\n    print('{}\\n\\t{}\\n'.format(p.names[i], p.descs[i]))\n","repo_name":"jbyte/dotfiles","sub_path":"scripts/aur-search.py","file_name":"aur-search.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74972102327","text":"\"\"\"\nCharPattern12:\nE\nE D\nE D C\nE D C B\nE D C B A\n\n\"\"\"\nn=5\nfor i in range(n):\n    p=69\n    for j in range(i+1):\n        print(chr(p),end=' ')\n        p-=1\n    print()","repo_name":"dhirajmehta777/PythonLocalFiles","sub_path":"PycharmProjects/PythonPrograms/CharacterPatternPrograms/CharPattern12.py","file_name":"CharPattern12.py","file_ext":"py","file_size_in_byte":165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"48334223253","text":"import pytest\nimport time\n\nimport numpy as np\nimport pyrobot.utils.util as prutil\nimport tf.transformations as tft\nfrom pyrobot import Robot\n\n\n@pytest.fixture(scope=\"module\")\ndef create_robot():\n    return Robot(\"locobot\", use_base=False)\n\n\n@pytest.mark.parametrize(\n    \"position\", [[0.40, 0.72, -0.47, -1.4, 0.92], [-0.67, 0, 0.23, 1, -0.70]]\n)\n@pytest.mark.parametrize(\"plan\", [\"True\", \"False\"])\ndef test_position_control(create_robot, position, plan):\n    bot = create_robot\n    bot.arm.go_home()\n    time.sleep(1)\n    bot.arm.set_joint_positions(position, plan=plan)\n    time.sleep(1)\n    joint_angles = bot.arm.get_joint_angles()\n    ag_error = np.fabs(joint_angles.flatten() - np.array(position).flatten())\n    assert np.max(ag_error) < 0.06\n\n\npositions = [np.array([0.279, 0.176, 0.217]), np.array([0.339, 0.0116, 0.255])]\norientations = [\n    np.array(\n        [\n            [0.5380200, -0.6650449, 0.5179283],\n            [0.4758410, 0.7467951, 0.4646209],\n            [-0.6957800, -0.0035238, 0.7182463],\n        ]\n    ),\n    np.array([0.245, 0.613, -0.202, 0.723]),\n]\n\n\n@pytest.mark.parametrize(\n    \"position, orientation\",\n    [(positions[0], orientations[0]), (positions[1], orientations[1])],\n)\n@pytest.mark.parametrize(\"numerical\", [\"True\", \"False\"])\ndef test_ee_pose_control(create_robot, position, orientation, numerical):\n    bot = create_robot\n    bot.arm.go_home()\n    time.sleep(1)\n    bot.arm.set_ee_pose(position=position, orientation=orientation, numerical=numerical)\n    time.sleep(1)\n    trans, rot, quat = bot.arm.pose_ee\n    pos_error = np.linalg.norm(position.flatten() - trans.flatten())\n    if orientation.size == 4:\n        tgt_quat = orientation.flatten()\n    elif orientation.size == 3:\n        tgt_quat = prutil.euler_to_quat(orientation)\n    elif orientation.size == 9:\n        tgt_quat = prutil.rot_mat_to_quat(orientation)\n    else:\n        raise TypeError(\n            \"Orientation must be in one of the following forms:\"\n            \"rotation matrix, euler angles, or quaternion\"\n        )\n    quat_diff = tft.quaternion_multiply(tft.quaternion_inverse(tgt_quat), quat)\n    rot_similarity = quat_diff[3]\n    assert rot_similarity > 0.98 and pos_error < 0.02\n\n\nposition = [\n    np.array([0.28, 0.17, 0.22]),\n    np.array([0.28, -0.17, 0.22]),\n    np.array([0.09, 0.25, 0.34]),\n]\npitch = [0.5, 0.5, np.pi / 2]\nroll = [None, 0.5, np.pi / 4]\ntgt_pos = [\n    np.array([[0.2792254], [0.1694128], [0.2174248]]),\n    np.array([[0.2793616], [-0.1692725], [0.2174295]]),\n    np.array([[0.0973994], [0.2505398], [0.3373943]]),\n]\ntgt_quat = [\n    np.array([-0.0930975, 0.2367324, 0.3539513, 0.900005]),\n    np.array([0.3134469, 0.1415047, -0.4016185, 0.8487815]),\n    np.array([-0.1477051, 0.6930908, 0.1401051, 0.6915048]),\n]\n\n\n@pytest.mark.parametrize(\n    \"position, \" \"pitch, \" \"roll, \" \"tgt_pos, \" \"tgt_quat\",\n    [\n        (position[i], pitch[i], roll[i], tgt_pos[i], tgt_quat[i])\n        for i in range(len(position))\n    ],\n)\ndef test_ee_pitch_control(create_robot, position, pitch, roll, tgt_pos, tgt_quat):\n    bot = create_robot\n    bot.arm.go_home()\n    time.sleep(1)\n    bot.arm.set_ee_pose_pitch_roll(\n        position=position, pitch=pitch, roll=roll, numerical=False, plan=False\n    )\n    time.sleep(1)\n    trans, rot, quat = bot.arm.pose_ee\n    pos_error = np.linalg.norm(position.flatten() - tgt_pos.flatten())\n    quat_diff = tft.quaternion_multiply(tft.quaternion_inverse(tgt_quat), quat)\n    rot_similarity = quat_diff[3]\n\n    assert rot_similarity > 0.98 and pos_error < 0.01\n\n\n@pytest.mark.parametrize(\"plan\", [\"True\", \"False\"])\ndef test_ee_xyz_control(create_robot, plan):\n    bot = create_robot\n    bot.arm.go_home()\n    time.sleep(1)\n    tgt_trans, tgt_rot, tgt_quat = bot.arm.pose_ee\n    displacement = np.array([0, 0, -0.15])\n    tgt_pos = tgt_trans.flatten() + displacement.flatten()\n    bot.arm.move_ee_xyz(displacement, plan=plan)\n    time.sleep(1)\n    trans, rot, quat = bot.arm.pose_ee\n    pos_error = np.linalg.norm(tgt_pos.flatten() - trans.flatten())\n    quat_diff = tft.quaternion_multiply(tft.quaternion_inverse(tgt_quat), quat)\n    rot_similarity = quat_diff[3]\n\n    rot_thresh = 0.98\n    pos_thresh = 0.02\n    assert rot_similarity > rot_thresh and pos_error < pos_thresh\n\n\ndef test_gripper_control(create_robot):\n    bot = create_robot\n    bot.arm.go_home()\n    time.sleep(1)\n    bot.gripper.open()\n    time.sleep(1)\n    g_state = bot.gripper.get_gripper_state()\n    assert g_state == 0\n\n    bot.gripper.close()\n    time.sleep(1)\n    g_state = bot.gripper.get_gripper_state()\n    assert g_state == 3\n","repo_name":"facebookresearch/pyrobot","sub_path":"tests/test_arm_controls.py","file_name":"test_arm_controls.py","file_ext":"py","file_size_in_byte":4571,"program_lang":"python","lang":"en","doc_type":"code","stars":2174,"dataset":"github-code","pt":"77"}
+{"seq_id":"9250505898","text":"# 1. Two Sum\n# Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.\n# You may assume that each input would have exactly one solution, and you may not use the same element twice.\n# You can return the answer in any order.\n\n# Link - https://leetcode.com/problems/two-sum/\n\n\ndef twoSum(nums, target):\n    map = {}\n    for i in range (len(nums)):\n        comp = target - nums[i]\n        if comp in map:\n            return [i,map[comp]]\n        map[nums[i]] = i\n        \n\nprint(twoSum([2,7,11,15],9))","repo_name":"Yashsh29/Leetcode","sub_path":"Two_Sum.py","file_name":"Two_Sum.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40969275004","text":"from bs4 import BeautifulSoup\nimport urllib.request\nimport sys\n\nclass Scraping():\n\tdef __init__(self):\n\t\tself.url = \"http://bo.unsa.edu.ar/\"\n\t\tself.listaArchivos = []\n\t\tself.listaCarpetas = []\n\n\n\tdef cargarHTML(self):\n\t\tresponse = urllib.request.urlopen(self.url)\n\t\tself.html = BeautifulSoup(response, 'html.parser')\n\n\n\n\tdef cargarCarpetas(self):\n\t\ttags = self.html('a')\n\t\tfor tag in tags:\n\t\t\tcarpeta = tag.get('href')\n\t\t\tif(carpeta != 'http://www.unsa.edu.ar/' and carpeta != 'http://validator.w3.org/check/refer' and carpeta != 'http://www.google.com/'):\n\t\t\t\turlCarpeta = self.url + carpeta\n\t\t\t\tself.listaCarpetas.append(urlCarpeta)\n\n\n\n\tdef cargaURLs(self):\n\t\tx = 0\n\t\twhile x < len(self.listaCarpetas):\n\t\t\tcarpeta = self.listaCarpetas[x]\n\t\t\ttry:\n\t\t\t\tresponse = urllib.request.urlopen(carpeta)\n\t\t\t\turls = BeautifulSoup(response,'html.parser')\n\t\t\texcept:\n\t\t\t\tprint(\"ERROR 404. No se pudo cargar la página: \"+ carpeta)\n\t\t\telse:\n\t\t\t\ttags = urls('a')\n\t\t\t\tfor tag in tags:\n\t\t\t\t\tsubdir = tag.get('href')\n\t\t\t\t\tif (\".pdf\" or \".doc\" or \".docx\" or \".txt\" or \".html\" or \".htm\" or \".xml\") in subdir:\n\t\t\t\t\t\turl = carpeta + subdir\n\t\t\t\t\t\tself.listaArchivos.append(url)\n\t\t\tx+=1\n\n\tdef getListaArchivos(self):\n\t\treturn self.listaArchivos\ndef main():\n\tscraping = Scraping()\n\tscraping.cargarHTML()\n\tscraping.cargarCarpetas()\n\tscraping.cargaURLs()\n\n\tlistaArchivos = scraping.getListaArchivos()\n\tprint(len(listaArchivos))\n\tf = open(\"links.txt\",'a')\n\tfor archivo in listaArchivos:\n\t\tf.write('\\n'+archivo)\n\n\tf.close()\n\nif __name__ == '__main__':\n\tmain()","repo_name":"sebaVargas99/buscador-resoluciones-unsa","sub_path":"buscadorRec/clsScrapForPdf.py","file_name":"clsScrapForPdf.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41294894060","text":"\n\ndef rec(x):\n    f = open(r\"C:\\Users\\Lenovo\\Desktop\\compte.txt\", \"r\")\n    d = {}\n    for ligne in f:\n        l = ligne.split(\";\")\n        if l[0].strip() == x:\n            d['idProp'] = l[1]\n            d['solde'] = l[2]\n            d['Dte'] = l[3]\n            d['Type'] = l[4]\n            return d\n        else:\n            return \"Compte n'existe pas\"\n    f.close()\n\n\n\n","repo_name":"tchakpame/projet-git1","sub_path":"partie2.py","file_name":"partie2.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6660280686","text":"import numpy as np\nimport tensorflow as tf\nimport random\n\n# tensor_a 에서 학습한 모델 적용시키기\n\nPmodel = tf.keras.models.load_model('./model_repository')\n\n\nwith open(r'./dataset/PIANOBAC.txt', 'r') as text:\n    text = text.read()\n    bag_of_words = list(set(text))\n    bag_of_words.sort()\n\n\n# utilities 둘 다 만들어놓는게 용이함\ntext_to_num = {}\nnum_to_text = {}\n\nfor i, data in enumerate(bag_of_words):\n    text_to_num[data] = i\n    num_to_text[i] = data\n\nnew_text = list()\n\nfor i in text:\n    new_text.append(text_to_num[i])\n    \nfirst_input = new_text[117:117+25]\nfirst_input = tf.one_hot(first_input, 31) # 원핫 인코딩\nfirst_input = tf.expand_dims(first_input, axis=0) # 차원 증가\n\n# print(first_input)\n\n# predict_value = Pmodel.predict(first_input)\n# predict_value = np.argmax(predict_value[0])\n# print(predict_value)\n# print(new_text[117+25])\n\n# 연속 예측 하기\nmusic = list()\n\n\nfor i in range(350):\n\n    predict_value = Pmodel.predict(first_input)\n    old_predict_value = np.argmax(predict_value[0])\n    new_predict_value = np.random.choice(bag_of_words, 1, p=predict_value[0])\n    new_predict_value = text_to_num[new_predict_value[0]]\n    random_value = random.choice([old_predict_value, new_predict_value])\n\n    # print(new_predict_value)\n\n\n    # input('break')\n\n    music.append(random_value)\n    next_input = first_input.numpy()[0][1:]\n\n    predict_value_one_hot = tf.one_hot(random_value, 31)\n\n    first_input = np.vstack([next_input, predict_value_one_hot.numpy()])\n    first_input = tf.expand_dims(first_input, axis=0)\n\n# print(music)\n\nmusic_text = list()\n\nfor i in music:\n    music_text.append(num_to_text[i])\n\nprint(''.join(music_text))","repo_name":"surplusboy/machine_learning_ex","sub_path":"RNN_training_ex/tensor_b.py","file_name":"tensor_b.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72039055608","text":"from supabase_py import create_client, Client\r\nimport os\r\n\r\n# Initialize Supabase client using environment variables\r\nSUPABASE_URL = os.environ.get('SUPABASE_URL')\r\nSUPABASE_API_KEY = os.environ.get('SUPABASE_API_KEY')\r\nsupabase: Client = create_client(SUPABASE_URL, SUPABASE_API_KEY)\r\n\r\ndef fetch_rss_structure() -> dict:\r\n    \"\"\"\r\n    Fetch the RSS structure from the Supabase table 'rss_feeds'.\r\n    \r\n    Returns:\r\n        dict: A dictionary containing countries as keys, topics as sub-keys, \r\n              and a list of source names and URLs as values.\r\n    \r\n    Raises:\r\n        Exception: If there's an error fetching data from the database.\r\n    \"\"\"\r\n    try:\r\n        result = supabase.table('rss_feeds').select().execute()\r\n        data = result.data\r\n        \r\n        # Data transformation logic\r\n        rss_structure = {}\r\n        for item in data:\r\n            country = item['country']\r\n            topic = item['topic']\r\n            source_name = item['source_name']\r\n            source_url = item['source_url']\r\n\r\n            if country not in rss_structure:\r\n                rss_structure[country] = {}\r\n            if topic not in rss_structure[country]:\r\n                rss_structure[country][topic] = []\r\n\r\n            rss_structure[country][topic].append({\r\n                'name': source_name,\r\n                'url': source_url\r\n            })\r\n        \r\n        return rss_structure\r\n        \r\n    except Exception as e:\r\n        print(f\"Error fetching RSS structure: {e}\")\r\n        raise\r\n","repo_name":"mertdemir0/newsmobile","sub_path":"tldr/database/supabase_db.py","file_name":"supabase_db.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26495767716","text":"import os\nimport platform\nimport importlib\nfrom importlib.machinery import SourceFileLoader\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\n\nclass LinearRegression():\n\n  def __init__(self, logger):\n    self.logger = logger\n\n  def cost_function(self, linear_model, y):\n    return tf.reduce_sum(tf.square((linear_model - y) / (2 * y.shape[0])))\n\n  def train(self, X_train, y_train, initial_theta, bias, learning_rate, n_epochs):\n    tf_theta = tf.Variable(initial_value = initial_theta, dtype = tf.float32)\n    tf_b = tf.Variable(initial_value = bias, dtype = tf.float32)\n\n    tf_X = tf.placeholder(dtype = tf.float32, shape = (None, X_train.shape[1]))\n    linear_model = tf.add(tf.matmul(tf_X, tf_theta), tf_b)\n    tf_y = tf.placeholder(dtype = tf.float32)\n\n    loss = self.cost_function(linear_model, tf_y)\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate)\n    train = optimizer.minimize(loss)\n\n    init = tf.global_variables_initializer()\n    sess = tf.Session()\n    sess.run(init)\n\n    for i in range(n_epochs):\n      sess.run(train, feed_dict = { tf_X : X_train, tf_y : y_train })\n\n    curr_W, curr_b, curr_loss = sess.run([tf_theta, tf_b, loss], { tf_X : X_train, tf_y : y_train })\n    print(\"W: %s b: %s loss: %s\"%(curr_W, curr_b, curr_loss))\n\n  def predict(self, X_test, y_test):\n    pass\n\ndef get_paths(current_platform, data_dir):\n\n  if current_platform == \"Windows\":\n    base_dir = os.path.join(\"D:/\", \"GoogleDrive/_cloudifier_data/09_tests\")\n  else:\n    base_dir = os.path.join(os.path.expanduser(\"~\"), \"Google Drive/_cloudifier_data/09_tests\")\n\n  utils_path = os.path.join(base_dir, \"Utils\")\n  data_path  = os.path.join(base_dir, data_dir)\n\n  return base_dir, utils_path, data_path\n\n\nif __name__ == \"__main__\":\n\n   _, utils_path, data_path = get_paths(platform.system(), \"_pricing_data\")\n\n   logger_lib = SourceFileLoader(\"logger\", os.path.join(utils_path, \"logger.py\")).load_module()\n   logger = logger_lib.Logger(show = True)\n\n   data_file =  os.path.join(data_path, \"prices.csv\")\n   df = pd.read_csv(data_file)\n\n   print(df[:10])\n   X = np.array(df.iloc[:, 1:-1].values)\n   y = np.array(df.iloc[:, -1].values)\n\n   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.10)\n   initial_theta = np.zeros((X_train.shape[1], 1))\n   bias = [0]\n\n   solver = LinearRegression(logger)\n   solver.train(X_train, y_train, initial_theta, bias, 0.01, 50)\n\n","repo_name":"Cloudifier/CLOUDIFIER_WORK","sub_path":"01_tests/05_andrei_repository/2017.09.11_Tf_pricing/pricing.py","file_name":"pricing.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21456020317","text":"k=input()\na=0\nfor i in range(0,len(k)):\n    for j in range(i+1,len(k)):\n        if k[i]==k[j]:\n            a+=1\nif a>=1:\n    print(\"yes\")\nelse:\n    print(\"no\")\n","repo_name":"mathi98/madhu","sub_path":"repeatingd.py","file_name":"repeatingd.py","file_ext":"py","file_size_in_byte":160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71305898489","text":"#!/usr/bin/python3\n\"\"\" This script takes geometry file (in any ASE-supported format)\n    and performs Phono3py analysis: builds displacements and\n    after they are calculated (outside of this script),\n    builds the 3rd order force constant matrix\n    and calculates phonon self-energy (gamma).\n\n    Naturally, Phono3py stores data in hdf5 format,\n    but here gammas and eigenfrequencies at each q-point are extracted as a postpocessing.\n\n    Adjustable parameters in the beginning of 'main' section:\n    - infile = 'geometry.unitcell.in' - initial structure to analyze\n    - supercell_matrix=np.diag([4, 4, 1])\n    - mesh = [21, 21, 1] - mesh of q-points to output gammas and frequencies\n    - temperatures=[80]  - list of temperatures to calculate gammas\n    - symprec=1e-5       - symmetry precision. Not recommended to change it\n\n    - cutoff_pair_distance = 6.0 - cutoff dist. for phonon-phonon interaction\n        Displacements involving atoms beyond cutoff will be excluded.\n        It is recommended to start with lower values\n        and increase it until convergence.\n        The displacements are enumerated in such a way\n        that adding further ones keeps previous valid,\n        therefore one need only to calculate additional points.\n\n    For a detailed description of output files see\n    https://atztogo.github.io/phono3py/output-files.html\n\"\"\"\n\nimport os\nimport sys\nimport numpy as np\nfrom ase.atoms import Atoms\nfrom ase.io import read, write\nfrom phono3py import Phono3py\n\n\ndef to_phonopy_atoms(structure, wrap=False):\n    \"\"\" (c) Florian Knoop, Hilde project (a.k.a. FHI-vibes)\n    Converts ase.atoms.Atoms to PhonopyAtoms\n    Parameters:\n    structure: ase.atoms.Atoms  - Atoms to convert\n    wrap:      bool             - If True wrap the scaled positions\n\n    Returns\n    phonopy_atoms: PhonopyAtoms - The PhonopyAtoms for the same structure\n    \"\"\"\n    from phonopy.structure.atoms import PhonopyAtoms\n\n    phonopy_atoms = PhonopyAtoms(\n        symbols=structure.get_chemical_symbols(),\n        cell=structure.get_cell(),\n        masses=structure.get_masses(),\n        positions=structure.get_positions(wrap=wrap),\n    )\n    return phonopy_atoms\n\n\ndef to_Atoms(structure, info=None, pbc=True):\n    \"\"\" (c) Florian Knoop, Hilde project (a.k.a. FHI-vibes)\n    Convert structure to ase.atoms.Atoms\n    Parameters:\n    structure: PhonopyAtoms  - The structure to convert\n    info:      dict          - Additional information to include in atoms.info\n    pbc:       bool          - True if the structure is periodic\n\n    Returns\n    atoms:     ase.atoms.Atoms  - The ASE representation of the material\n    \"\"\"\n\n    if info is None:\n        info = {}\n    if structure is None:\n        return None\n\n    atoms_dict = {\n        \"symbols\": structure.get_chemical_symbols(),\n        \"cell\": structure.get_cell(),\n        \"masses\": structure.get_masses(),\n        \"positions\": structure.get_positions(),\n        \"pbc\": pbc,\n        \"info\": info,\n    }\n\n    atoms = Atoms(**atoms_dict)\n    return atoms\n\n\ndef create_supercells_with_displacements(phono3py, cutoff_pair_distance=None):\n    phono3py.generate_displacements(distance=0.03,\n                                    cutoff_pair_distance=cutoff_pair_distance\n                                    )\n    scells_with_disps = phono3py.get_supercells_with_displacements()\n    print(\"%i displacements exist.\" % len(scells_with_disps))\n\n    excluded = 0\n    for i, scell in enumerate(scells_with_disps):\n        if scell is None:\n            # print(\"phono3py-displacement-%05d is excluded.\" % (i+1))\n            excluded += 1\n            continue\n        # write(\"displacements_all.xyz\", to_Atoms(scell), append=True)\n        if not os.path.isdir(\"phono3py-displacement-%05d\" % (i+1)):\n            os.mkdir(\"phono3py-displacement-%05d\" % (i+1))\n        # I don't want to rewrite all geometries each time I run the script\n        if os.path.exists(\"phono3py-displacement-%05d/geometry.in\" % (i+1)):\n            if to_Atoms(scell) == read(\"phono3py-displacement-%05d/geometry.in\"\n                                       % (i+1)):\n                print(\"phono3py-displacement-%05d/geometry.in \"\n                      \"exists and is valid.\"\n                      % (i+1))\n                continue\n        write(\"phono3py-displacement-%05d/geometry.in\"\n              % (i+1),\n              to_Atoms(scell))\n\n    print(\"%i displacements are included.\"\n          % (len(scells_with_disps) - excluded))\n    disp_dataset = phono3py.get_displacement_dataset()\n    print(\"Duplucates:\")\n#    print(disp_dataset['duplicates'])\n    print(\"(not printed now)\")\n\n    # Print displacements to screen\n    print(\"Set of displacements:\")\n    print(\"(not printed now)\")\n    count = 0\n    for i, disp1 in enumerate(disp_dataset['first_atoms']):\n        # print(\"%4d: %4d                %s\" % (\n        #     count + 1,\n        #     disp1['number'] + 1,\n        #     np.around(disp1['displacement'], decimals=3)))\n        count += 1\n    distances = []\n    for i, disp1 in enumerate(disp_dataset['first_atoms']):\n        for j, disp2 in enumerate(disp1['second_atoms']):\n            # print(\"%4d: %4d-%4d (%6.3f)  %s %s \\tincluded: %s\" % (\n            #     count + 1,\n            #     disp1['number'] + 1,\n            #     disp2['number'] + 1,\n            #     disp2['pair_distance'],\n            #     np.around(disp1['displacement'], decimals=3),\n            #     np.around(disp2['displacement'], decimals=3),\n            #     disp2['included']))\n            distances.append(disp2['pair_distance'])\n            count += 1\n    # Find unique pair distances\n    distances = np.array(distances)\n    unique_thres = 1e-5  # Threshold of identity for finding unique distances\n    distances_int = (distances / unique_thres).astype(int)\n    unique_distances = np.unique(distances_int) * unique_thres\n    print(\"Unique pair distances\")\n    print(unique_distances)\n\n\n# ==================== HERE THE ALGORITHM STARTS ====================\nif __name__ == '__main__':\n    infile = 'geometry.unitcell.in'\n    supercell_matrix = np.diag([4, 4, 1])\n    mesh = [21, 21, 1]\n    temperatures = [80]\n    symprec = 1e-5\n    cutoff_pair_distance = 6.0\n    print(\"cutoff_pair_distance: %.2f\" % cutoff_pair_distance)\n\n    atoms = read(infile, format='aims')\n    print(\"Cell:\")\n    print(atoms.get_cell()[:])\n    cell = to_phonopy_atoms(atoms, wrap=False)\n    phono3py = Phono3py(unitcell=cell,\n                        supercell_matrix=supercell_matrix,\n                        mesh=mesh,\n                        symprec=symprec,\n                        log_level=2)  # log_level=0 make phono3py quiet\n\n    print(\"Cell symmetry by international table:  %s\"\n          % phono3py._symmetry._international_table)\n    print(\"Supercell symmetry by international table:  %s\"\n          % phono3py._phonon_supercell_symmetry._international_table)\n    print(\"Symmetry precision %g\" % symprec)\n    print(\"Primitive cell:\")\n    print(phono3py._primitive)\n\n#    sys.exit(0)\n\n    create_supercells_with_displacements(\n        phono3py,\n        cutoff_pair_distance=cutoff_pair_distance\n        )\n\n    # ============== Postprocessing after forces are calculated ==============\n#    print(\"phono3py._supercell.get_atomic_numbers():\",\n#          phono3py._supercell.get_atomic_numbers())\n    zero_force = np.zeros([len(phono3py._supercell.get_atomic_numbers()), 3])\n\n    # collect the forces and put zeros where no supercell was created\n    force_sets = []\n    disp_scells = phono3py.get_supercells_with_displacements()\n#    print(phono3py._displacement_dataset)\n    for nn, scell in enumerate(disp_scells):\n        if not scell:\n            force_sets.append(zero_force)\n        else:\n            try:\n                with open(\"phono3py-displacement-%05d/aims.out\"\n                          % (nn+1), 'r') as f:\n                    lines = f.read().splitlines()\n                    if \"nice day\" not in lines[-2]:\n                        print(\"ERROR: phono3py-displacement-%05d/aims.out \"\n                              \"doesn't have 'nice day'. Leaving now.\"\n                              % (nn+1))\n                        sys.exit(-1)\n                atoms = read(\"phono3py-displacement-%05d/aims.out\" % (nn+1))\n            except:\n                print(\"Cannot read 'phono3py-displacement-%05d/aims.out'\\n\"\n                      \"Make sure that all displacements are calculated.\"\n                      % (nn+1))\n                sys.exit(-1)\n            force_sets.append(atoms.get_forces())\n\n    phono3py.produce_fc3(force_sets)\n    # How grid_points are treated:\n    # ~/.local/lib/python3.5/site-packages/phono3py/phonon3/conductivity.py\n    phono3py.run_thermal_conductivity(temperatures=temperatures,\n                                      boundary_mfp=1e6,  # in micrometers\n                                      # gamma_unit_conversion=None,\n                                      # gv_delta_q=None,  # for group velocity\n                                      write_gamma=True,\n                                      write_kappa=True,\n                                      write_gamma_detail=True,\n                                      write_collision=True,\n                                      # write_pp=True,\n                                      )\n\n# --------------- ~/soft/phono3py/example/Si-PBEsol/Si.py --------------\n#             Has some useful pieces, also it's a reference.\n# ----------------------------------------------------------------------\n","repo_name":"sabia-group/miscellaneous","sub_path":"script-phono3py.py","file_name":"script-phono3py.py","file_ext":"py","file_size_in_byte":9475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"42573800758","text":"__author__      = \"Noreddine Kessa\"\r\n__copyright__   = \"!\"\r\n__license__ = \"MIT License\"\r\n\r\n\r\n\r\nclass Utilities:\r\n\r\n    def __init__(self, name=\"\"):\r\n        self.name = name\r\n        self.toolName = \"CanPacker\"\r\n        self.version = \"V0.08\"\r\n\r\n \r\n    \r\n    def indent(self,st,  numSpaces):\r\n        spaces = \"\"\r\n        for i in range(numSpaces):\r\n            spaces += \" \"\r\n        #indent the first line\r\n        ns = spaces + st\r\n        #indent all other lines\r\n        ns = ns.replace('\\n', '\\n' + spaces)\r\n\r\n        #remove indentation after the last line\r\n        ns+='\\n'\r\n        return ns \r\n    \r\n    def removeWhiteSpace(self, s):\r\n        s = s.replace(' ', '')\r\n        s = s.replace('\\t', '')\r\n        return s\r\n    \r\n\r\n    def getDisclamer(self, model):\r\n        from datetime import date        \r\n        s = f\"\\\r\n/*\\n\\\r\n*this file is auto generated by  {self.toolName} version {self.version }.\\n\\\r\n*@file {model.sourceFile}\\n\\\r\n*@date {date.today()}\\n\\\r\n*@author n.kessa\\n\\\r\n*@brief frame packer/unpacker {model.sourceFile}\\n\\\r\n*/\\n\\\r\n\\n\\\r\n\\n\\\r\n\\n\"\r\n        return s \r\n    \r\n\r\n    def writeFile(self, fileName, content):\r\n        with open(fileName, 'w') as f:\r\n            f.write(content)\r\n\r\n\r\n    def writeFileWithIndent(self, fileName, content, numSpaces):\r\n        with open(fileName, 'w') as f:\r\n            f.write(self.indent(content, numSpaces))\r\n\r\n\r\n","repo_name":"knor12/CanPacker","sub_path":"CanPacker/code/Utilities.py","file_name":"Utilities.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37397307327","text":"import requests\nimport os.path\nimport sys\nimport getopt\n\n\n# update at 2019-08-07\n# http://nexus.paypal.com/nexus/service/local/*  does not work now as per artifactory team.\n# Please use http://nexus.paypal.com/nexus/content/repositories/releases/com/paypal/compliance/compliance-common-constant/3.0.102/compliance-common-constant-3.0.102.jar\n\ndef get_rbo_jar(version, file_type='jar', folder=None):\n    # url_tpl = 'https://engineering.paypalcorp.com/nexus/service/local/artifact/maven/redirect?r=releases&g=com.paypal.risk.idi&a=rbo-api&v={}&e=jar'\n    # url_tpl = 'https://nexus.paypal.com/artifactory/releases/com/paypal/risk/idi/rbo-api/{version}/'\n    url_tpl = 'http://paypalcentral.es.paypalcorp.com/artifactory/releases/com/paypal/risk/idi/rbo-api/{version}/'\n    url = url_tpl.format(version=version)\n\n    if file_type == 'source':\n        file_name = 'rbo-api-{}-sources.jar'.format(version)\n    else:\n        file_name = 'rbo-api-{}.jar'.format(version)\n    url += file_name\n\n    if folder is not None:\n        file_name = os.path.join(folder, file_name)\n\n    ret = requests.get(url)\n    if ret.status_code != 200:\n        print('Error calling {}'.format(url))\n        return\n    with open(file_name, 'wb') as jar_file:\n        jar_file.write(ret.content)\n        print('Get {} successfully'.format(file_name))\n\n\ndef usage():\n    print('Usage:%s -v {version} [-s] [-f {folder}]' % sys.argv[0])\n\n\nif __name__ == '__main__':\n    if len(sys.argv) == 1:\n        usage()\n        sys.exit(1)\n    else:\n        try:\n            opts, args = getopt.getopt(sys.argv[1:], '[v:sf:]', [''])\n            print('{} {}'.format(opts, args))\n\n            version = None\n            jar_type = None\n            folder = None\n            for opt, arg in opts:\n                if opt == '-v':\n                    version = arg\n                elif opt == '-s':\n                    jar_type = 'source'\n                elif opt == '-f':\n                    folder = arg\n                else:\n                    print('wrong opt')\n\n            if version is not None:\n                get_rbo_jar(version, 'jar', folder)\n                if jar_type is not None:\n                    get_rbo_jar(version, jar_type, folder)\n\n            else:\n                usage()\n                sys.exit(1)\n\n        except getopt.GetoptError:\n            print('getopt error!')\n            usage()\n            sys.exit(1)\n","repo_name":"zhzhzh/python-misc","sub_path":"Modules/Scripts/get_rbo_api.py","file_name":"get_rbo_api.py","file_ext":"py","file_size_in_byte":2395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31842126575","text":"import platform\nimport textwrap\nimport unittest\n\nimport pytest\n\nfrom conans.test.utils.tools import TestClient\n\n\n@pytest.mark.xfail(reason=\"Move this test to CMakeToolchain\")\nclass LibcxxSettingTest(unittest.TestCase):\n\n    @pytest.mark.skipif(platform.system() == \"Windows\", reason=\"Not in Windows\")\n    @pytest.mark.tool(\"cmake\")\n    def test_declared_stdlib_and_passed(self):\n        file_content = textwrap.dedent('''\n            from conan import ConanFile, CMake\n\n            class ConanFileToolsTest(ConanFile):\n                settings = \"os\", \"compiler\", \"arch\", \"build_type\"\n                generators = [\"cmake\"]\n\n                def build(self):\n                    cmake = CMake(self)\n                    self.output.warning(cmake.command_line)\n                    self.run('cmake . %s' % cmake.command_line)\n                    self.run(\"cmake --build . %s\" %  cmake.build_config)\n                ''')\n\n        cmakelists = textwrap.dedent('''PROJECT(conanzlib)\n            set(CONAN_DISABLE_CHECK_COMPILER TRUE)\n            cmake_minimum_required(VERSION 2.8)\n            include(conanbuildinfo.cmake)\n            conan_basic_setup()\n            message(\"CXX FLAGS=> ${CMAKE_CXX_FLAGS}\")\n            get_directory_property( DirDefs DIRECTORY ${CMAKE_SOURCE_DIR} COMPILE_DEFINITIONS)\n            foreach( d ${DirDefs} )\n                message( STATUS \"Found Define: \" ${d} )\n            endforeach()\n            ''')\n        client = TestClient()\n        client.save({\"conanfile.py\": file_content,\n                     \"CMakeLists.txt\": cmakelists})\n        client.run(\"export . --name=pkg --version=0.1 --user=user --channel=testing\")\n\n        if platform.system() == \"SunOS\":\n            client.run('build . -s compiler=sun-cc -s compiler.libcxx=libCstd')\n            self.assertIn(\"-library=Cstd\", client.out)\n\n            client.run('build -s compiler=sun-cc -s compiler.libcxx=libstdcxx')\n            self.assertIn(\"-library=stdcxx4\", client.out)\n\n            client.run('build . -s compiler=sun-cc -s compiler.libcxx=libstlport')\n            self.assertIn(\"-library=stlport4\", client.out)\n        else:\n            client.run('build . -s compiler=clang -s compiler.version=3.3 '\n                       '-s compiler.libcxx=libstdc++ ')\n            self.assertIn(\"-stdlib=libstdc++\", client.out)\n            self.assertIn(\"Found Define: _GLIBCXX_USE_CXX11_ABI=0\", client.out)\n\n            client.run('build . -s compiler=clang -s compiler.version=3.3 '\n                       '-s compiler.libcxx=libstdc++11')\n            self.assertIn(\"-stdlib=libstdc++\", client.out)\n            self.assertIn(\"Found Define: _GLIBCXX_USE_CXX11_ABI=1\", client.out)\n\n            client.run('build . -s compiler=clang -s compiler.version=3.3 '\n                       '-s compiler.libcxx=libc++')\n            self.assertIn(\"-stdlib=libc++\", client.out)\n            self.assertNotIn(\"Found Define: _GLIBCXX_USE_CXX11\", client.out)\n","repo_name":"conan-io/conan","sub_path":"conans/test/functional/settings/libcxx_setting_test.py","file_name":"libcxx_setting_test.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":7343,"dataset":"github-code","pt":"77"}
+{"seq_id":"5385182977","text":"\"\"\"Problem: Write a program that prompts for the names of a source file to read and a target file to write,\r\nand copy the content of the source file to the target file, but with all empty lines removed, then output the number\r\nof empty lines removed.\r\nOutput:\r\nSource file name: string_doc.txt\r\nTarget file name: string_doc_nonempty.txt\r\nLines removed: 16\r\n\"\"\"\r\nsrc = open(input(\"Source file name: \"), 'r')\r\ntgt = open(input(\"Target file name: \"), 'w')\r\ncount = 0\r\nfor line in src:\r\n    if line.strip():\r\n        tgt.write(line)\r\n    else:\r\n        count += 1\r\n\r\n\r\nsrc.close()\r\ntgt.close()\r\nprint(\"lines removed:\", count)\r\n","repo_name":"vinaya07/Python","sub_path":"File_Pgms/removeEmptyLines.py","file_name":"removeEmptyLines.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36195297087","text":"\"\"\"Track and analyze grocery spending at an item level.\n\nThis app allows a user to input a grocery item.\n\n\"\"\"\n\n# app.py\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output, State\nimport plotly.express as px\nimport pandas as pd\nimport dash_table\nfrom dash_table import FormatTemplate\nfrom datetime import date\n\ndf = pd.read_csv('data/items.csv') # read items.csv file into df\ndf_category = pd.read_csv('data/category.csv') # read category.csv file into df\npd.options.display.float_format = '{:.2f}'.format # set pandas format to 2 decimals\n\ndf['total'] = df['price'] * df['quantity'] # add 'total' column to df\n\ndf['month_year'] = pd.to_datetime(df['date']).dt.strftime('%B %Y') # add 'month_year' column to df and convert to 'month year' str format\ndf = df.sort_values(by='date').reset_index(drop=True) # sort df by date and reset and drop index\n\ndf_table = df[['name', 'price', 'quantity', 'date']] # create df to display table in layout\n\ndf_date = df.sort_values(by='date', ascending=False) # sort df by date in descending order and set to variable\ndf_date = df_date.head(1) # select top row of df\ndf_date = df_date.month_year.item() # select value from 'month_year' column to use as default in date dropdown\n\n# Ref: https://dash.plotly.com/layout\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\napp.config.suppress_callback_exceptions = True\n\n# Ref: https://dash-bootstrap-components.opensource.faculty.ai/docs/components/form/\ndef InputItem():\n    \"\"\"Create a form with inputs to add a new item.\n    \n    Args:\n        none\n    \n    Returns:\n        item name: input text field to enter item name\n        category: dropdown to select category\n        price: input numeric field to enter price\n        quantity: slider to select quantity\n        date: date picker to select date of purchase\n        output: text to show exceptions\n    \"\"\"\n    input_addItem = dbc.Form(\n        [\n            dbc.FormGroup(\n                [\n                    dbc.Label('Item Name'),\n                    dcc.Input(\n                        id='name',\n                        placeholder='Enter grocery item',\n                        style={'width': '100%'}\n                    ),\n                ]\n            ),\n            dbc.FormGroup(\n                [\n                    html.Label('Category'),\n                    dcc.Dropdown(\n                        id='category',\n                        options=[\n                            {'label': i, 'value': i} for i in sorted(df_category.Category)\n                        ],\n                    )\n                ]\n            ),\n            dbc.FormGroup(\n                [\n                    html.Label('Price'),\n                    dcc.Input(\n                        id='price',\n                        type='number',\n                        placeholder='Enter price of item',\n                        style={'width': '100%'}\n                    )\n                ]\n            ),\n            dbc.FormGroup(\n                [\n                    html.Label('Quantity'),\n                    dcc.Slider(\n                        id='quantity',\n                        min=0,\n                        max=10,\n                        step=1,\n                        marks={\n                        i: '{}'.format(i)\n                        if i == 1\n                        else str(i)\n                        for i in range(1,11)\n                        },\n                        value=1,\n                    ),\n                    html.Br(),\n                    html.Label('Date of Purchase'),\n                    html.Br(),\n                    dcc.DatePickerSingle(\n                        id='date',\n                        month_format='MMM Do, YY',\n                        date=date.today()\n                    )\n                ]\n            ),\n                    html.Div(id='output-add-item',\n                    style={'color': 'red'})\n                ]\n            )\n    return input_addItem\n\nbutton_item = html.Div([\ndbc.Row(\n    [\n        dbc.Col(\n            dbc.Button(\n                \"New Item\", id='button-new-item', color=\"primary\", className=\"ml-2\", n_clicks=0, block=True\n            ),\n            width=\"auto\",\n            \n        ), \n        dbc.Modal(\n        [\n        dbc.ModalHeader('Add New Item'),\n        dbc.ModalBody(InputItem()),\n        dbc.ModalFooter(\n            [\n            dbc.Button('Submit', id='submit-new-item', className='ml-auto', n_clicks=0, color='primary'),\n            dbc.Button('Cancel', id='cancel', className='ml-auto', n_clicks=0, color='primary')\n            ]\n        )\n        ],\n        id='modal',\n        is_open=False,\n    ) \n    ],\n    no_gutters=True,\n    className=\"ml-auto flex-nowrap mt-3 mt-md-0\",\n    align=\"center\"\n),\n])\n\n# Ref: https://dash.plotly.com/dash-core-components/graph\ndashboard = html.Div(\n        [\n            html.Br(),\n            html.Br(),\n            html.H5('Spending Dashboard', style={'textAlign': 'left'}),\n            html.Hr(),\n            html.Div([\n                dash_table.DataTable(\n                        id='table-item',\n                        data=df.to_dict('records'),\n                        columns=[\n                            {\n                            'name': i, 'id': i\n                            }\n                            for i in (df.columns)\n                            ],\n                        ) \n            ],\n            style={'display': 'none'},\n            ),\n            html.P('Select a date:'),\n            html.Div([\n                dcc.Dropdown(\n                    id='dash-monthyear',\n                    options=[\n                        {'label': i, 'value': i} for i in df.month_year.unique()\n                    ],\n                    value=df_date,\n                    clearable=False\n                )], \n                style={\n                    'width': '20%',\n                    'display': 'inline-block'\n                },\n            ),\n            dbc.Row(\n                dcc.Graph(id='graph-spending-all')\n                ),\n            html.Hr(),\n            html.P('Select a category:'),\n            html.Div([\n                dcc.Dropdown(id='dash-category',\n                clearable=False\n                )\n            ],\n                style={\n                    'width': '20%',\n                    'display': 'inline-block'\n                },\n            ),\n            html.Br(),\n            dbc.Row([\n                dbc.Col(\n                    dcc.Graph(id='graph-item')\n                ),\n                dbc.Col([\n                        dash_table.DataTable(\n                        id='table-item-display',\n                        data=df.to_dict('records'),\n                        columns=[\n                            {'name': 'Name', 'id': 'name'},\n                            {'name': 'Price', 'id': 'price', 'type': 'numeric', 'format': FormatTemplate.money(2)},\n                            {'name': 'Quantity', 'id': 'quantity'},\n                            {'name': 'Date', 'id': 'date'},\n                            ],\n                        page_action='native',\n                        page_current=0,\n                        page_size=10,\n                        sort_action='native',\n                        sort_mode='single',\n                        sort_by=[{'column_id': 'date', 'direction': 'desc'}],\n                        style_cell={'textAlign': 'left', 'font-family': 'sans-serif'},\n                        selected_columns=[],\n                        selected_rows=[],\n                        style_as_list_view=True,\n                        )\n                ])\n            ]),\n            dbc.Row(\n                    dcc.Graph(id='graph-trend')\n                ),\n        ],\n        style={\n            'margin-left': '15%',\n            'margin-right': '5%',\n            'padding': '20px 10px'\n        }\n)\n\n# Ref: https://dash-bootstrap-components.opensource.faculty.ai/docs/components/navbar/#\nnavbar = dbc.Navbar(\n    [\n        html.A(\n            dbc.Row(\n                [\n                    dbc.Col(dbc.NavbarBrand(\"Grocery Spending Tracker\", className=\"ml-2\")),\n                ],\n                align=\"left\",\n                no_gutters=True,\n            ),\n        ),\n    ],\n    color=\"dark\",\n    dark=True,\n    fixed='top'\n)\n\n# Ref: https://dash-bootstrap-components.opensource.faculty.ai/examples/simple-sidebar/\nsidebar = html.Div(\n    [\n    html.Br(),\n    html.Br(),\n    button_item,\n    ],\n    style={\n        'position': 'fixed',\n        'top': 0,\n        'left': 0,\n        'bottom': 0,\n        'width': '10%',\n        'padding': '20px 10px',\n        'background-color': '#f8f9fa'\n    }\n)\n\n@app.callback(\n    Output('modal', 'is_open'),\n    Output('output-add-item', 'children'),\n    [Input('button-new-item', 'n_clicks'), \n    Input('cancel', 'n_clicks'),\n    Input('submit-new-item', 'n_clicks')],\n    [State('modal', 'is_open'),\n    State('name', 'value'),\n    State('category', 'value'),\n    State('price', 'value')\n    ]\n)\ndef toggle_modal(n1, n2, n3, is_open, name, category, price):\n    \"\"\"Callback to toggle modal.\n    \n    Args:\n        n1: number of times new item button is clicked\n        n2: number of times cancel button is clicked\n        n3: number of times submit button is clicked\n        is_open: passes open state of modal\n        name: passes state of item name value\n        category: passes state of category value\n        price: passes state of price value\n    \n    Returns:\n        enables modal to be toggled between open and closed when the buttons are clicked,\n        if submit button is clicked and name, category or price is empty (quantity & date have default values):\n            modal does not close and string displays with missing input fields \n    \"\"\"\n    ctx = dash.callback_context\n    input_id = ctx.triggered[0]['prop_id'].split('.')[0]\n\n    if input_id == 'button-new-item':\n        return not is_open, None\n    elif input_id == 'cancel':\n        return not is_open, None\n    elif input_id == 'submit-new-item':\n        if name == None:\n            return dash.no_update, 'Please enter an item name.'\n        if category == None:\n            return dash.no_update, 'Please select a category.'\n        if price == None:\n            return dash.no_update, 'Please enter a price.'\n        return not is_open, None\n    return is_open, None\n\n@app.callback(\n    [Output('name', 'value'),\n    Output('category', 'value'),\n    Output('price', 'value'),\n    Output('quantity', 'value'),\n    Output('date', 'date')],\n    [Input('modal', 'is_open')]\n)\ndef clear_input(is_open):\n    \"\"\"Callback to clear input values when modal is opened.\n        \n        Args:\n            is_open: open state of modal\n        \n        Returns:\n            None for name, category, and price inputs and resets quantity slider to 1 and date to today's date\n    \"\"\"\n    return (None,None,None,1,date.today())\n\n# Ref: https://dash.plotly.com/basic-callbacks\n@app.callback(\n    Output('dash-category', 'options'),\n    [Input('table-item', 'data'),\n    Input('dash-monthyear', 'value')]\n)\ndef set_cat_option(data, month_year):\n    \"\"\"Callback to set category dropdown options based on the month selected.\n        \n        Args:\n            data: dataframe\n            month_year: selected date from dropdown\n        \n        Returns:\n            list of categories into category dropdown from dataframe for selected date\n    \"\"\"\n    dff = pd.DataFrame.from_dict(data)\n    dff = dff.query('month_year == @month_year')\n    return [{'label': i, 'value': i} for i in sorted(dff.category.unique())]\n\n@app.callback(\n    Output('dash-category', 'value'),\n    [Input('dash-category', 'options')]\n)\ndef set_cat_default(available_options):\n    \"\"\"Callback to set category dropdown default value.\n        \n        Args:\n            available_options: list of categories from dropdown\n        \n        Returns:\n            first value from category dropdown to set as default dropdown value\n    \"\"\"\n    return available_options[0]['value']\n\n\n# Ref: https://plotly.com/python/pie-charts/\n@app.callback(\n    Output('graph-spending-all', 'figure'),\n    [Input('table-item', 'data'),\n    Input('dash-monthyear', 'value')]\n)\ndef generate_graph_all_cat(data, month_year):\n    \"\"\"Callback to generate graph to show spending in all categories for the selected month.\n        \n        Args:\n            data: dataframe\n            month_year: selected date from dropdown\n        \n        Returns:\n            pie chart dispalying amounts spent per category and total amount spent for selected month\n    \"\"\"\n    dff = pd.DataFrame.from_dict(data)\n    dff = dff.query('month_year == @month_year')\n    dff_total = dff['total'].sum()\n    total_format = '{:.2f}'.format(dff_total)\n\n    fig = px.pie(dff, \n        values='total', \n        names='category', \n        title= 'Spending for All Categories in {}'.format(month_year),\n        hole= .5)\n    fig.update_traces(\n        hoverinfo='label+percent', \n        texttemplate='%{value:$.2f}',\n        textposition='inside'\n    )\n    fig.update_layout(\n        annotations= [\n            dict(text= 'Total Amount <br> ${}'.format(total_format), x=0.5, y=0.5, font_size=15, showarrow=False),\n        ],\n        legend_title='<b> Category </b>'\n    )\n    return fig  \n\n# Ref: https://plotly.com/python/pie-charts/\n@app.callback(\n    Output('graph-item', 'figure'),\n    [Input('table-item', 'data'),\n    Input('dash-monthyear', 'value'),\n    Input('dash-category', 'value')]\n)\ndef update_graph_item(data, month_year, category):\n    \"\"\"Callback to generate graph to show amounts spent per item for the selected month and category.\n        \n        Args:\n            data: dataframe\n            month_year: selected date from dropdown\n            category: selected category from dropdown\n        \n        Returns:\n            pie chart dispalying amounts spent per item and total amount spent for selected month and category \n    \"\"\"\n    dff = pd.DataFrame.from_dict(data)\n    dff = dff.query('month_year == @month_year and category ==@category')\n    dff_total = dff['total'].sum()\n    total_format = '{:.2f}'.format(dff_total)\n\n    fig = px.pie(dff, \n        values='total', \n        names='name', \n        title= 'Spending for {} in {}'.format(category, month_year),\n        hole= .5,\n    )\n    fig.update_traces(\n        hoverinfo='label+percent', \n        texttemplate='%{value:$}',\n        textinfo='value'\n    )\n    fig.update_layout(\n        annotations= [\n            dict(text= 'Total Amount <br> ${}'.format(total_format), x=0.5, y=0.5, font_size=15, showarrow=False),\n            ],\n            legend_title='<b> Item </b>'\n    )\n    return fig\n\n# Ref: https://plotly.com/python/pie-charts/\n@app.callback(\n    Output('graph-trend', 'figure'),\n    [Input('table-item', 'data'),\n    Input('dash-category', 'value')]\n)\ndef update_graph_trend(data, category):\n    \"\"\"Callback to generate graph to show amounts spent in all months for the selected category.\n        \n        Args:\n            data: dataframe\n            category: selected category from dropdown\n        \n        Returns:\n            bar chart dispalying amounts spent per month for selected category \n    \"\"\"\n    dff = pd.DataFrame.from_dict(data)\n\n    fig = px.bar(dff.query('category == @category'), x='month_year', y='price', color='category', barmode='group', \n        title= 'Spending History for {}'.format(category),\n        labels={\n            'category': 'Category', 'price': 'Total Amount', 'month_year': 'Month of Purchase'\n        }\n    )\n    fig.update_traces(\n        texttemplate='%{value:$}',\n        textposition='outside'\n    )\n    return fig\n\n# Ref: https://dash.plotly.com/advanced-callbacks\n@app.callback(\n    Output('table-item', 'data'),\n    [Input('submit-new-item', 'n_clicks')],\n    [State('name', 'value'),\n    State('category', 'value'),\n    State('price', 'value'),\n    State('quantity', 'value'),\n    State('date', 'date'),\n    ]\n)\ndef update_table(n, name, category, price, quantity, date):\n    \"\"\"Callback to add new item to dataframe and write to csv file.\n        \n        Args:\n            n: number of times submit button is clicked\n            name: passes state of item name value\n            category: passes state of category value\n            price: passes state of price value\n            quantity: passes state of quantity value\n            date: passes state of date value\n        \n        Returns:\n            dataframe with appended new row to hidden datatable in dashboard layout\n\n        Raises:\n            no update to dataframe if name, category, price, quantity, or date is empty \n    \"\"\"\n    df = pd.read_csv(\"data/items.csv\")\n    ctx = dash.callback_context\n    input_id = ctx.triggered[0]['prop_id'].split('.')[0]\n\n    if input_id == 'submit-new-item':\n        if None in [name, category, price]:\n            return dash.no_update\n        else:\n            new_row = {'name': name, 'category': category, 'price': price, 'quantity': quantity, 'date': date}\n            df = df.append(new_row, ignore_index=True)\n            df.to_csv(\"data/items.csv\", index=False)\n            df['total'] = df['price'] * df['quantity']\n            df['month_year'] = pd.to_datetime(df['date']).dt.strftime('%B %Y')\n            df = df.sort_values(by='date').reset_index(drop=True)\n            return df.to_dict('records')\n    else:\n        df['total'] = df['price'] * df['quantity']\n        df['month_year'] = pd.to_datetime(df['date']).dt.strftime('%B %Y')\n        df = df.sort_values(by='date').reset_index(drop=True)\n        return df.to_dict('records')\n\n@app.callback(\n    Output('table-item-display', 'data'),\n    [Input('table-item', 'data'),\n    Input('dash-monthyear', 'value'),\n    Input('dash-category', 'value')]\n)\ndef update_table_display(data, month_year, category):\n    \"\"\"Callback to update datatable based on date and category dropdown selection.\n        \n        Args:\n            data: dataframe\n            month_year: selected date from dropdown\n            category: selected category from dropdown\n        \n        Returns:\n            datatable dispalying items for selected month and category \n    \"\"\"\n    dff = pd.DataFrame.from_dict(data)\n    df_table = pd.DataFrame(dff.query('month_year == @month_year and category == @category'))\n    return df_table.to_dict('records')\n        \napp.layout = html.Div([navbar, sidebar, dashboard])\n\nif __name__ == '__main__':\n    app.run_server(debug=True)","repo_name":"skristimorris/grocery-spending_skmorris","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":18557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29019404622","text":"import collections\nimport heapq\nimport functools\nimport itertools\nfrom pkgutil import read_code\nimport re\nimport sys\nimport math\nimport bisect\nfrom typing import *\n\nfrom collections import deque\n\ns = \"cdcd\"\ndef solution(s):\n    s = deque(s)\n\n    while 1:\n        length = len(s)\n        if length == 0:\n            return 1\n        elif length == 1:\n            return 0\n        elif length == 2:\n            if s.pop() == s.pop():\n                return 1\n            else:\n                return 0\n        for i in range(0, length):\n            print(s, i)\n            if i == 0:\n                s.append(s.popleft())\n                continue\n            s_right = s.pop()\n            s_left = s.popleft()\n            if s_right == s_left:\n                cnt = length - i - 2\n                s.rotate(cnt)\n                break\n            else:\n                if i == length - 1:\n                    return 0\n                else:\n                    s.append(s_right)\n                    s.append(s_left)\n\n            \n            \n    answer = 1\n    return answer\n\nprint(solution(s))","repo_name":"LLagoon3/Programmers","sub_path":"Python3/Level_2/코딩테스트 연습 2017 팁스타운 짝지어 제거하기2.py","file_name":"코딩테스트 연습 2017 팁스타운 짝지어 제거하기2.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72133264888","text":"from discord import PermissionOverwrite, Embed, ButtonStyle, Interaction, utils, Member, Guild\nfrom discord.ui import View, Button\n\nfrom Roles.guildroles import GuildRoles\n\nclass ConfirmButton(Button):\n    \n    def __init__(self, member: Member) -> None:\n        super().__init__(style=ButtonStyle.primary, emoji=\"<:check_blue:1125608256153337987>\")\n        \n        self.member = member\n        \n    async def callback(self, interaction: Interaction):\n        \n        gr = GuildRoles(interaction.guild)\n        # print(gr.getGuildProperties())\n        roleid = int(gr.getGuildProperties()[\"default_role_verified_id\"])\n        verified_role = interaction.guild.get_role(roleid)\n        unverified_role = interaction.guild.get_role(int(gr.getGuildProperties()[\"default_role_unverified_id\"]))\n        # print(verified_role.name, verified_role.id)\n        approve_embed = Embed(title=f\"{self.member} has been approved!\", description=\"Go ahead and welcome them into the family\")\n        await interaction.response.send_message(embed=approve_embed)\n        \n        await self.member.add_roles(verified_role)\n        await self.member.remove_roles(unverified_role)\n        \n\nclass ConfirmView(View):\n    \n    def __init__(self, member: Member):\n        super().__init__(timeout=None)\n        \n        self.add_item(ConfirmButton(member=member))\n\n\nclass VerifyButton(Button):\n    \n    def __init__(self, guild: Guild) -> None:\n        super().__init__(style=ButtonStyle.primary, emoji=\"<:check_green:1125605364340097044>\")\n\n        self.custom_id = str(guild.id) + str(guild.name)\n        \n    async def callback(self, interaction: Interaction):\n        \n        verify_channel = utils.get(interaction.guild.channels, name=\"verification-requests\")\n        confirm_view = ConfirmView(interaction.user)\n        confirm_embed = Embed(title=f\"{interaction.user.name} is requesting for verification\", description=f\"Click on the button below to grant {interaction.user.name} access to the server.\")\n        await verify_channel.send(embed=confirm_embed, view=confirm_view)\n        \n        sent_embed = Embed(title=\"Request Sent\", description=\"Please wait patiently while someone approves your request.\")\n        await interaction.response.send_message(embed=sent_embed, ephemeral=True)\n\n\nclass VerifyView(View):\n    \n    def __init__(self, guild: Guild):\n        super().__init__(timeout=None)\n        \n        vb = VerifyButton(guild)\n        \n        self.add_item(vb)\n","repo_name":"Lejio/System-Bot","sub_path":"src/server/discord/Admin/verify.py","file_name":"verify.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22354534234","text":"# Lesson 5 - function compile()\r\nimport time\r\nimport math\r\n\r\nsource = \"reportLine += 1\"\r\n\r\nreportLine = 0\r\nstart = time.time()\r\nfor i in range(100000):\r\n    exec(source)\r\nstop = time.time()\r\ntime_not_compiled = stop - start\r\n\r\nstart = time.time()\r\nsourceCompiled = compile(source, 'internal variable source', 'exec')\r\nfor i in range(100000):\r\n    exec(sourceCompiled)\r\nstop = time.time()\r\ntime_compiled = stop - start\r\n\r\nprint(time_not_compiled)\r\nprint(time_compiled)\r\nprint(time_not_compiled / time_compiled)\r\n\r\n# LAB\r\nformulas_list = [\r\n     \"abs(x**3 - x**0.5)\",\r\n     \"abs(math.sin(x) * x**2)\"\r\n     ]\r\nprint('-' * 10, 'NOT COMPILED START', '-' * 10)\r\n\r\nargument_list = []\r\nfor i in range(100000):\r\n    argument_list.append(i/10)\r\n\r\nresults_list = []\r\nfor formula in formulas_list:\r\n    print('Loop working right now with formula', formula)\r\n    start = time.time()\r\n    for arg in argument_list:\r\n        x = arg\r\n        results_list.append(eval(formula))\r\nprint('Max: {} Min: {}'.format(max(results_list), min(results_list)))\r\nstop = time.time()\r\nnot_compiled = stop - start\r\nprint('-' * 10, 'COMPILED START', '-' * 10)\r\n\r\nresults_list = []\r\nfor formula in formulas_list:\r\n    print('Loop working right now with formula', formula)\r\n    start = time.time()\r\n    compiled_formula = compile(formula, formula, 'eval')\r\n    for arg in argument_list:\r\n        x = arg\r\n        results_list.append(eval(compiled_formula))\r\nprint('Max: {} Min: {}'.format(max(results_list), min(results_list)))\r\nstop = time.time()\r\ncompiled = stop - start\r\nprint('-' * 10, 'TIME RESULTS', '-' * 10)\r\nprint('Time with compile is {}'.format(compiled))\r\nprint('Time without compile is {}'.format(not_compiled))","repo_name":"konfle/Python","sub_path":"Code development/function_compile.py","file_name":"function_compile.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26741009588","text":"from selenium.common.exceptions import NoAlertPresentException # в начале файла\nfrom .base_page import BasePage\nfrom .locators import ProductPageLocators, BasketPageLocators\n\nimport math\n\nempty_texts = [\"سلة التسوق فارغة\",\n\"La seva cistella està buida.\",\n\"Váš košík je prázdný.\",\n\"Din indkøbskurv er tom.\",\n\"Ihr Warenkorb ist leer.\",\n\"Your basket is empty.\",\n\"Το καλάθι σας είναι άδειο.\",\n\"Tu carrito esta vacío.\",\n\"Korisi on tyhjä\",\n\"Votre panier est vide.\",\n\"Il tuo carrello è vuoto.\",\n\"장바구니가 비었습니다.\",\n\"Je winkelmand is leeg\",\n\"Twój koszyk jest pusty.\",\n\"O carrinho está vazio.\",\n\"Sua cesta está vazia.\",\n\"Cosul tau este gol.\",\n\"Ваша корзина пуста\",\n\"Váš košík je prázdny\",\n\"Ваш кошик пустий.\",\n\"Your basket is empty.\"]\n\n\nclass ProductPage(BasePage):\n    def should_be_product_page(self):\n        self.should_be_product_url()\n        self.should_be_same_product_name()\n        self.should_be_same_product_price()\n        self.add_to_basket()\n        self.should_not_be_success_message()\n        self.should_not_be_item_basket()\n        self.solve_quiz_and_get_code()\n    \n    def should_be_product_url(self):\n        assert 'promo=newYear' in self.browser.current_url, 'It is not product page!'\n\n    def should_be_same_product_name(self):\n        product_name = self.browser.find_element(*ProductPageLocators.PRODUCT_NAME).text\n        product_name_in_basket = self.browser.find_element(*ProductPageLocators.PRODUCT_NAME_IN_BASKET).text\n        assert product_name == product_name_in_basket, 'Product name is not the same!'\n    \n    def should_be_same_product_price(self):\n        product_price = self.browser.find_element(*ProductPageLocators.PRODUCT_PRICE).text\n        product_price_in_basket = self.browser.find_element(*ProductPageLocators.PRODUCT_PRICE_IN_BASKET).text\n        assert product_price == product_price_in_basket, 'Product price is not the same!'\n\n    def add_to_basket(self):\n        self.browser.find_element(*ProductPageLocators.ADD_TO_BASKET).click()\n\n    def should_not_be_success_message(self):\n        assert self.is_not_element_present(*ProductPageLocators.SUCCESS_MESSAGE), \\\n        \"Success message is presented, but should not be\"\n\n\n    def success_message_is_disappeared(self):\n        assert self.is_disappeared(*ProductPageLocators.SUCCESS_MESSAGE), \\\n        \"Success message should be disappeared, but should not be\"\n\n    def should_not_be_item_basket(self):\n        assert self.is_not_element_present(*BasketPageLocators.BASKET_ITEMS), \\\n        \"Basket item is presented, but should not be\"\n\n    def should_be_empty_basket_text(self):\n        text = self.browser.find_element(*BasketPageLocators.EMPTY_BASKET_TEXT).text\n        for empty_text in empty_texts:\n            if empty_text in text:\n                return True\n        return False\n\n\n    def solve_quiz_and_get_code(self):\n        alert = self.browser.switch_to.alert\n        x = alert.text.split(\" \")[2]\n        answer = str(math.log(abs((12 * math.sin(float(x))))))\n        alert.send_keys(answer)\n        alert.accept()\n        try:\n            alert = self.browser.switch_to.alert\n            alert_text = alert.text\n            print(f\"Your code: {alert_text}\")\n            alert.accept()\n        except NoAlertPresentException:\n            print(\"No second alert presented\")\n\n","repo_name":"spurlats/final_project","sub_path":"pages/product_page.py","file_name":"product_page.py","file_ext":"py","file_size_in_byte":3398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21863695204","text":"text=['Он. ','её любил. Она съела кусок мяса. Он её',\r\n      'убил. И в землю закопал.']\r\nminlen=len(text[0].split(' '))\r\nmaxword='В тексте нет слов'\r\nword=''\r\nmaxwordlen=0\r\nwordcount=0\r\nmincount=len(text[0])\r\nlocmaxwordlen=0\r\nfor i in range(len(text)):\r\n    wordcount+=1\r\n    if len(word)>locmaxwordlen:\r\n        locmaxword=word\r\n        locmaxwordlen=len(word)\r\n    word=''\r\n    print(text[i])\r\n    for j in range(len(text[i])):\r\n        lord=ord(text[i][j].lower())\r\n        if lord>=ord('а') and lord<=ord('я') or lord==ord('ё'):\r\n            word+=text[i][j]\r\n        elif text[i][j]==' ':\r\n            wordcount+=1\r\n            if len(word)>locmaxwordlen:\r\n                locmaxword=word\r\n                locmaxwordlen=len(word)\r\n            word=''\r\n        elif text[i][j]=='.':\r\n            if len(word)>locmaxwordlen:\r\n                    locmaxword=word\r\n                    locmaxwordlen=len(word)\r\n            if wordcount<mincount or (wordcount==mincount\r\n                                      and len(locmaxword)>maxwordlen):\r\n                mincount=wordcount\r\n                maxword=locmaxword\r\n                maxwordlen=len(locmaxword)\r\n            word=locmaxword=''\r\n            locmaxwordlen=0\r\n            wordcount=0   \r\nprint('\\n',end=maxword)\r\n","repo_name":"tekcellat/University","sub_path":"First course/1st semester/Защитаv0.5.py","file_name":"Защитаv0.5.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27373711037","text":"import os\nimport threading\nimport queue\nimport time\nimport sys\nimport traceback\nfrom mrf_structs import *\nimport ctypes\nimport unittest\n\nimport signal\n\nclass rx_thread(threading.Thread):\n    \"\"\"\n        A thread class to read stub output\n    \"\"\"\n\n    def __init__ (self, stub_out_fifo_path,q):        \n        self.stub_out_fifo_path = stub_out_fifo_path\n        self.q = q\n        print(\"rx_thread.init: stub_out_fifo_path = %s\"%self.stub_out_fifo_path)\n        self.app_out_fifo = os.open(self.stub_out_fifo_path, os.O_RDONLY | os.O_NONBLOCK)\n        print(\"rx_thread app_out_fifo type is %s\"%type(self.app_out_fifo))\n        self._stop = False\n        threading.Thread.__init__ (self)\n   \n    def stop(self):\n        self._stop = True\n    def run(self):\n        while True:\n            try:\n                resp = os.read(self.app_out_fifo, MRFBUFFLEN)\n                #print \"rx_thread.read got something, length is %d\"%len(resp)\n                self.q.put(resp)\n            except:\n                if self._stop:\n                    return\n\ndef buffToObj(resp,app_cmds):\n    hdr = PktHeader()\n    print(\"buffToObj len is %d\"%len(resp))\n    if len(resp) >= len(hdr):\n        hdr_data = bytes(resp)[0:len(hdr)]\n        hdr.load(bytes(resp)[0:len(hdr)])\n        print(\"hdr is %s\"%repr(hdr))\n        if hdr.type in list(MrfSysCmds.keys()):\n            if MrfSysCmds[hdr.type]['param'] and ( MrfSysCmds[hdr.type]['name'] == 'USR_RESP' or MrfSysCmds[hdr.type]['name'] == 'USR_STRUCT' ):  # testing aye\n                param = MrfSysCmds[hdr.type]['param']()\n                param_data = bytes(resp)[len(hdr):len(hdr)+len(param)]\n                param.load(param_data)\n                if param.type in list(MrfSysCmds.keys()) and MrfSysCmds[param.type]['resp']:\n                    respobj = MrfSysCmds[param.type]['resp']()\n                    respdat = bytes(resp)[len(hdr)+len(param):len(hdr)+len(param) + len(respobj)]\n                    respobj.load(respdat)\n                    return respobj\n                elif param.type in list(app_cmds.keys()) and app_cmds[param.type]['resp']:\n                    respobj = app_cmds[param.type]['resp']()\n                    respdat = bytes(resp)[len(hdr)+len(param):len(hdr)+len(param) + len(respobj)]\n                    respobj.load(respdat)\n                    return respobj\n                else:\n                    print(\"got param type %d \"%param.type)\n            else:\n                print(\"got hdr.type %d\"%hdr.type)\n    return None\n\n                \nclass struct_thread(threading.Thread):\n    \"\"\"\n        A thread class to read stub structure output\n    \"\"\"\n\n    def __init__ (self, stub_out_fifo_path,app_cmds):        \n        self.stub_out_fifo_path = stub_out_fifo_path\n        print(\"struct_thread.init: stub_out_fifo_path = %s\"%self.stub_out_fifo_path)\n        self.app_out_fifo = os.open(self.stub_out_fifo_path, os.O_RDONLY | os.O_NONBLOCK)\n        print(\"struct_thread app_out_fifo type is %s\"%type(self.app_out_fifo))\n        self._stop = False\n        self.app_cmds = app_cmds\n        threading.Thread.__init__ (self)\n   \n    def stop(self):\n        self._stop = True\n    def run(self):\n        while True:\n            try:\n                resp = os.read(self.app_out_fifo, MRFBUFFLEN)\n                print(\"struct_thread.read got something, length is %d\"%len(resp))\n                rstr = buffToObj(resp)\n                print(\"rstr %s\"%repr(rstr))\n                #self.q.put(resp)\n            except:\n                if self._stop:\n                    return\n\n    \n\nclass StubIf(object):\n    def __init__(self):\n        self.q = queue.Queue()\n        self.stub_out_pipe_path = \"/tmp/mrf_bus/0-app-out\"\n        self.stub_struct_pipe_path = \"/tmp/mrf_bus/0-app-str\"\n        self.app_fifo_pipe_path = \"/tmp/mrf_bus/0-app-in\"\n        self.rx_thread = rx_thread(self.stub_out_pipe_path, self.q)\n        self.rx_thread.start()\n        print(\"StubIf trying to open app_fifo %s\"%self.app_fifo_pipe_path)\n        self.app_fifo = open(self.app_fifo_pipe_path,\"w\")\n        print(\"StubIf.__init__  opened app_fifo \")\n        print(repr(self.app_fifo))\n        outfname = \"/tmp/mrf_bus/0-app-out\"\n        self.app_cmds = {}  # need to override in some ugly way to allow app command testing for now\n\n        self.struct_thread = struct_thread(self.stub_struct_pipe_path,self.app_cmds)\n        self.struct_thread.start()\n\n        \n    def bin2hex(self,buff):\n        return ''.join('{:02x}'.format(x) for x in bytearray(buff))\n\n    def cmd(self,dest,cmd_code,dstruct=None):\n\n        if dest > 255:\n            print(\"dest > 255\")\n            return -1\n\n\n        if cmd_code in list(MrfSysCmds.keys()):\n            paramtype = MrfSysCmds[cmd_code]['param']\n\n        elif cmd_code in list(self.app_cmds.keys()):\n            paramtype = self.app_cmds[cmd_code]['param']\n            #print \"got app command - cmd_code %d  paramtype %s :\\n   %s\"%(cmd_code,\n            #                                                              repr(paramtype),\n            #                                                              repr(dstruct),\n            #)\n        else:\n            print(\"unrecognised cmd_code (01xs) %d\"%cmd_code)\n            return -1\n            \n         \n        if type(dstruct) == type(None) and type(paramtype) != type(None):\n            print(\"No param sent , expected %s\"%type(paramtype))\n            return -1\n        \n        if type(paramtype) == type(None) and type(dstruct) != type(None):\n            print(\"Param sent ( type %s ) but None expected\"%type(dstruct))\n            return -1\n\n        if type(dstruct) != type(None) and type(dstruct) != type(paramtype()):\n            print(\"Param sent ( type %s ) but  %s expected\"%(type(dstruct),type(paramtype())))\n            return -1\n\n        while not self.q.empty():\n            self.q.get()\n\n        mlen = 4\n        if dstruct:\n            mlen += len(dstruct)\n        if mlen > 64:\n            print(\"mlen = %d\"%mlen)\n            return -1\n        msg = bytearray(mlen)\n        msg[0] = mlen\n        msg[1] = dest\n        msg[2] = cmd_code\n        \n        n = 3\n        if dstruct:\n            dbytes = dstruct.dump()\n            for b in dbytes:\n                msg[ n ] =  b\n                n += 1\n\n        csum = 0\n        for i in range(mlen -1):\n            csum += int(msg[i])\n        csum = csum % 256\n        msg[mlen - 1] = csum\n        self.app_fifo.write(msg)\n        self.app_fifo.flush()\n        return 0\n    def response(self,timeout = 1.0):\n        elapsed = 0.0\n        tinc = 0.1\n        while self.q.empty():\n            time.sleep(tinc)\n            elapsed += tinc\n            if elapsed >= timeout:\n                return None\n        resp = self.q.get()\n        return buffToObj(resp,self.app_cmds)\n                        \n    def quit(self):\n        self.rx_thread.join(0.1)\n        self.rx_thread.stop()\n        self.struct_thread.join(0.1)\n        self.struct_thread.stop()\n\n    def cmd_test(self,dest,cmd_code,expected,dstruct=None):\n        \"\"\"\n        simple test interface to send cmds to destinations, supplying expected values for checking\n        \"\"\"\n        rv = self.cmd(dest,cmd_code)\n        if (rv != 0):\n            return rv\n        rsp = self.response()\n\n        print(\"received:\\n %s\"%repr(rsp))\n        print(\"expected:\\n %s\"%repr(expected))        \n        if rsp != expected:\n            print(\"ERROR \")\n            print(\"cmd_test failed\")\n            return -1\n        else:\n            print(\"cmd_test passed\")\n            return 0\n\n\nclass StubTestCase(unittest.TestCase):\n    def setUp(self):\n        print(\"StubTestCase setUp : entry\")\n        def exit_nicely(signum,frame):\n            signal.signal(signal.SIGINT, self.original_sigint)\n            print(\"CTRL-C pressed , quitting\")\n            self.tearDown()            \n        self.original_sigint = signal.getsignal(signal.SIGINT)\n        signal.signal(signal.SIGINT, exit_nicely)\n        #unittest.installHandler()\n        self.timeout = 0.4\n        self.stub = StubIf()\n\n    def tearDown(self):\n        self.stub.quit()\n","repo_name":"ReallyGnusys/mrfbus","sub_path":"land/teststub.py","file_name":"teststub.py","file_ext":"py","file_size_in_byte":8067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72999855288","text":"import smtplib\n\nwith smtplib.SMTP('smtp.gmail.com',587) as smtp:\n    smtp.ehlo()\n    smtp.starttls()\n    smtp.ehlo()\n    smtp.login('manasirvariar13121999@gmail.com','gfduqhtxinpmymxi')\n    \n    subject= 'how are you?'\n    body= 'I hope you are fine.'\n\n    msg= f'Subject: {subject}\\n\\n{body}'\n\n    smtp.sendmail('manasirvariar13121999@gmail.com', 'manasirvariar007@gmail.com', msg)\n    print('EMAIL SENT!')\n\n    smtp.quit()\n","repo_name":"manasivariar/email-bot","sub_path":"sending_emails.py","file_name":"sending_emails.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15347498382","text":"import time\nimport pyautogui\nfrom libs.shared import is_img_on_screen, locate_and_click_center, locate_and_click_center_2, wait_for_existance\n\nclass combat:\n  @staticmethod\n  def criteria():\n    if not is_img_on_screen('combat_exist'):\n      raise ValueError(\"criteria mismatch: combat.exec()\")\n\n  @staticmethod\n  def exec(boss_fight = False):\n    combat.criteria()\n    combat.play_hand()\n    combat.end_turn()\n\n    while True:\n      # expect loop to start for every ability selection state\n      choose_abilities.wait_criteria()\n      choose_abilities.exec()\n      combat.end_turn()\n\n      # trigger battle ~~\n\n      ended = combat.has_combat_ended()\n      if ended:\n        print('combat ended!')\n\n        if boss_fight:\n          print('do boss rewards')\n\n          print('time to click on rewards')\n\n          ref = pyautogui.locateOnScreen('images/opt_ref.png', confidence = 0.9)\n\n          # skip dummies\n          time.sleep(3)\n          pyautogui.click(ref.left - 100, ref.top)\n          time.sleep(3)\n          pyautogui.click(ref.left - 100, ref.top)\n          time.sleep(3)\n          pyautogui.click(ref.left - 100, ref.top)\n\n          time.sleep(15)\n\n          print('ref', ref)\n\n          pyautogui.click(ref.left - 300, ref.top - 100)\n          time.sleep(1.5)\n          pyautogui.click(ref.left - 600, ref.top - 100)\n          time.sleep(1.5)\n          pyautogui.click(ref.left - 700, ref.top - 400)\n          time.sleep(1.5)\n          pyautogui.click(ref.left - 200, ref.top - 400)\n          time.sleep(1.5)\n          pyautogui.click(ref.left - 400, ref.top - 550)\n          time.sleep(1.5)\n          pyautogui.click(ref.left - 400, ref.top - 310)\n          time.sleep(5)\n\n          print('click completed')\n\n          pyautogui.moveTo(ref.left - 100, ref.top)\n\n          wait_for_existance('complete_ok')\n          locate_and_click_center('complete_ok')\n          wait_for_existance('felwood_title')\n\n        else:\n          wait_for_existance('combat_reward', dummy_click=True)\n\n        break\n      else:\n        print('fight temp ended, wait for another round')\n        continue\n\n  @staticmethod\n  def revert():\n    locate_and_click_center_2('opt_ref')\n    print('opened ref')\n    time.sleep(5)\n    locate_and_click_center('sur')\n    print('clicked sur')\n    wait_for_existance('felwood_title', dummy_click=True)\n    return\n\n  @staticmethod\n  def play_hand():\n    combat.play_rag()\n    combat.play_baron()\n    combat.play_anton()\n\n  @staticmethod\n  def play_rag():\n    rag = pyautogui.locateOnScreen('images/hand_rag.png', confidence = 0.6)\n\n    if rag is None:\n      raise ValueError(\"rag hero died in combat\")\n\n    pyautogui.moveTo(rag.left + rag.width/2, rag.top + rag.height/2)\n    time.sleep(2)\n    pyautogui.mouseDown(button='left')\n    time.sleep(2)\n    pyautogui.moveTo(rag.left + rag.width/2, rag.top + rag.height/2 - 500)\n    time.sleep(2)\n    pyautogui.mouseUp(button='left')\n    time.sleep(3)\n\n    wait_for_existance('board_rag')\n    \n\n  @staticmethod\n  def play_baron():\n    target = pyautogui.locateOnScreen('images/hand_baron.png', confidence = 0.7)\n\n    pyautogui.moveTo(target.left + target.width/2, target.top + target.height/2)\n    time.sleep(2)\n    pyautogui.mouseDown(button='left')\n    time.sleep(2)\n    pyautogui.moveTo(target.left + target.width/2, target.top + target.height/2 - 500)\n    time.sleep(2)\n    pyautogui.mouseUp(button='left')\n    time.sleep(3)\n\n    wait_for_existance('board_baron')\n\n  @staticmethod\n  def play_anton():\n    target = pyautogui.locateOnScreen('images/hand_anton.png', confidence = 0.7)\n\n    if target is None:\n      raise ValueError(\"anton hero died in combat\")\n\n    pyautogui.moveTo(target.left + target.width/2, target.top + target.height/2)\n    time.sleep(2)\n    pyautogui.mouseDown(button='left')\n    time.sleep(2)\n    pyautogui.moveTo(target.left + target.width/2, target.top + target.height/2 - 500)\n    time.sleep(2)\n    pyautogui.mouseUp(button='left')\n    time.sleep(3)\n\n    wait_for_existance('board_anton')\n\n  @staticmethod\n  def has_combat_ended():\n    loop_count = 0\n\n    while True:\n      loop_count = loop_count + 1\n      time.sleep(5)\n      still_in_combat = pyautogui.locateOnScreen('images/ab_2_ok_baron.png', confidence = 0.7)\n      combat_won = pyautogui.locateOnScreen('images/combat_won.png', confidence = 0.7)\n      someone_died = pyautogui.locateOnScreen('images/someone_died.png', confidence = 0.7)\n\n\n      if still_in_combat:\n        return False\n      elif combat_won:\n        return True\n      elif someone_died:\n        raise ValueError(\"hero died in combat\")\n      else:\n        if loop_count > 10:\n          raise ValueError(\"has_combat_ended() unexpected state\")\n        else:\n          continue\n\n\n  @staticmethod\n  def end_turn():\n    time.sleep(2)\n    wait_for_existance('end_turn')\n    locate_and_click_center('end_turn')\n\nclass choose_abilities:\n  @staticmethod\n  def wait_criteria():\n    wait_for_existance('ab_2_ok_baron')\n\n  @staticmethod\n  def exec():\n    wait_for_existance('ab_2_ok_baron')\n    locate_and_click_center('ab_2_ok_baron')\n\n    wait_for_existance('ab_2_ok_rag')\n    locate_and_click_center('ab_2_ok_rag')\n\n    wait_for_existance('ab_1_ok_anton')\n    anton_target = pyautogui.locateOnScreen('images/ab_1_ok_anton.png', confidence = 0.7)\n    locate_and_click_center('ab_1_ok_anton')\n    pyautogui.click(anton_target.left + anton_target.width/2 + 120, anton_target.top - 95) \n\n    print('try to see if bomb exist')\n\n    time.sleep(3)\n    if is_img_on_screen('bomb', confidence=0.5):\n      print('trigger bomb')\n      locate_and_click_center('bomb')\n    else:\n      print('bomb not exist')\n\n    \n  ","repo_name":"alxtz/auto","sub_path":"libs/combat.py","file_name":"combat.py","file_ext":"py","file_size_in_byte":5620,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"44744972395","text":"import pandas as pd\nimport numpy as np\nimport sklearn.preprocessing as sp\n \nimport chainer\nfrom chainer import cuda, Function, gradient_check, report, training, utils, Variable\nfrom chainer import datasets, iterators, optimizers, serializers\nfrom chainer import Link, Chain, ChainList\nimport chainer.functions as F\nimport chainer.links as L\nfrom chainer.training import extensions\nfrom chainer.functions.loss.mean_squared_error import mean_squared_error\nfrom chainer.datasets import tuple_dataset\nimport optuna\n\nimport matplotlib.pyplot as plt\n\nimport math\nimport functools\nimport pickle\n\n#LSTMを複数層重ねた学習モデル\n#x_i -> Linear -> LSTM -> LSTM -> ... -> LSTM -> Linear -> output\n#listにそれぞれの層のLSTMCellの数を与える\nclass Model(Chain):\n    def __init__(self, list, dr=0.2, train=True):\n\n        lst=[]\n        p=list[0]\n        for e in list[1:]:\n            lst.append(L.LSTM(p, e))\n            p=e\n        super(Model, self).__init__()\n        with self.init_scope():\n            self.dr = dr if train else 0.0\n            self.s=L.Linear(1,list[0])\n            self.l=lst\n            for i,v in enumerate(lst):\n                self.add_link('lst_{}'.format(i), v)\n            self.t=L.Linear(p, 1)\n         \n    def reset_state(self):\n        for e in self.l:\n            e.reset_state()\n         \n    def __call__(self, x):\n        h = F.dropout(F.relu(self.s(x)), ratio=self.dr)\n        for e in self.l:\n            h = F.dropout(F.relu(e(h)), ratio=self.dr)\n        o = self.t(h)\n        return o\n\nclass LossFuncL(Chain):\n    def __init__(self, predictor):\n        super(LossFuncL, self).__init__(predictor=predictor)\n\n    def __call__(self, x, t):\n        #print(type(x))\n        if isinstance(x, np.ndarray):\n            x=chainer.Variable(x)\n            t=chainer.Variable(t)\n        #print(x.data)\n        x.data = x.data.reshape((-1, 1)).astype(np.float32)\n        t.data = t.data.reshape((-1, 1)).astype(np.float32)\n\n        y = self.predictor(x)\n        loss = F.mean_squared_error(y, t)\n        report({'loss':loss}, self)\n        return loss\n\n    def setdr(self,dr):\n        self.predictor.dr=dr\n\n#時系列データを持ち、getdataで展開する\nclass LSTM_Iterator(chainer.dataset.Iterator):\n    def __init__(self, dataset, batch_size = 10, seq_len = 5, repeat = True):\n        self.seq_length = seq_len\n        self.dataset = dataset\n        self.nsamples =  len(dataset)\n\n        self.batch_size = batch_size\n        self.repeat = repeat\n\n        self.epoch = 0\n        self.iteration = 0\n        self.offsets = np.random.randint(0, len(dataset),size=batch_size)\n\n        self.is_new_epoch = False\n\n    def __next__(self):\n        if not self.repeat and self.iteration * self.batch_size >= self.nsamples:\n            raise StopIteration\n\n        x, t = self.get_data()\n        self.iteration += 1\n\n        epoch = self.iteration // self.batch_size\n        self.is_new_epoch = self.epoch < epoch\n        if self.is_new_epoch:\n            self.epoch = epoch\n            self.offsets = np.random.randint(0, self.nsamples,size=self.batch_size)\n\n        #print(x)\n        return list(zip(x, t))\n\n    @property\n    def epoch_detail(self):\n        return self.iteration * self.batch_size / len(self.dataset)\n\n    def get_data(self):\n        tmp0 = [self.dataset[(offset + self.iteration)%self.nsamples][0]\n               for offset in self.offsets]\n        tmp1 = [self.dataset[(offset + self.iteration + 1)%self.nsamples][0]\n               for offset in self.offsets]\n        return tmp0,tmp1\n\n    def serialzie(self, serialzier):\n        self.iteration = serializer('iteration', self.iteration)\n        self.epoch     = serializer('epoch', self.epoch)\n\n#学習によってModelの更新を行う\nclass LSTM_updater(training.StandardUpdater):\n    def __init__(self, train_iter, optimizer, device):\n        super(LSTM_updater, self).__init__(train_iter, optimizer, device=device)\n        self.seq_length = train_iter.seq_length\n\n    def update_core(self):\n        loss = 0\n\n        train_iter = self.get_iterator('main')\n        optimizer = self.get_optimizer('main')\n\n        for i in range(self.seq_length):\n            batch = np.array(train_iter.__next__()).astype(np.float32)\n            x, t  = batch[:,0].reshape((-1,1)), batch[:,1].reshape((-1,1))\n            loss += optimizer.target(chainer.Variable(x), chainer.Variable(t))\n\n        optimizer.target.zerograds()\n        loss.backward()\n        loss.unchain_backward()\n        optimizer.update()\n\n\n\n#dataを読み込む dataの最大値で割って[0,1]にする\ndef getdata():\n    #normalize\n    data = np.loadtxt(\"./output_300.txt\", comments='#').astype(np.float32)\n    #data_size=1800\n    #data = np.array([math.sin(math.pi * 11 * i / 180)*0.8+np.random.normal(0,0.0) for i in range(data_size)]).astype(np.float32)\n    #data = np.log(data + 1)\n    data_max = np.max(data)\n    data = data/data_max\n    return data, data_max\n\n#[0,1]にされたデータを戻す\ndef expand(data, data_max):\n    #return np.exp(np.multiply(data, data_max)) - 1\n    return np.multiply(data, data_max)\n\ndef compress(data, data_max):\n    #return np.exp(np.multiply(data, data_max)) - 1\n    return np.divide(data, data_max)\n\n#test全てを連続して予測する\ndef predicateTestAll(train, test, model, data_max):\n    model.predictor.reset_state()\n    model.setdr(0.0)\n    res=[]\n    train_size=len(train)\n    test_size=len(test)\n    for i in range(train_size):\n        y=model.predictor(chainer.Variable(train[i][0].reshape(-1,1)))\n        res.append(train[i][0])\n    for i in range(test_size):\n        y = model.predictor(chainer.Variable(y.data))\n        res.append(y.data)\n\n    res = np.array(res).astype(np.float32)\n    train = expand(train,data_max)\n    test = expand(test,data_max)\n    res2 = expand(res,data_max)\n    plt.plot([i for i in range(train_size)], train)\n    plt.plot([train_size + i for i in range(test_size)], test)\n    plt.plot([i for i in range(train_size+test_size)], res2)\n    plt.show()\n    \n    return res\n\n#任意の地点でseq_lenからseq_len+1を予測\ndef predicateNext(data, model, seq_len, data_max):\n    model.predictor.reset_state()\n    model.setdr(0.0)\n    presteps = seq_len+1\n    model.predictor.reset_state()\n    \n    for i in range(presteps):\n        y = model.predictor(chainer.Variable(np.roll(data,i).reshape((-1,1))))\n    \n    res = expand(y.data, data_max)\n    data = expand(data, data_max)\n    plt.plot([i for i in range(len(data))],data)\n    plt.plot([i for i in range(len(data))],np.roll(res,-presteps))\n    plt.show()\n\n    return res\n\n#20ステップ先まで予測する\ndef predicate20Steps(train, test, model, data_max):\n    model.predictor.reset_state()\n    model.setdr(0.0)\n    res=[]\n    train_size=len(train)\n    test_size=len(test)\n    for i in range(train_size):\n        y=model.predictor(chainer.Variable(train[i][0].reshape(-1,1)))\n        res.append(train[i][0])\n    for i in range(test_size//20):\n        model2 = model.copy() \n        for j in range(20):\n            y = model2.predictor(chainer.Variable(y.data))\n            model.predictor(chainer.Variable(test[i*20+j][0].reshape(-1,1)))\n            res.append(y.data)\n\n    for j in range(test_size%20):\n        y = model.predictor(chainer.Variable(y.data))\n        res.append(y.data)\n\n    \n    res = np.array(res).astype(np.float32)\n    train = expand(train,data_max)\n    test = expand(test,data_max)\n    res2 = expand(res,data_max)\n    #plt.plot([i for i in range(data_size)], train)\n    #plt.plot([data_size + i for i in range(data2_size)], test)\n    #plt.plot([i for i in range(data_size+data2_size)], res)\n    plt.plot([i for i in range(test_size)], test, linewidth=1.0)\n    for p in range(test_size//20):\n        plt.plot([p*20+i for i in range(20)], res2[train_size+p*20:train_size+(p+1)*20], linewidth=1.0, color='red')\n    plt.plot([test_size//20*20+i for i in range(test_size%20)], res2[train_size+test_size//20*20:], linewidth=1.0, color='red')\n    plt.show()\n    #print(F.mean_squared_error(res2[train_size:], test.reshape(-1)))\n\n    return res","repo_name":"taiseiKMC/wave_prediction","sub_path":"src/lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":8067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72393603450","text":"# SPDX-License-Identifier: MIT\nimport struct\nfrom dataclasses import dataclass\nfrom enum import IntEnum\n\nfrom ..asc.base import *\nfrom ...utils import *\n\n## DCP main endpoint\n\nclass DCPMessage(Register64):\n    TYPE        = 3, 0\n\nclass DCPEp_SetShmem(DCPMessage):\n    DVA         = 63, 16\n    FLAG        = 7, 4\n    TYPE        = 3, 0, Constant(0)\n\nclass DCPEp_InitComplete(DCPMessage):\n    TYPE        = 3, 0, Constant(1)\n\nclass CallContext(IntEnum):\n    CB          = 0\n    CMD         = 2\n    ASYNC       = 3\n    OOBCB       = 4\n    OOBCMD      = 6\n\nclass DCPEp_Msg(DCPMessage):\n    LEN         = 63, 32\n    OFF         = 31, 16\n    CTX         = 11, 8, CallContext\n    ACK         = 6\n    TYPE        = 3, 0, Constant(2)\n\n@dataclass\nclass DCPCallState:\n    tag: str\n    off: int\n    in_len: int\n    in_data: bytes\n    out_addr: int\n    out_len: int\n    complete: bool = False\n\nclass DCPCallChannel(Reloadable):\n    def __init__(self, dcpep, name, buf, bufsize):\n        self.dcp = dcpep\n        self.name = name\n        self.buf = buf\n        self.bufsize = bufsize\n        self.off = 0\n        self.pending = []\n\n    def ack(self):\n        if not self.pending:\n            raise Exception(\"ACK with no calls pending\")\n\n        self.pending[-1].complete = True\n\n    def call(self, ctx, tag, inbuf, out_len):\n        in_len = len(inbuf)\n        data = tag.encode(\"ascii\")[::-1] + struct.pack(\"<II\", in_len, out_len) + inbuf\n        data_size = len(data) + out_len\n        assert (self.off + data_size) <= self.bufsize\n\n        self.dcp.asc.iface.writemem(self.dcp.shmem + self.buf + self.off, data)\n\n        state = DCPCallState(off=self.off, tag=tag, in_len=in_len, in_data=data, out_len=out_len,\n                             out_addr=self.buf + self.off + 12 + in_len)\n\n        self.off += align_up(data_size, 0x40)\n        self.pending.append(state)\n\n        print(f\"len={data_size:#x} {in_len}\")\n        self.dcp.send(DCPEp_Msg(LEN=data_size, OFF=state.off, CTX=ctx, ACK=0))\n\n        while not state.complete:\n            self.dcp.asc.work()\n\n        print(f\"off={state.out_addr:#x} len={out_len}\")\n        out_data = self.dcp.asc.iface.readmem(self.dcp.shmem + state.out_addr, out_len)\n\n        assert self.pending.pop() is state\n        self.off = state.off\n\n        return out_data\n\nclass DCPCallbackChannel(Reloadable):\n    def __init__(self, dcpep, name, buf, bufsize):\n        self.dcp = dcpep\n        self.name = name\n        self.buf = buf\n        self.bufsize = bufsize\n        self.pending = []\n\n    def cb(self, msg):\n        data = self.dcp.asc.iface.readmem(self.dcp.shmem + self.buf + msg.OFF, msg.LEN)\n        tag = data[:4][::-1].decode(\"ascii\")\n        in_len, out_len = struct.unpack(\"<II\", data[4:12])\n        in_data = data[12:12 + in_len]\n\n        state = DCPCallState(off=msg.OFF, tag=tag, in_len=in_len, out_len=out_len,\n                             in_data=in_data, out_addr=self.buf + msg.OFF + 12 + in_len)\n\n        self.pending.append(state)\n\n        out_data = self.dcp.mgr.handle_cb(state)\n        self.dcp.asc.iface.writemem(self.dcp.shmem + state.out_addr, out_data)\n        self.dcp.send(DCPEp_Msg(CTX=msg.CTX, ACK=1))\n\n        assert self.pending.pop() is state\n\n\nclass DCPEndpoint(ASCBaseEndpoint):\n    BASE_MESSAGE = DCPMessage\n    SHORT = \"dcpep\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.shmem = self.shmem_dva = None\n        self.init_complete = False\n        self.mgr = None\n\n        self.ch_cb = DCPCallbackChannel(self, \"CB\", 0x60000, 0x8000)\n        self.ch_cmd = DCPCallChannel(self, \"CMD\", 0, 0x8000)\n        self.ch_async = DCPCallbackChannel(self, \"ASYNC\", 0x40000, 0x20000)\n        self.ch_oobcb = DCPCallbackChannel(self, \"OOBCB\", 0x68000, 0x8000)\n        self.ch_oobcmd = DCPCallChannel(self, \"OOBCMD\", 0x8000, 0x8000)\n\n    @msg_handler(2, DCPEp_Msg)\n    def Rx(self, msg):\n        if msg.ACK:\n            if msg.CTX in (CallContext.CMD, CallContext.CB):\n                self.ch_cmd.ack()\n            elif msg.CTX in (CallContext.OOBCMD, CallContext.OOBCB):\n                self.ch_oobcmd.ack()\n            else:\n                raise Exception(f\"Unknown RX ack channel {msg.CTX}\")\n        else:\n            if msg.CTX == CallContext.CB:\n                self.ch_cb.cb(msg)\n            elif msg.CTX == CallContext.OOBCMD:\n                self.ch_oobcb.cb(msg)\n            elif msg.CTX == CallContext.ASYNC:\n                self.ch_async.cb(msg)\n            else:\n                raise Exception(f\"Unknown RX callback channel {msg.CTX}\")\n        return True\n\n    @msg_handler(1, DCPEp_InitComplete)\n    def InitComplete(self, msg):\n        self.log(\"init complete\")\n        self.init_complete = True\n        return True\n\n    def initialize(self):\n        self.shmem, self.shmem_dva = self.asc.ioalloc(0x100000)\n        self.asc.p.memset32(self.shmem, 0, 0x100000)\n        self.send(DCPEp_SetShmem(DVA=self.shmem_dva))\n        while not self.init_complete:\n            self.asc.work()\n\n","repo_name":"AsahiLinux/m1n1","sub_path":"proxyclient/m1n1/fw/dcp/dcpep.py","file_name":"dcpep.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","stars":3110,"dataset":"github-code","pt":"77"}
+{"seq_id":"41051626713","text":"from bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport smtplib, ssl\nimport time\nimport datetime\n\ndef sendMail():\n    port = 465  # For SSL\n    context = ssl.create_default_context()\n    sender_email=''\n    receiver_email=''\n    body=\"\"\n    subject=\"\"\n    message='To: {}\\r\\nSubject: {}\\r\\n\\r\\n{}'.format(receiver_email, subject, body)\n    with smtplib.SMTP_SSL(\"smtp.gmail.com\", port, context=context) as server:\n        server.login(\"\", \"\")\n        server.sendmail(sender_email, [receiver_email], message)\n\nwhile True:\n    print(datetime.datetime.now())\n    browser= webdriver.Chrome('chromedriver.exe') #chrome\n    browser.get('https://www2.hm.com/no_no/productpage.0749333001.html')#item page\n    html=browser.page_source\n    bs = BeautifulSoup(html, 'html.parser')\n    for item in bs.find_all():\n        if \"data-code\" in item.attrs:\n            if item.attrs['data-code']=='0749333001005': #locate the size identifier\n                if len(item.find_all(text='Utsolgt'))==0: #not out of stock\n                    sendMail()\n                    break\n    browser.close()\n    time.sleep(60)\n","repo_name":"Joshua-Schecker/in_stock_reminder","sub_path":"notifier.py","file_name":"notifier.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37605004986","text":"# first snippet. \n# a = {(1,2):1,(2,3):2}\n# print(a[1,2])\n\n# second snoppet..\n# a = {'a':1,'b':2,'c':3}\n# print (a['a'])\n\n# third snippet...\n# fruit = {}\n\n# def addone(index):\n#     if index in fruit:\n#         fruit[index] += 1\n#     else:\n#         fruit[index] = 1\n\n# addone('Apple')\n# addone('Banana')\n# addone('apple')\n# addone('Apple')\n\n# print (len(fruit))\n# print(fruit) \n\n#fourth snippet...\n# Student = {}\n# Age = {}\n# Details = {}\n# Student['name'] = \"bikki\"\n# Age['student_age'] = 14\n# Details['Student'] = Student\n# Details['Age'] = Age\n\n# print (len(Details[\"Student\"]))\n\n# five snippet......\nmy_dict = {}\nmy_dict[(1,2,4)] = 8\nmy_dict[(4,2,1)] = 10\nmy_dict[(1,2)] = 12\n\nsum = 0\nfor k in my_dict:\n    sum += my_dict[k]\n\nprint (sum)\nprint(my_dict) \n\n\n","repo_name":"amritat123/dictionary-questions","sub_path":"snippet_code.py","file_name":"snippet_code.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21047882757","text":"# -*- coding: utf-8 -*-\n#\n# Decorators for Syncopy metafunctions and `computeFunction`s\n#\n\n# Builtin/3rd party package imports\nimport functools\nimport h5py\nimport inspect\nimport numpy as np\nimport dask.distributed as dd\n\n\nimport syncopy as spy\nfrom syncopy.shared.errors import (\n    SPYTypeError,\n    SPYValueError,\n    SPYError,\n    SPYWarning,\n    SPYInfo,\n)\nfrom syncopy.shared.tools import StructDict\nfrom syncopy.shared.metadata import h5_add_metadata, parse_cF_returns\n\n# Local imports\nfrom .dask_helpers import check_slurm_available, check_workers_available\nfrom .log import get_logger\n\n__all__ = []\n\n\ndef unwrap_cfg(func):\n    \"\"\"\n    Decorator that unwraps `cfg` \"structure\" in metafunction call\n\n    Parameters\n    ----------\n    func : callable\n        Typically a Syncopy metafunction such as :func:`~syncopy.freqanalysis`\n\n    Returns\n    -------\n    wrapper_cfg : callable\n        Wrapped function; `wrapper_cfg` extracts keyword arguments from a possibly\n        provided `cfg` option \"structure\" based on the following logic:\n\n        1. Probe positional argument list of `func` for regular Python dict or\n           :class:`~syncopy.StructDict`. *Every hit* is assumed to be a `cfg` option\n           \"structure\" and removed from the list. Raises a\n           :class:`~syncopy.shared.errors.SPYValueError` if (a) more than one\n           dict (or :class:`~syncopy.StructDict`) is found in provided positional\n           arguments (b) keywords are provided in addition to `cfg` (c) `cfg` is\n           provided as positional as well as keyword argument.\n        2. If no `cfg` is found in positional arguments, check `func`'s keyword\n           arguments for a provided `cfg` entry. Raises a\n           :class:`~syncopy.shared.errors.SPYValueError` if `cfg` was provided\n           as positional argument as well as keyword.\n           A :class:`~syncopy.shared.errors.SPYTypeError` if `cfg` keyword\n           entry is not a Python dict or :class:`~syncopy.StructDict`.\n        3. If `cfg` was found either in positional or keyword arguments, then\n           (a) process its \"linguistic\" boolean keys (convert any \"yes\"/\"no\" entries\n           to `True` /`False`) and then (b) extract any existing \"data\"/\"dataset\"\n           entry/entries. Raises a :class:`~syncopy.shared.errors.SPYValueError`\n           if `cfg` contains both a \"data\" and \"dataset\" entry.\n        4. Perform the actual unwrapping: at this point, a provided `cfg` only\n           contains keyword arguments of `func`. If the (first) input object `data`\n           was provided as `cfg` entry, it already exists in the local namespace.\n           If not, then by convention, `data` is the first element of the\n           (remaining) positional argument list. Thus, the metafunction can now\n           be called via ``func(data, *args, **kwargs)``.\n        5. Amend the docstring of `func`: add a one-liner mentioning the possibility\n           of using `cfg` when calling `func` to the header of its docstring.\n           Append a paragraph to the docstrings' \"Notes\" section illustrating\n           how to call `func` with a `cfg` option \"structure\" that specifically\n           uses `func` and its input parameters. Note: both amendments are only\n           inserted in `func`'s docstring if the respective sections already exist.\n\n    Notes\n    -----\n    This decorator is primarily intended as bookkeeper for Syncopy metafunctions.\n    It permits \"FieldTrip-style\" calls of Syncopy metafunctions by \"Pythonizing\"\n    (processing and subsequent unpacking) of dict-like `cfg` objects. This\n    standardization allows all other Syncopy decorators (refer to See also section)\n    to safely use standard Python ``*args`` and ``**kwargs`` input arguments.\n\n    Supported call signatures:\n\n    * ``func(cfg, data)``: `cfg` exclusively contains keyword arguments of `func`,\n      `data` is a Syncopy data object.\n    * ``func(data, cfg)``: same as above\n    * ``func(data, cfg=cfg)``: same as above, but `cfg` itself is provided as\n      keyword argument\n    * ``func(cfg)``: `cfg` contains a field `data` or `dataset` (not both!)\n      holding one or more Syncopy data objects used as input of `func`\n    * ``func(cfg=cfg)``: same as above with `cfg` being provided as keyword\n    * ``func(data, kw1=val1, kw2=val2)``: standard Python call style with keywords\n      being provided explicitly\n    * ``func(data, cfg, kw2=val2)``: valid if `cfg` does NOT contain `'kw2'`\n\n\n    Invalid call signatures:\n\n    * ``func(data, cfg, cfg=cfg)``: `cfg` must not be provided as positional and\n      keyword argument\n    * ``func(cfg, {})``: every dict in `func`'s positional argument list is interpreted\n      as `cfg` \"structure\"\n    * ``func(data, cfg=value)``: `cfg` must be a Python dict or :class:`~syncopy.StructDict`\n    * ``func(data, cfg, kw1=val1)``: invalid if keyword `'kw1'` also appears in `cfg`\n\n    See also\n    --------\n    unwrap_select : extract `select` keyword and process in-place data-selections\n    process_io : set up\n                :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.computeFunction`-calls\n                based on parallel processing setup\n    detect_parallel_client : controls parallel processing engine via `parallel` keyword\n    _append_docstring : local helper for manipulating docstrings\n    _append_signature : local helper for manipulating function signatures\n    \"\"\"\n\n    # Perform a little introspection gymnastics to get the name of the first\n    # positional and keyword argument of `func` (if we only find anonymous `**kwargs`,\n    # come up with an exemplary keyword - `kwarg0` is only used in the generated docstring)\n    funcParams = inspect.signature(func).parameters\n    paramList = list(funcParams)\n    kwargList = [pName for pName, pVal in funcParams.items() if pVal.default != pVal.empty]\n    arg0 = paramList[0]\n    if len(kwargList) > 0:\n        kwarg0 = kwargList[0]\n    else:\n        kwarg0 = \"some_parameter\"\n\n    @functools.wraps(func)\n    def wrapper_cfg(*args, **kwargs):\n\n        # First, parse positional arguments for dict-type inputs (`k` counts the\n        # no. of dicts provided) and convert tuple of positional args to list\n        cfg = None\n        k = 0\n        args = list(args)\n        for argidx, arg in enumerate(args):\n            if isinstance(arg, dict):\n                cfgidx = argidx\n                k += 1\n\n        # If a dict was found, assume it's a `cfg` dict and extract it from\n        # the positional argument list; if more than one dict was found, abort\n        if k == 1:\n            cfg = args.pop(cfgidx)\n        elif k > 1:\n            raise SPYValueError(\n                legal=\"single `cfg` input\",\n                varname=\"cfg\",\n                actual=\"{0:d} `cfg` objects in input arguments\".format(k),\n            )\n\n        # Now parse provided keywords for `cfg` entry - if `cfg` was already\n        # provided as positional argument, abort\n        if kwargs.get(\"cfg\") is not None:\n            if cfg:\n                lgl = \"`cfg` either as positional or keyword argument, not both\"\n                raise SPYValueError(legal=lgl, varname=\"cfg\")\n            cfg = kwargs.pop(\"cfg\")\n\n        # If `cfg` was detected either in positional or keyword arguments, process it\n        if cfg:\n\n            # If `cfg` is not dict-like, abort (`StructDict` is a `dict` child)\n            if not isinstance(cfg, dict):\n                raise SPYTypeError(cfg, varname=\"cfg\", expected=\"dictionary-like\")\n\n            # check if we have saved pre-sets (replay a frontend run via out.cfg)\n            if func.__name__ in cfg.keys():\n                cfg = StructDict(cfg[func.__name__])\n\n            # IMPORTANT: create a copy of `cfg` using `StructDict` constructor to\n            # not manipulate `cfg` in user's namespace!\n            cfg = StructDict(cfg)\n\n            # If a meta-function is called using `cfg`, any (not only non-default) values for\n            # keyword arguments must *either* be provided via `cfg` or via standard kw\n            # NOTE: the frontend defaults not set by the user do NOT appear in `kwargs`!\n            for key in kwargs:\n                # these get special treatment below\n                if key in [\"data\", \"dataset\"]:\n                    continue\n                elif key in cfg:\n                    lgl = f\"parameter set either via `cfg.{key}=...` or directly via keyword\"\n                    act = f\"parameter `{key}` set in both `cfg` and via explicit keyword\"\n                    raise SPYValueError(legal=lgl, varname=f\"cfg/{key}\", actual=act)\n                # now attach the explicit set keywords to `cfg`\n                # to be passed to the func call\n                else:\n                    cfg[key] = kwargs[key]\n\n            # Translate any existing \"yes\" and \"no\" fields to `True` and `False`\n            for key in cfg.keys():\n                if str(cfg[key]) == \"yes\":\n                    cfg[key] = True\n                elif str(cfg[key]) == \"no\":\n                    cfg[key] = False\n\n        # No explicit `cfg`: rename `kwargs` to `cfg` to consolidate processing below;\n        # IMPORTANT: this does *not* create a copy of `kwargs`, thus the `pop`-ing\n        # below actually manipulates `kwargs` as well - crucial for the `kwargs.get(\"data\")`\n        # error checking!\n        else:\n            cfg = kwargs\n\n        # If `cfg` contains keys 'data' or 'dataset' extract corresponding\n        # entry and make it a positional argument (abort if both 'data'\n        # and 'dataset' are present)\n        data = cfg.pop(\"data\", None)\n        if cfg.get(\"dataset\"):\n            if data:\n                lgl = \"either 'data' or 'dataset' in `cfg`/keywords, not both\"\n                raise SPYValueError(legal=lgl, varname=\"cfg\")\n            data = cfg.pop(\"dataset\")\n\n        # If `cfg` did not contain `data`, look into `kwargs`\n        if data is None:\n            data = kwargs.pop(\"data\", None)\n            if kwargs.get(\"dataset\"):\n                if data:\n                    lgl = \"either `data` or `dataset` keyword, not both\"\n                    raise SPYValueError(legal=lgl, varname=\"data/dataset\")\n                data = kwargs.pop(\"dataset\")\n\n        # If Syncopy data object(s) were provided convert single objects to one-element\n        # lists, ensure positional args do *not* contain add'l objects; ensure keyword\n        # args (besides `cfg`) do *not* contain add'l objects; ensure `data` exclusively\n        # contains Syncopy data objects. Finally, rename remaining positional arguments\n        if data:\n            if any([isinstance(arg, spy.datatype.base_data.BaseData) for arg in args]):\n                lgl = (\n                    \"Syncopy data object provided either via `cfg`/keyword or \"\n                    + \"positional arguments, not both\"\n                )\n                raise SPYValueError(legal=lgl, varname=\"cfg/data\")\n            if kwargs.get(\"data\") or kwargs.get(\"dataset\"):\n                lgl = \"Syncopy data object provided either via `cfg` or as \" + \"keyword argument, not both\"\n                raise SPYValueError(legal=lgl, varname=\"cfg.data\")\n            if not isinstance(data, spy.datatype.base_data.BaseData):\n                raise SPYError(\"`data` must be Syncopy data object!\")\n            posargs = args\n\n        # If `data` was not provided via `cfg` or as kw-arg, parse positional arguments\n        if data is None:\n            posargs = []\n            while args:\n                arg = args.pop(0)\n                if data is not None and isinstance(arg, spy.datatype.base_data.BaseData):\n                    lgl = \"only one Syncopy data object\"\n                    raise SPYValueError(lgl, varname=\"data\")\n                if isinstance(arg, spy.datatype.base_data.BaseData):\n                    data = arg\n                else:\n                    posargs.append(arg)\n\n        # if there was no Syncopy data found at this point,\n        # we call the wrapped function without it\n        if data is None:\n            return func(*posargs, **cfg)\n        else:\n            # Call function with unfolded `data` + modified positional/keyword args\n            return func(data, *posargs, **cfg)\n\n    # Append two-liner to docstring header mentioning the use of `cfg`\n    introEntry = (\n        \"    \\n\"\n        + \"    The parameters listed below can be provided as is or a via a `cfg`\\n\"\n        + \"    configuration 'structure', see Notes for details. \\n\"\n    )\n    wrapper_cfg.__doc__ = _append_docstring(wrapper_cfg, introEntry, insert_in=\"Header\", at_end=True)\n\n    # Append a paragraph explaining the use of `cfg` by an example that explicitly\n    # mentions `func`'s name and input parameters\n    notesEntry = (\n        \"    This function can be either called providing its input arguments directly\\n\"\n        + \"    or via a `cfg` configuration 'structure'. For instance, the following function\\n\"\n        + \"    calls are equivalent\\n\"\n        + \"    \\n\"\n        + \"    >>> spy.{fname:s}({arg0:s}, {kwarg0:s}=...)\\n\"\n        + \"    >>> cfg = spy.StructDict()\\n\"\n        + \"    >>> cfg.{kwarg0:s} = ...\\n\"\n        + \"    >>> spy.{fname:s}(cfg, {arg0:s})\\n\"\n        + \"    >>> cfg.{arg0:s} = {arg0:s}\\n\"\n        + \"    >>> spy.{fname:s}(cfg)\\n\"\n        + \"    \\n\"\n        + \"    Please refer to :doc:`/user/fieldtrip` for further details. \\n\\n\"\n    )\n    wrapper_cfg.__doc__ = _append_docstring(\n        wrapper_cfg,\n        notesEntry.format(fname=func.__name__, arg0=arg0, kwarg0=kwarg0),\n        insert_in=\"Notes\",\n        at_end=False,\n    )\n\n    return wrapper_cfg\n\n\ndef unwrap_select(func):\n    \"\"\"\n    Decorator for handling in-place data selections via `select` keyword\n\n    Parameters\n    ----------\n    func : callable\n        Typically a Syncopy metafunction such as :func:`~syncopy.freqanalysis`\n\n    Returns\n    -------\n    wrapper_select : callable\n        Wrapped function; `wrapper_select` extracts `select` from keywords\n        provided to `func` and uses it to set the `._selector` property of the\n        input object(s). After successfully calling `func` with the modified input,\n        `wrapper_select` modifies `func` itself:\n\n        1. The \"Parameters\" section in the docstring of `func` is amended by an\n           entry explaining the usage of `select` (that mostly points to\n           :func:`~syncopy.selectdata`). Note: `func`'s docstring is only extended\n           if it has a \"Parameters\" section.\n        2. If not already present, `select` is added as optional keyword (with\n           default value `None`) to the signature of `func`.\n\n    Notes\n    -----\n    This decorator assumes that `func` has already been processed by\n    :func:`~syncopy.shared.kwarg_decorators.unwrap_cfg` and hence expects\n    `func` to obey standard Python call signature ``func(*args, **kwargs)``.\n    In other words, :func:`~syncopy.shared.kwarg_decorators.unwrap_select` is\n    intended as \"inner\" decorator of metafunctions, for instance\n\n    .. code-block:: python\n\n        @unwrap_cfg\n        @unwrap_select\n        def somefunction(data, kw1=\"default\", kw2=None, **kwargs):\n        ...\n\n    **Important** The metafunction `func` **must** accept \"anonymous\" keywords\n    via a ``**kwargs`` dictionary. This requirement is due to the fact that\n    :func:`~syncopy.shared.kwarg_decorators.unwrap_cfg` cowardly refuses to change\n    the byte-code of `func`, that is, `select` is not actually added as a new\n    keyword to `func`, only the corresponding signature is manipulated.\n    Thus, if `func` does not support a `kwargs` parameter dictionary,\n    using this decorator will have *strange* consequences. Specifically, `select`\n    will show up in `func`'s signature but it won't be actually usable:\n\n    .. code-block:: python\n\n        @unwrap_cfg\n        @unwrap_select\n        def somefunction(data, kw1=\"default\", kw2=None):\n        ...\n\n        >>> help(somefunction)\n        somefunction(data, kw1=\"default\", kw2=None, select=None)\n        ...\n        >>> somefunction(data, select=None)\n        TypeError: somefunction() got an unexpected keyword argument 'select'\n\n\n    See also\n    --------\n    unwrap_cfg : Decorator for processing `cfg` \"structs\"\n    detect_parallel_client : controls parallel processing engine via `parallel` keyword\n    \"\"\"\n\n    @functools.wraps(func)\n    def wrapper_select(*args, **kwargs):\n\n        # Either extract `select` from input kws and cycle through positional\n        # argument to apply in-place selection to the Syncopy object, or raise\n        # an error if a selection is already present and `select` is not None\n        select = kwargs.get(\"select\", None)\n        attached_selection = False\n        for obj in args:\n            # this hits all Syncopy data objects\n            if hasattr(obj, \"selection\"):\n                if obj.selection is None and select is not None:\n                    obj.selection = select\n                    attached_selection = True\n                    # we have one and only one input data object\n                    break\n                else:\n                    if select is not None:\n                        raise SPYError(\n                            f\"Selection found both in kwarg 'selection' ({select}) and in \\npassed Syncopy Data object of type '{type(obj)}' ({obj.selection})\"\n                        )\n\n        # Call function with modified data object(s)\n        res = func(*args, **kwargs)\n\n        # Wipe data-selection slot to not alter user objects\n        # if the selection got attached by this wrapper here\n        for obj in args:\n            if hasattr(obj, \"selection\") and attached_selection:\n                obj.selection = None\n\n        return res\n\n    # Append `select` keyword entry to wrapped function's docstring and signature\n    selectDocEntry = (\n        \"    select : dict or :class:`~syncopy.shared.tools.StructDict` or str\\n\"\n        + \"        In-place selection of subset of input data for processing. Please refer\\n\"\n        + \"        to :func:`syncopy.selectdata` for further usage details.\"\n    )\n    wrapper_select.__doc__ = _append_docstring(func, selectDocEntry)\n    wrapper_select.__signature__ = _append_signature(func, \"select\")\n\n    return wrapper_select\n\n\ndef detect_parallel_client(func):\n    \"\"\"\n    Decorator for handling parallelization via `parallel` keyword/client detection\n\n    Any already initialized Dask cluster always takes precedence\n    with both `parallel=True` and `parallel=None`. This gets checked via `dd.get_client()`,\n    and hence if a Dask cluster was set up before, Syncopy (and also potentially ACME later) will just\n    pass-through this one to the compute classes.\n    In case no cluster is running, only a dedicated `parallel=True` will spawn either a new\n    Dask cluster down the road via ACME (if on a slurm cluster) or a new LocalCluster as a default fallback.\n    The LocalCluster gets closed again after the wrapped function exited.\n\n    If `parallel` is `None`:\n        First attempts to connect to a running dask parallel processing client. If successful,\n        `parallel` is set to `True` and updated in `func`'s keyword argument dict.\n        If no client is found `parallel` is set to `False`\n    If `parallel` is True and ACME is installed AND we are on a slurm cluster:\n        Do nothing and forward all the parallelization setup with `parallel=True`\n        to the CR and ultimately ACME\n    If `parallel` is True and ACME is NOT installed OR we ar NOT on a slurm cluster:\n        Fire up a standard dask LocalCluster and forward `parallel=True` to func\n\n    Parameters\n    ----------\n    func : callable\n        Typically a Syncopy metafunction such as :func:`~syncopy.freqanalysis`\n\n    Returns\n    -------\n    parallel_client_detector : callable\n        Wrapped function; `parallel_client_detector` attempts to extract `parallel`\n        from keywords provided to `func`.\n\n        After successfully calling `func` with the modified\n        input arguments, `parallel_client_detector` modifies `func` itself:\n\n        1. The \"Parameters\" section in the docstring of `func` is amended by an\n           entry explaining the usage of `parallel`. Note: `func`'s docstring is\n           only extended if it has a \"Parameters\" section.\n        2. If not already present, `parallel` is added as optional keyword (with\n           default value `None`) to the signature of `func`.\n\n    Notes\n    -----\n    This decorator assumes that `func` has already been processed by\n    :func:`~syncopy.shared.kwarg_decorators.unwrap_cfg` and hence expects\n    `func` to obey standard Python call signature ``func(*args, **kwargs)``.\n    In other words, :func:`~syncopy.shared.kwarg_decorators.detect_parallel_client`\n    is intended as \"inner\" decorator of, e.g.,  metafunctions. See Notes in\n    the docstring of :func:`~syncopy.shared.kwarg_decorators.unwrap_select` for\n    further details.\n\n    See also\n    --------\n    unwrap_select : extract `select` keyword and process in-place data-selections\n    unwrap_cfg : Decorator for processing `cfg` \"structs\"\n    \"\"\"\n\n    # timeout in seconds for dask worker allocation\n    dask_timeout = 600\n\n    @functools.wraps(func)\n    def parallel_client_detector(*args, **kwargs):\n\n        logger = get_logger()\n\n        # Extract `parallel` keyword: if `parallel` is `False`, nothing happens\n        parallel = kwargs.get(\"parallel\")\n        kill_spawn = False\n        has_slurm = check_slurm_available()\n\n        # warning only emitted if slurm available but no ACME or Dask client\n        slurm_msg = \"\"\n\n        # if acme is around, let it manage everything assuming we are on the ESI cluster\n        if spy.__acme__ and parallel is not False:\n            try:\n                client = dd.get_client()\n                parallel = True\n            except ValueError:\n                if parallel:\n                    msg = (\n                        f\"Could not find a running dask cluster!\\n\"\n                        \"Try `esi_cluster_setup` from ACME to set up a cluster on the ESI HPC\\n\"\n                        \"..computing sequentially\"\n                    )\n                    logger.important(msg)\n                parallel = False\n\n        # This effectively searches for a global dask cluster, and sets\n        # parallel=True if one was found. If no cluster was found, parallel is set to False,\n        # so no automatic spawning of a LocalCluster this needs explicit `parallel=True`.\n        elif parallel is None:\n            # w/o acme interface dask directly\n            try:\n                client = dd.get_client()\n                # wait for at least 1 worker\n                check_workers_available(client, timeout=dask_timeout, n_workers=1)\n                msg = f\"..attaching to running Dask client:\\n\\t{client}\"\n                logger.important(msg)\n                parallel = True\n            except ValueError:\n                parallel = False\n\n        # If parallel processing was requested but ACME is not installed\n        # and no other Dask cluster is running,\n        # initialize a local dask cluster as fallback for local machines\n        elif parallel is True:\n            # if already one cluster is reachable do nothing\n            try:\n                client = dd.get_client()\n                # wait for at least 1 worker\n                check_workers_available(client, timeout=dask_timeout, n_workers=1)\n                msg = f\"..attaching to running Dask client:\\n{client}\"\n                logger.important(msg)\n            except ValueError:\n                # we are on a HPC but ACME and/or Dask client are missing,\n                # LocalCluster still gets created\n                if has_slurm and not spy.__acme__:\n                    slurm_msg = (\n                        \"We are apparently on a slurm cluster but\\n\"\n                        \"Syncopy could not find a Dask client.\\n\"\n                        \"Syncopy does not provide an \"\n                        \"automatic Dask SLURMCluster on its own!\"\n                        \"\\nPlease consider configuring your own dask cluster \"\n                        \"via `dask_jobqueue.SLURMCluster()`\"\n                        \"\\n\\nCreating a LocalCluster as fallback..\"\n                    )\n                    SPYWarning(slurm_msg)\n\n                # -- spawn fallback local cluster --\n\n                cluster = dd.LocalCluster()\n                # attaches to local cluster residing in global namespace\n                dd.Client(cluster)\n                kill_spawn = True\n                msg = \"No running Dask cluster found, created a local instance:\\n\" f\"\\t{cluster.scheduler}\"\n                logger.important(msg)\n\n        # Add/update `parallel` to/in keyword args\n        kwargs[\"parallel\"] = parallel\n\n        results = func(*args, **kwargs)\n\n        # kill local cluster\n        if kill_spawn:\n            # disconnect\n            dd.get_client().close()\n            # and kill\n            cluster.close()\n        # print again in case it got drowned\n        if slurm_msg:\n            SPYWarning(slurm_msg)\n\n        return results\n\n    # Append `parallel` keyword entry to wrapped function's docstring and signature\n    parallelDocEntry = (\n        \"    parallel : None or bool\\n\"\n        + \"        If `None` (recommended), processing is automatically performed in \\n\"\n        + \"        parallel (i.e., concurrently across trials/channel-groups), provided \\n\"\n        + \"        a dask parallel processing client is running and available. \\n\"\n        + \"        Parallel processing can be manually disabled by setting `parallel` \\n\"\n        + \"        to `False`. If `parallel` is `True` but no parallel processing client\\n\"\n        + \"        is running, computing will be performed sequentially.\"\n    )\n    parallel_client_detector.__doc__ = _append_docstring(func, parallelDocEntry)\n    parallel_client_detector.__signature__ = _append_signature(func, \"parallel\")\n\n    return parallel_client_detector\n\n\ndef process_io(func):\n    \"\"\"\n    Decorator for handling parallel execution of a\n    :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.computeFunction`\n\n    Parameters\n    ----------\n    func : callable\n        A Syncopy :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.computeFunction`\n\n    Returns\n    -------\n    wrapper_io : callable\n        Wrapped function; `wrapper_io` changes the way it invokes the wrapped\n        `computeFunction` and processes its output based on the type of the\n        provided first positional argument `trl_dat`.\n\n        * `trl_dat` : dict\n          Wrapped `computeFunction` is executed concurrently; `trl_dat` was\n          assembled by\n          :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.compute_parallel`\n          and contains information for parallel workers (particularly, paths and\n          dataset indices of HDF5 files for reading source data and writing results).\n          Nothing is returned (the output of the wrapped `computeFunction` is\n          directly written to disk).\n        * `trl_dat` : :class:`numpy.ndarray` or :class:`~syncopy.datatype.base_data.FauxTrial` object\n          Wrapped `computeFunction` is executed sequentially (either during dry-\n          run phase or in purely sequential computations); `trl_dat` is directly\n          propagated to the wrapped `computeFunction` and its output is returned\n          (either a tuple or :class:`numpy.ndarray`, depending on the value of\n          `noCompute`, see\n          :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.computeFunction`\n          for details)\n\n    Notes\n    -----\n    Parallel execution supports two writing modes: concurrent storage of results\n    in multiple HDF5 files or sequential writing of array blocks in a single\n    output HDF5 file. In both situations, the output array returned by\n    :meth:`~syncopy.shared.computational_routine.ComputationalRoutine.computeFunction`\n    is immediately written to disk and **not** propagated back to the caller to\n    avoid inter-worker network communication.\n\n    In case of parallel writing, trial-channel blocks are stored in individual\n    HDF5 files (virtual sources) that are consolidated into a single\n    :class:`h5py.VirtualLayout` which is subsequently used to allocate a virtual\n    dataset inside a newly created HDF5 file (located in Syncopy's temporary\n    storage folder).\n\n    Conversely, in case of sequential writing, each resulting array is written\n    sequentially to an existing single output HDF5 file using  a distributed mutex\n    for access control to prevent write collisions.\n\n    See also\n    --------\n    unwrap_cfg : Decorator for processing `cfg` \"structs\"\n    \"\"\"\n\n    @functools.wraps(func)\n    def wrapper_io(trl_dat, *wrkargs, **kwargs):\n\n        # `trl_dat` is a NumPy array or `FauxTrial` object: execute the wrapped\n        # function and return its result\n        if not isinstance(trl_dat, (dict, tuple)):\n            # Adding the metadata is done in compute_sequential(), nothing to do here.\n            # Note that the return value of 'func' in the next line may be a tuple containing\n            # both the ndarray for 'data', and the 'details'.\n            return func(trl_dat, *wrkargs, **kwargs)\n\n        # compatibility to adhere to the inargs the CRs produces: ill-formatted tuples\n        # which mix dicts, lists and even slices\n        if isinstance(trl_dat, tuple):\n            wrkargs = trl_dat[1:]\n            trl_dat = trl_dat[0]\n\n        # The fun part: `trl_dat` is a dictionary holding components for parallelization\n        keeptrials = trl_dat[\"keeptrials\"]\n        infilename = trl_dat[\"infile\"]\n        indset = trl_dat[\"indset\"]\n        ingrid = trl_dat[\"ingrid\"]\n        inshape = trl_dat[\"inshape\"]\n        sigrid = trl_dat[\"sigrid\"]\n        fancy = trl_dat[\"fancy\"]\n        vdsdir = trl_dat[\"vdsdir\"]\n        outfilename = trl_dat[\"outfile\"]\n        outdset = trl_dat[\"outdset\"]\n        outgrid = trl_dat[\"outgrid\"]\n        outshape = trl_dat[\"outshape\"]\n        outdtype = trl_dat[\"dtype\"]\n        call_id = trl_dat[\"call_id\"]\n\n        # === STEP 1 === read data into memory\n        # Catch empty source-array selections; this workaround is not\n        # necessary for h5py version 2.10+ (see https://github.com/h5py/h5py/pull/1174)\n        if any([not sel for sel in ingrid]):\n            res, details = np.empty(outshape, dtype=outdtype), {}\n        else:\n            with h5py.File(infilename, mode=\"r\") as h5fin:\n                if fancy:\n                    arr = np.array(h5fin[indset][ingrid])[np.ix_(*sigrid)]\n                else:\n                    arr = np.array(h5fin[indset][ingrid])\n\n            # === STEP 2 === perform computation\n            # Ensure input array shape was not inflated by scalar selection\n            # tuple, e.g., ``e=np.ones((2,2)); e[0,:].shape = (2,)`` not ``(1,2)``\n            # (use an explicit `shape` assignment here to avoid copies)\n            arr.shape = inshape\n\n            # Now, actually call wrapped function\n            # Put new outputs here!\n            res, details = parse_cF_returns(func(arr, *wrkargs, **kwargs))\n            # User-supplied cFs may return a single numpy.ndarray, or a 2-tuple of type (ndarray, sdict) where\n            # 'ndarray' is a numpy.ndarray containing computation results to be stored in the Syncopy\n            # data type (like AnalogData),\n            #  and 'sdict' is a shallow dictionary containing meta data that will be temporarily\n            # attached to the hdf5 container(s)\n            # during the compute run, but removed/collected and returned as separate return values\n            # to the user in the frontend.\n\n            # In case scalar selections have been performed, explicitly assign\n            # desired output shape to re-create \"lost\" singleton dimensions\n            # (use an explicit `shape` assignment here to avoid copies)\n            res.shape = outshape\n\n        # === STEP 3 === write result to disk\n        # Write result to multiple stand-alone HDF files or use a mutex to write to a\n        # common single file (sequentially)\n        if vdsdir is not None:\n            with h5py.File(outfilename, \"w\") as h5fout:\n                h5fout.create_dataset(outdset, data=res)\n                h5_add_metadata(h5fout, details, unique_key_suffix=call_id)\n                h5fout.flush()\n        else:\n\n            # Create distributed lock (use unique name so it's synced across workers)\n            lock = dd.lock.Lock(name=\"sequential_write\")\n            # Either (continue to) compute average or write current chunk\n            lock.acquire()\n            with h5py.File(outfilename, \"r+\") as h5fout:\n                main_dset = h5fout[outdset]\n                if keeptrials:\n                    main_dset[outgrid] = res\n                else:\n                    main_dset[()] += res\n\n                h5_add_metadata(h5fout, details, unique_key_suffix=call_id)\n                h5fout.flush()\n            lock.release()\n\n        return None  # result has already been written to disk\n\n    return wrapper_io\n\n\ndef _append_docstring(func, supplement, insert_in=\"Parameters\", at_end=True):\n    \"\"\"\n    Local helper to automate text insertions in docstrings\n\n    Parameters\n    ----------\n    func : callable\n        Typically a (wrapped) Syncopy metafunction such as :func:`~syncopy.freqanalysis`\n    supplement : str\n        Text entry to be added to `func`'s docstring. Has to be already formatted\n        correctly for its intended destination section, specifically respecting\n        indentation and line-breaks (e.g., following double-indentation of variable\n        descriptions in the \"Parameters\" section)\n    insert_in : str\n        Name of section `supplement` should be inserted into. Available options\n        are `\"Header\"` (part of the docstring before \"Parameters\"), `\"Parameters\"`,\n        `\"Returns\"`, `\"Notes\"` and `\"See also\"`. Note that the section specified\n        by `insert_in` has to already exist in `func`'s docstring, otherwise\n        `supplement` is *not* inserted.\n    at_end : bool\n        If `True`, `supplement` is appended at the end of the section specified\n        by `insert_in`. If `False`, `supplement` is included at the beginning of\n        the respective section.\n\n    Returns\n    -------\n    newDocString : str\n        A copy of `func`'s docstring with `supplement` inserted at the location\n        specified by `insert_in` and `at_end`.\n\n    Notes\n    -----\n    This routine is a local auxiliary method that is purely intended for internal\n    use. Thus, no error checking is performed.\n\n    See also\n    --------\n    _append_signature : extend a function's signature\n    \"\"\"\n\n    if func.__doc__ is None:\n        return\n\n    # these are the 4 whitespaces right in front of every doc string line\n    space4 = \"    \"\n\n    # \"Header\" insertions always work (an empty docstring is enough to do this).\n    # Otherwise ensure the provided `insert_in` section already exists, i.e.,\n    # partitioned `sectionHeading` == queried `sectionTitle`\n    if insert_in == \"Header\":\n        sectionText, sectionDivider, rest = func.__doc__.partition(\"Parameters\\n\")\n        textBefore = \"\"\n        sectionHeading = \"\"\n    else:\n        sectionTitle = insert_in + \"\\n\"\n        textBefore, sectionHeading, textAfter = func.__doc__.partition(sectionTitle)\n        if sectionHeading != sectionTitle:  # `insert_in` was not found in docstring\n            return func.__doc__\n        sectionText, sectionDivider, rest = textAfter.partition(\"\\n\\n\")\n    sectionTextList = sectionText.splitlines(keepends=True)\n\n    if at_end:\n        insertAtLine = -1\n        while sectionTextList[insertAtLine].isspace():\n            insertAtLine -= 1\n        insertAtLine = min(-1, insertAtLine + 1)\n\n        # to avoid clipping the last line of a parameter description\n        if sectionTextList[-1] != space4:\n            sectionTextList.append(\"\\n\")\n            sectionTextList.append(space4)\n    else:\n        # this is the 1st line break or the '    --------'\n        insertAtLine = 1\n\n    sectionText = \"\".join(sectionTextList[:insertAtLine])\n    sectionText += supplement\n    sectionText += \"\".join(sectionTextList[insertAtLine:])\n\n    newDocString = textBefore + sectionHeading + sectionText + sectionDivider + rest\n\n    return newDocString\n\n\ndef _append_signature(func, kwname, kwdefault=None):\n    \"\"\"\n    Local helper to automate keyword argument insertions in function signatures\n\n    Parameters\n    ----------\n    func : callable\n        Typically a (wrapped) Syncopy metafunction such as :func:`~syncopy.freqanalysis`\n    kwname : str\n        Name of keyword argument to be added to `func`'s signature\n    kwdefault : None or any valid type\n        Default value of keyword argument specified by `kwname`\n\n    Returns\n    -------\n    newSignature : inspect.Signature\n        A copy of `func`'s signature with ``kwname=kwdefault`` included as last\n        named keyword argument (before ``**kwargs``). If `kwname` already exists\n        in `func`'s named keyword arguments, `newSignature` is an identical copy\n        of `func`'s signature.\n\n    Notes\n    -----\n    This function **does not** change `func`'s byte-code, that is, it does not\n    actually add a new keyword argument to `func` but just appends a named parameter\n    to `func`'s signature. As a consequence, `func` **must** accept \"anonymous\"\n    keywords via a ``**kwargs`` dictionary for this manipulation to work as\n    intended. If `func` does not support a ``kwargs`` parameter dictionary,\n    `kwname` with default value `kwdefault` will be listed in `func`'s signature\n    but trying to use it will trigger a \"unexpected keyword argument\"-`TypeError`.\n\n    This routine is a local auxiliary method that is purely intended for internal\n    use. Thus, no error checking is performed.\n\n    See also\n    --------\n    _append_docstring : extend a function's signature\n    \"\"\"\n\n    funcSignature = inspect.signature(func)\n    if kwname in list(funcSignature.parameters):\n        newSignature = funcSignature\n    else:\n        paramList = list(funcSignature.parameters.values())\n        keyword = inspect.Parameter(kwname, inspect.Parameter.POSITIONAL_OR_KEYWORD, default=kwdefault)\n        if paramList[-1].name == \"kwargs\":\n            paramList.insert(-1, keyword)\n        else:\n            paramList.append(keyword)\n        newSignature = inspect.Signature(parameters=paramList)\n    return newSignature\n","repo_name":"esi-neuroscience/syncopy","sub_path":"syncopy/shared/kwarg_decorators.py","file_name":"kwarg_decorators.py","file_ext":"py","file_size_in_byte":38449,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"77"}
+{"seq_id":"8218543887","text":"import xlrd, xlwt\r\nfrom openpyxl import load_workbook\r\nimport re\r\n# убрать символы \\xa0 и \\n (['8:00', 'ИСТОРИЯ\\xa0(пр)\\nпроф.\\xa0Дианов\\xa0С.А. 413\\xa0к.А\\xa0(ЭТФ)'])\r\ndef xlsx_get_content(token = 'r.xlsx'):\r\n  wb = load_workbook(token)\r\n  sheet = wb['Лист1']\r\n  row = 4\r\n  i = row - 1\r\n  table = []\r\n\r\n  while row < 76:\r\n    checkdata2 = sheet.cell(row, column=2).value\r\n    checkdata3 = sheet.cell(row, column=3).value\r\n    if checkdata3 != None: \r\n      temp = []\r\n      if checkdata2 != None: \r\n        temp.append(checkdata2.replace(\"\\n\", \" \").replace(\"\\xa0\", \" \").strip())\r\n        temp.append(checkdata3.replace(\"\\n\", \" \").replace(\"\\xa0\", \" \").strip())\r\n        table.append(temp)\r\n      else:\r\n        temp.append(sheet.cell(i, column=2).value.replace(\"\\n\", \" \").replace(\"\\xa0\", \" \").strip())\r\n        temp.append(checkdata3.replace(\"\\n\", \" \").replace(\"\\xa0\", \" \").strip())\r\n        table.append(temp)\r\n    row += 1\r\n    i = row - 1\r\n  #print(table)#test\r\n  return table\r\n\r\nif __name__ == '__main__':\r\n  print(xlsx_get_content('r.xlsx'))","repo_name":"NotikSol/searching_and_processing-","sub_path":"xlsx_get_content.py","file_name":"xlsx_get_content.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5646559515","text":"from fastapi import FastAPI, HTTPException\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom starlette.requests import Request\nfrom starlette.responses import JSONResponse\n\nfrom app.routes.user_route import user_router\nfrom app.database import verify_connection\n\napp = FastAPI()\norigins = [\n    \"http://localhost:8080\",\n]\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\nif verify_connection():\n    print(\"Database connection successful 🚀\")\n    app.include_router(user_router)\nelse:\n    print(\"Database connection failed ❌\")\n    raise HTTPException(status_code=500, detail=\"Database connection failed ❌\")\n","repo_name":"42-bosch/portfolio-squeeze-second-project","sub_path":"backend/api/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7290326990","text":"import os\n#!/usr/bin/env python\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email import encoders\n\n# set for gmail\nsmtp_server = \"smtp.gmail.com\"\n\ndef eml_SendEmail(fromAddr, toAddr, email_pwd, subject, body, attachFile=None):\n\n    msg = MIMEMultipart()\n    msg[\"From\"] = fromAddr\n    msg[\"To\"] = toAddr\n    msg[\"Subject\"] = subject\n    msg.attach(MIMEText(body, \"plain\"))\n    \n    if attachFile != None:\n        attachment = open(attachFile, 'rb')\n    \n        part = MIMEBase(\"application\", \"octet-stream\")\n        part.set_payload((attachment).read())\n        encoders.encode_base64(part)\n        part.add_header('Content-Disposition', 'attachment', filename=os.path.basename(attachFile))\n        msg.attach(part)\n        \n        server = smtplib.SMTP(smtp_server, 587)\n        server.starttls()\n        server.login(fromAddr, email_pwd)\n        text = msg.as_string()\n        \n        server.sendmail(fromAddr, toAddr, text)\n        server.quit()\n","repo_name":"walkken/SeeTalker","sub_path":"eml_Email.py","file_name":"eml_Email.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"45595346015","text":"K=int(input())\nn=int(input())\n\ndp=[0,set(),set(),set(),set(),set(),set(),set(),set()]\n\ndp[1].add(K)\ndp[2].add(11*K)\ndp[3].add(111*K)\ndp[4].add(1111*K)\ndp[5].add(11111*K)\ndp[6].add(111111*K)\ndp[7].add(1111111*K)\ndp[8].add(11111111*K)\n\nfor i in range(1,9):\n    for j in range(1,i+1):\n        kind=i+j\n        if kind>8:\n            continue\n        \n        for num1 in dp[i]:\n            for num2 in dp[j]:\n                dp[kind].add(num1+num2)\n                if num1!=num2:\n                    dp[kind].add(abs(num1-num2))\n                dp[kind].add(num1*num2)\n\n                if num1>num2:\n                    dp[kind].add(num1//num2)\n                else:\n                    dp[kind].add(num2//num1)\n\n\nfor _ in range(n):\n    a=int(input())\n    res=-1\n\n    for i in range(1,9):\n        if a in dp[i]:\n            res=i\n            break\n    \n    if res==-1:\n        print('NO')\n    else:\n        print(res)","repo_name":"SeongjinLee00/Baekjoon","sub_path":"백준/Gold/2287. 모노디지털 표현/모노디지털 표현.py","file_name":"모노디지털 표현.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15221886254","text":"\nfrom siphon.cli.utils.siphon import config_required, login_required\nfrom siphon.cli.wrappers import Auth, Config, Siphon\n\nfrom siphon.cli.utils.mixpanel import mixpanel_event, MIXPANEL_EVENT_LOGS\n\nfrom clint.textui import colored, puts\n\nimport websocket\n\ndef print_usage():\n    print('Usage: siphon logs [--help]\\n\\nStream the console logs from your ' \\\n        'app. It must be running in the simulator, developer device or in ' \\\n        'the Siphon Sandbox.')\n\n@login_required\n@config_required\ndef stream_logs():\n    conf = Config()\n    # Request the correct streamer URL from siphon-web\n    auth = Auth()\n    siphon = Siphon(auth.auth_token)\n\n    # Track\n    mixpanel_event(MIXPANEL_EVENT_LOGS, properties={'app_id': conf.app_id})\n\n    streamer_url = siphon.get_streamer_url(conf.app_id, 'log_reader')\n\n    puts(colored.yellow('Connecting...'))\n    ws = websocket.create_connection(streamer_url)\n    puts(colored.green('Streaming logs and errors... (ctrl-c to stop)\\n'))\n    try:\n        for line in ws:\n            print(line)\n    except KeyboardInterrupt:\n        puts(colored.yellow('\\nClosing the connection.'))\n        ws.close()\n\ndef run(args):\n    if args is None:\n        args = []\n    if '--help' in args or len(args) > 0:\n        print_usage()\n    else:\n        stream_logs()\n","repo_name":"siphoncode/siphon-cli","sub_path":"siphon/cli/commands/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14942037443","text":"import time\nfrom pimoroni import Button\nfrom picographics import PicoGraphics, DISPLAY_PICO_EXPLORER, PEN_P4\nimport dht\nimport machine\n\nsensor = dht.DHT11(machine.Pin(4))\npir = machine.Pin(1, machine.Pin.IN, machine.Pin.PULL_UP)\nled_onboard = machine.Pin(25, machine.Pin.OUT)\n# We're only using a few colours so we can use a 4 bit/16 colour palette and save RAM!\ndisplay = PicoGraphics(display=DISPLAY_PICO_EXPLORER, pen_type=PEN_P4)\n\nbutton_a = Button(12)\nbutton_b = Button(13)\nbutton_x = Button(14)\nbutton_y = Button(15)\n\n\n\n\nWHITE = display.create_pen(255, 255, 255)\nBLACK = display.create_pen(0, 0, 0)\nCYAN = display.create_pen(0, 255, 255)\nMAGENTA = display.create_pen(255, 0, 255)\nYELLOW = display.create_pen(255, 255, 0)\nGREEN = display.create_pen(0, 255, 0)\n\n\n# sets up a handy function we can call to clear the screen\ndef clear():\n    display.set_pen(BLACK)\n    display.clear()\n    display.update()\n   \ndef flash_led():\n    led_onboard.value(1)\n    time.sleep(1) #this value is how long the LED is on.\n    led_onboard.value(0)\n    #utime.sleep(1) #this value is how long the LED is off.\n\n\ndef dht_sensor():\n    for i in range(10):\n        update_numbers()\n        sensor.measure()\n        temp = sensor.temperature()\n        temp_imperial = (int(temp) * (9/5)) + 32\n        temp_imperial = round(int(temp_imperial),2)\n        humidity = sensor.humidity()\n        display.set_pen(MAGENTA)\n        display.text(f\"Temperature:\\n\\n{temp} °C\",30,40, scale=3)\n        display.text(f\"{temp_imperial} °F\",120,67,scale=3)\n        display.set_pen(CYAN)\n        display.text(f\"Humidity:\\n\\n{humidity} %\",30,140, scale=3)\n        display.update()\n        time.sleep(.5)\n    \n    \ndef update_numbers():\n    display.set_pen(BLACK)\n    display.rectangle(30,66,180,30)\n    display.rectangle(30,164,60,30)\n    \n# set up\nclear()\ndisplay.set_font(\"bitmap8\")\n#display.set_font(\"sans\")\n\nled_external = machine.Pin(5, machine.Pin.OUT)\n\nwhile True:\n    if pir.value() == 1:\n        dht_sensor()\n        time.sleep(1)\n    else:\n        clear()\n","repo_name":"tcolinbright/Pico_Explorer","sub_path":"Temp_Humidity_Explorer/displaytempandhumidityPIR.py","file_name":"displaytempandhumidityPIR.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74621779448","text":"import os\r\nimport sys\r\nimport shutil\r\nimport traceback\r\nimport logging\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport pickle as pkl\r\n\r\nimport contextlib\r\nimport base64\r\n\r\n\r\nfrom yattag import Doc\r\nfrom yattag import indent\r\n\r\nimport cv2\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n##################################################\r\n\r\nclass Plotter(object):\r\n\t'''\r\n\t\tclass Plotter\r\n\t\t将训练过程中的数值化输出保存成html以及csv文件进行记录\r\n\r\n\t\t用法：\r\n\t\t1. 初始化：\r\n\t\t\tplotter = Plotter()\r\n\r\n\t\t2. 记录：\r\n\t\t\tplotter.scalar('loss1', step=10, value=0.1)\r\n\r\n\t\t3. 输出到html文件：\r\n\t\t\tplotter.to_html_report('./experiment/plt')\r\n\t\t\r\n\t\t4. 输出所有数据分别到单独的csv文件中：\r\n\t\t\tplotter.to_csv('./experiment/plt')\r\n\t'''\r\n\r\n\tdef __init__(self, args=None):\r\n\t\tself._scalar_data_frame_dict = {}\r\n\t\t\t\r\n\t\tself._out_svg_list = []\r\n\t\tself._upper_bound = {}\r\n\t\tself._lower_bound = {}\r\n\t\tself.args = args\r\n\r\n\tdef _check_dir(self, path):\r\n\t\tp = os.path.dirname(path)\r\n\t\tif not os.path.exists(p):\r\n\t\t\tos.mkdir(p)\r\n\t\treturn p\r\n\r\n\tdef set_min_max(self, name, min_val=None, max_val=None):\r\n\t\tif min_val is not None:\r\n\t\t\tself._lower_bound[name] = min_val\r\n\t\tif max_val is not None:\r\n\t\t\tself._upper_bound[name] = max_val\r\n\r\n\t@property\r\n\tdef scalar_names(self):\r\n\t\treturn list(self._scalar_data_frame_dict.keys())\r\n\r\n\tdef get(self, name):\r\n\t\tif name in self._scalar_data_frame_dict:\r\n\t\t\treturn self._scalar_data_frame_dict[name]\r\n\t\telse:\r\n\t\t\treturn None\r\n\r\n\tdef has(self, name):\r\n\t\treturn name in self._scalar_data_frame_dict\r\n\r\n\tdef keys(self):\r\n\t\treturn list(self._scalar_data_frame_dict.keys())\r\n\r\n\r\n\tdef scalar(self, name, step, value, epoch=None):\r\n\t\tif isinstance(value, dict):\r\n\t\t\tdata = value.copy()\r\n\t\t\tdata.update({\r\n\t\t\t\t'step' : step\r\n\t\t\t})\r\n\t\telse:\r\n\t\t\tdata = {\r\n\t\t\t\t'step' : step,\r\n\t\t\t\tname : value,\r\n\t\t\t}\r\n\t\t\r\n\t\tif epoch is not None:\r\n\t\t\tdata['epoch'] = epoch\r\n\t\t\t\t\r\n\t\tdf = pd.DataFrame(data, index=[0])\r\n\r\n\t\tif name not in self._scalar_data_frame_dict:\r\n\t\t\tself._scalar_data_frame_dict[name] = df\r\n\t\telse:\r\n\t\t\tself._scalar_data_frame_dict[name] = self._scalar_data_frame_dict[name].append(df, ignore_index=True)\r\n\r\n\r\n\tdef to_csv(self, output_dir):\r\n\t\t\" 将记录保存到多个csv文件里面，csv文件放在output_dir下面。\"\r\n\t\tif not os.path.exists(output_dir):\r\n\t\t\tos.mkdir(output_dir)\r\n\r\n\t\tfor name, data_frame in self._scalar_data_frame_dict.items():\r\n\t\t\tcsv_filepath = os.path.join(output_dir, 'scalar_'+name+'.csv')\r\n\t\t\tdata_frame.to_csv(csv_filepath, index=False)\r\n\r\n\r\n\tdef from_csv(self, output_dir):\r\n\t\t\" 从output_dir下面的csv文件里面读取并恢复记录 \"\r\n\t\tcsv_name_list = [fn.split('.')[0] for fn in os.listdir(output_dir) if fn.endswith('csv')]\r\n\t\tfor name in csv_name_list:\r\n\t\t\tif name.startswith('scalar_'):\r\n\t\t\t\tin_csv = pd.read_csv(os.path.join(output_dir, name+'.csv'))\r\n\t\t\t\tself._scalar_data_frame_dict[name[len('scalar_'):]] = in_csv\r\n\r\n\tdef write_svg_all(self, output_dir):\r\n\t\t\" 将所有记录绘制成svg图片 \"\r\n\t\tfor ind, (name, data_frame) in enumerate(self._scalar_data_frame_dict.items()):\r\n\t\t\ttry:\r\n\t\t\t\tmin_val = self._lower_bound.get(name, None)\r\n\t\t\t\tmax_val = self._upper_bound.get(name, None)\r\n\t\t\t\toutput_svg_filepath = os.path.join(output_dir, name+'.svg')\r\n\t\t\t\tplt.figure()\r\n\t\t\t\tplt.clf()\r\n\t\t\t\theaders = [hd for hd in data_frame.columns if hd not in ['step', 'epoch']]\r\n\t\t\t\tif len(headers) == 1:\r\n\t\t\t\t\tplt.plot(data_frame['step'], data_frame[name])\r\n\t\t\t\telse:\r\n\t\t\t\t\tfor hd in headers:\r\n\t\t\t\t\t\tplt.plot(data_frame['step'], data_frame[hd])\r\n\t\t\t\t\tplt.legend(headers)\r\n\t\t\t\tif min_val is not None:\r\n\t\t\t\t\tplt.ylim(bottom=min_val)\r\n\t\t\t\tif max_val is not None:\r\n\t\t\t\t\tplt.ylim(top=max_val)\r\n\r\n\t\t\t\tplt.grid(axis='y')\r\n\t\t\t\tplt.grid(axis='x', which='major')\r\n\t\t\t\tplt.grid(axis='x', which='minor', color=[0.9, 0.9, 0.9])\r\n\t\t\t\t\r\n\t\t\t\tplt.tight_layout()\r\n\t\t\t\tplt.savefig(output_svg_filepath)\r\n\t\t\t\tplt.close()\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tlogger.info('draw %s failed : '%name)\r\n\t\t\t\tlogger.info(data_frame)\r\n\t\t\t\ttraceback.print_exc()\r\n\r\n\r\n\tdef to_html_report(self, output_filepath):\r\n\t\t\" 将所有记录整理成一个html报告 \"\r\n\t\tself.write_svg_all(self._check_dir(output_filepath))\r\n\t\tdoc, tag, text = Doc().tagtext()\r\n\t\twith open(output_filepath, 'w') as outfile:\r\n\t\t\twith tag('html'):\r\n\t\t\t\twith tag('body'):\r\n\r\n\t\t\t\t\ttitle_idx = 1\r\n\r\n\t\t\t\t\tif self.args is not None:\r\n\t\t\t\t\t\twith tag('h3'):\r\n\t\t\t\t\t\t\ttext('{}. args'.format(title_idx))\r\n\t\t\t\t\t\t\ttitle_idx += 1\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tkey_list = list(self.args.keys())\r\n\t\t\t\t\t\tkey_list = sorted(key_list)\r\n\r\n\t\t\t\t\t\twith tag('div', style='display:inline-block;width:850px;padding:5px;margin-left:20px'):\r\n\t\t\t\t\t\t\tfor key in key_list:\r\n\t\t\t\t\t\t\t\twith tag('div', style='display:inline-block;width:400px;'):\r\n\t\t\t\t\t\t\t\t\ttext('{} : {}\\n'.format(key, self.args[key]))\r\n\r\n\t\t\t\t\twith tag('h3'):\r\n\t\t\t\t\t\ttext('{}. scalars'.format(title_idx))\r\n\t\t\t\t\t\ttitle_idx += 1\r\n\r\n\t\t\t\t\tdata_frame_name_list = [n for n, d in self._scalar_data_frame_dict.items()]\r\n\t\t\t\t\tdata_frame_name_list = sorted(data_frame_name_list)\r\n\r\n\t\t\t\t\tfor ind, name in enumerate(data_frame_name_list):\r\n\t\t\t\t\t\twith tag('div', style='display:inline-block'):\r\n\t\t\t\t\t\t\twith tag('h4', style='margin-left:20px'):\r\n\t\t\t\t\t\t\t\ttext('(%d). '%(ind+1)+name)\r\n\t\t\t\t\t\t\tdoc.stag(\"embed\", style=\"width:800px;padding:5px;margin-left:20px\", src=name+'.svg', type=\"image/svg+xml\")\r\n\r\n\t\t\tresult = indent(doc.getvalue())\r\n\t\t\toutfile.write(result)\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef img_vertical_concat(images, pad=0, pad_value=255, pad_right=False):\r\n\tnb_images = len(images)\r\n\th_list = [i.shape[0] for i in images]\r\n\tw_list = [w.shape[1] for w in images]\r\n\tif pad_right:\r\n\t\tmax_w = np.max(w_list)\r\n\t\timages = [i if i.shape[1] == max_w else np.hstack([i, np.ones([i.shape[0], max_w-i.shape[1]]+list(i.shape[2:]), dtype=i.dtype)*pad_value])\r\n\t\t\t\tfor i in images]\r\n\telse:\r\n\t\tassert np.all(np.equal(w_list, w_list[0]))\r\n\r\n\tif pad != 0:\r\n\t\timages = [np.vstack([i, np.ones([pad,]+list(i.shape[1:]), dtype=i.dtype)*pad_value]) for i in images[:-1]] + [images[-1],]\r\n\r\n\tif not isinstance(images, list):\r\n\t\timages = [i for i in images]\r\n\treturn np.vstack(images)\r\n\r\ndef img_horizontal_concat(images, pad=0, pad_value=255, pad_bottom=False):\r\n\tnb_images = len(images)\r\n\th_list = [i.shape[0] for i in images]\r\n\tw_list = [w.shape[1] for w in images]\r\n\tif pad_bottom:\r\n\t\tmax_h = np.max(h_list)\r\n\t\timages = [i if i.shape[0] == max_h else np.vstack([i, np.ones([max_h-i.shape[0]]+list(i.shape[1:]), dtype=i.dtype)*pad_value])\r\n\t\t\t\tfor i in images]\r\n\telse:\r\n\t\tassert np.all(np.equal(h_list, h_list[0]))\r\n\r\n\tif pad != 0:\r\n\t\timages = [np.hstack([i, np.ones([i.shape[0], pad,]+list(i.shape[2:]), dtype=i.dtype)*pad_value]) for i in images[:-1]] + [images[-1],]\r\n\r\n\tif not isinstance(images, list):\r\n\t\timages = [i for i in images]\r\n\treturn np.hstack(images)\r\n\t\r\n\r\ndef img_grid(images, nb_images_per_row=10, pad=0, pad_value=255):\r\n\tret = []\r\n\twhile len(images) >= nb_images_per_row:\r\n\t\tret.append(img_horizontal_concat(images[0:nb_images_per_row], pad_bottom=True, pad=pad, pad_value=pad_value))\r\n\t\timages = images[nb_images_per_row:]\r\n\tif len(images) != 0:\r\n\t\tret.append(img_horizontal_concat(images, pad_bottom=True, pad=pad, pad_value=pad_value))\r\n\treturn img_vertical_concat(ret, pad=pad, pad_right=True, pad_value=pad_value)\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tp = Plotter()\r\n\r\n\tp.scalar('loss', 1, 100)\r\n\tp.scalar('loss', 2, 100)\r\n\tp.scalar('loss', 3, 100)\r\n\tp.scalar('loss', 4, 100)\r\n\tp.scalar('loss', 5, 100)\r\n\tp.scalar('loss', 6, 100)\r\n\tp.scalar('loss', 7, 100)\r\n\tp.scalar('loss', 8, 100)\r\n\tp.scalar('loss', 9, 100)\r\n\tp.scalar('loss', 10, 100)\r\n\tp.scalar('loss', 11, 100)\r\n\tp.scalar('loss', 12, 100)\r\n\r\n\tp.scalar('loss2', 1, 100)\r\n\tp.scalar('loss2', 2, 100)\r\n\tp.scalar('loss2', 3, 100)\r\n\tp.scalar('loss2', 4, 100)\r\n\tp.scalar('loss2', 5, 100)\r\n\tp.scalar('loss2', 6, 100)\r\n\tp.scalar('loss2', 7, 100)\r\n\tp.scalar('loss2', 8, 100)\r\n\tp.scalar('loss2', 9, 100)\r\n\tp.scalar('loss2', 10, 100)\r\n\tp.scalar('loss2', 11, 100)\r\n\tp.scalar('loss2', 12, 100)\r\n\r\n\t# p.from_csv('./test_plot_output')\r\n\tp.to_html_report('./test_plot_output/output.html')\r\n\tp.to_csv('./test_plot_output')\r\n\r\n","repo_name":"yanzhicong/DeHiB","sub_path":"utils/vis.py","file_name":"vis.py","file_ext":"py","file_size_in_byte":8152,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40542068084","text":"import logging\nimport sqlite3\nimport sys\nimport time\n\ndef handle_unhandled_exceptions(exc_type, exc_value, exc_traceback):\n    if issubclass(exc_type, KeyboardInterrupt):\n        sys.__excepthook__(exc_type, exc_value, exc_traceback)\n        return\n    logging.critical('Unhandled exception', exc_info=(exc_type, exc_value, exc_traceback))\n    logging.info('END')\n\ndef main():\n    START_TIME = time.perf_counter()\n\n    now   = time.localtime()\n    year  = now.tm_year\n    month = now.tm_mon\n    day   = now.tm_mday\n\n    logging.basicConfig(filename=f'logs/{year}_{month}_{day}_create_db.log',\n                        level=logging.DEBUG,\n                        format='%(asctime)s - [%(levelname)s] %(message)s',\n                        datefmt='%Y/%m/%d %H:%M:%S')\n\n    sys.excepthook = handle_unhandled_exceptions\n\n    logging.info('START')\n    \n    DATABASE = 'access.db'\n\n    con = sqlite3.connect(DATABASE)\n    con.execute('PRAGMA foreign_keys = ON')\n\n    end_time = time.perf_counter()\n    elapsed_time = end_time - START_TIME\n    logging.info(f'Connection with database {DATABASE} established ({elapsed_time})')\n\n    cur = con.cursor()\n\n    drop_table_users = 'DROP TABLE IF EXISTS users'\n    drop_table_codes = 'DROP TABLE IF EXISTS codes'\n    drop_table_logs = 'DROP TABLE IF EXISTS logs'\n\n    create_table_users = \"\"\" CREATE TABLE IF NOT EXISTS users (\n            id   INTEGER PRIMARY KEY,\n            name TEXT    NOT NULL\n            )\"\"\"\n    create_table_codes = \"\"\" CREATE TABLE IF NOT EXISTS codes (\n            id      INTEGER PRIMARY KEY,\n            code    TEXT    UNIQUE NOT NULL,\n            user_id INTEGER NOT NULL,\n            FOREIGN KEY (user_id) REFERENCES users (id)\n                ON UPDATE CASCADE\n                ON DELETE CASCADE\n            )\"\"\"\n    create_table_logs = \"\"\" CREATE TABLE IF NOT EXISTS logs (\n            id INTEGER PRIMARY KEY,\n            user_id    INTEGER NOT NULL,\n            datetime   TEXT NOT NULL,\n            FOREIGN KEY (user_id) REFERENCES users (id)\n            )\"\"\"\n\n    cur.execute(drop_table_users)\n    cur.execute(drop_table_codes)\n    cur.execute(drop_table_logs)\n    con.commit()\n\n    end_time = time.perf_counter()\n    elapsed_time = end_time - START_TIME\n    logging.info(f'Tables dropped ({elapsed_time})')\n\n    cur.execute(create_table_users)\n    cur.execute(create_table_codes)\n    cur.execute(create_table_logs)\n    con.commit()\n\n    end_time = time.perf_counter()\n    elapsed_time = end_time - START_TIME\n    logging.info(f'Tabled created ({elapsed_time})')\n\n    end_time = time.perf_counter()\n    elapsed_time = end_time - START_TIME\n    logging.info(f'END ({elapsed_time})')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NicoCassio/siemb_project","sub_path":"create_db.py","file_name":"create_db.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15930886755","text":"\"\"\"\n    GRM package\n    try_os... decorators\n\"\"\"\n\n\nimport errno\nimport os\nfrom flask import current_app\nfrom time import sleep\nfrom werkzeug.exceptions import InternalServerError, HTTPException\n\n\ndef try_os_io(function):\n    \"\"\" Try OS io anything and try again if busy decorator\n\n    :param function: Function to decorate\n    :return: decorator\n    \"\"\"\n    def wrapper(*args):\n        \"\"\" Try os io wrapper \"\"\"\n\n        # get configs\n        attempts = current_app.config.get(\"IO_ATTEMPTS\", 10)\n        sleep_for = current_app.config.get(\"IO_SLEEP_FOR\", 0.2)\n\n        # try to get os io attempts\n        attempt = 0\n        while attempt < attempts:\n\n            attempt += 1\n            try:\n                # if no errors occurred, return from function\n                return function(*args)\n\n            except HTTPException as e:\n                raise e\n\n            except OSError as e:\n                # try again only if file busy\n                if e.errno not in [errno.ETXTBSY, errno.EBUSY]:\n                    raise InternalServerError(f\"OS ERROR (errno={e.errno}).\\n{os.strerror(e.errno) if e.errno else ''}\")\n                # wait some time\n                sleep(sleep_for)\n\n        # if attempts is accomplished, raise exception\n        raise InternalServerError(\"Exceeded the allowed number of server I/O attempts\")\n\n    return wrapper\n","repo_name":"golosovsa/sky.pro-cw-pd-3","sub_path":"grm/try_os.py","file_name":"try_os.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"70682485688","text":"import bz2\nimport csv\nimport json\nimport os\n\n\ndef open_file(file_path, output_folder):\n    \"\"\"\n    Opens and processes a BZIP2-compressed JSON file containing events data.\n\n    Args:\n        file_path (str): The path to the BZIP2-compressed JSON file.\n        output_folder (str): The folder where CSV files will be written.\n\n    Returns:\n        None\n    \"\"\"\n    distinct_types = set()\n    type_data = {}\n\n    with bz2.open(file_path, \"rt\") as f:\n        for line in f:\n            event = json.loads(line.strip())\n            event_type = event.get(\"type\", None)\n\n            if event_type:\n                distinct_types.add(event_type)\n\n                if event_type not in type_data:\n                    type_data[event_type] = []\n\n                type_data[event_type].append(event)\n\n    for event_type, data in type_data.items():\n        output_filename = os.path.join(output_folder, f\"{event_type}.csv\")\n\n        with open(output_filename, \"w\", newline=\"\") as output_file:\n            csv_writer = csv.DictWriter(output_file, fieldnames=data[0].keys())\n            csv_writer.writeheader()\n            csv_writer.writerows(data)\n\n\nfile_path = os.path.join(os.getcwd(), \"events.jsonl.bz2\")\noutput_folder = os.path.join(os.getcwd(), \"reimagined_chainsaw/seeds\")\nopen_file(file_path=file_path, output_folder=output_folder)\n","repo_name":"TitoLulu/reimagined-chainsaw","sub_path":"decompress_file.py","file_name":"decompress_file.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19857579278","text":"import os\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport distributions\nfrom pyro.infer.mcmc import MCMC\nfrom pyro.infer.mcmc.nuts import HMC\n\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir)\nfrom samplers.basic_hmc import BasicHMC\n\n\n'''\nPerform sampling experiments for built-in Pyro HMC \nand basic implementation of HMC.\nDistributions used for experiments are *not* conditioned\nwith data.\nThis generates graphs for basic comparison.\n\n*********************************************************\nUsage:\npython sampling_experiments.py builtin\npython sampling_experiments.py basic\n\nResult will be saved in figures/sampling_experiments\n'''\n\n# Define HMC kernel based on argument\nkernel = None\noption = sys.argv[1]\nif sys.argv[1] == \"basic\":\n    kernel = BasicHMC\nelif sys.argv[1] == \"builtin\":\n    kernel = HMC\n\n\n# Define HMC sampler\ndef hmc_sampling(dist):\n    hmc_kernel = kernel(dist, step_size=0.9, num_steps=4)\n    mcmc_sampler = MCMC(hmc_kernel,\n                        num_samples=1000,\n                        warmup_steps=50)\n    mcmc_sampler.run()\n    mcmc_sampler.summary()\n    posterior = mcmc_sampler.get_samples()\n    return posterior\n\n\n# Code for generating plots for various distributions\n\nBIN_SIZE = 100\n\n# Unimodal distributions\nfig, ax1 = plt.subplots(1, 2, figsize=(16, 4))\n\nunimodal_sym_posterior = hmc_sampling(distributions.unimodal_sym)\nax1[0].hist(unimodal_sym_posterior['sample'].numpy(), bins=BIN_SIZE)\nax1[0].set_title('Unimodal symmetric distribution with ' + option + ' HMC sampler')\n\nunimodal_asym_posterior = hmc_sampling(distributions.unimodal_asym)\nax1[1].hist(unimodal_asym_posterior['sample'].numpy(), bins=BIN_SIZE)\nax1[1].set_title('Unimodal asymmetric distribution with ' + option + ' HMC sampler')\nplt.savefig(\"figures/sampling_experiments/unimodal_\" + option + \".jpg\")\n\n# Bimodal distributions\nfig, ax2 = plt.subplots(1, 2, figsize=(16, 4))\n\nbimodal_symmetric_posterior = hmc_sampling(distributions.bimodal_sym)\nsample1, sample2, weight = bimodal_symmetric_posterior[\"sample1\"], bimodal_symmetric_posterior[\"sample2\"], \\\n                           bimodal_symmetric_posterior[\"weight\"]\nbimodal_symmetric_posterior = np.array([[sample1[i], sample2[i]][weight[i] < 0.5] for i in range(len(sample1))])\n\nax2[0].hist(bimodal_symmetric_posterior, bins=BIN_SIZE)\nax2[0].set_title('Bimodal symmetric distribution with ' + option + ' HMC sampler')\n\nbimodal_asymmetric_posterior = hmc_sampling(distributions.bimodal_asym)\nsample1, sample2, weight = bimodal_asymmetric_posterior[\"sample1\"], bimodal_asymmetric_posterior[\"sample2\"], \\\n                           bimodal_asymmetric_posterior[\"weight\"]\nbimodal_asymmetric_posterior = np.array([[sample1[i], sample2[i]][weight[i] < 0.5] for i in range(len(sample1))])\n\nax2[1].hist(bimodal_asymmetric_posterior, bins=BIN_SIZE)\nax2[1].set_title('Bimodal asymmetric distribution with ' + option + ' HMC sampler')\nplt.savefig(\"figures/sampling_experiments/bimodal_\" + option + \".jpg\")\n\n# Trimodal distributions\nfig, ax3 = plt.subplots(1, 2, figsize=(16, 4))\n\ntrimodal_symmetric_posterior = hmc_sampling(distributions.trimodal_sym)\nsample1, sample2, sample3, weight = trimodal_symmetric_posterior[\"sample1\"], trimodal_symmetric_posterior[\"sample2\"], \\\n                                    trimodal_symmetric_posterior[\"sample3\"], trimodal_symmetric_posterior[\"weight\"]\ntrimodal_symmetric_posterior = np.array(\n    [[sample1[i], sample2[i], sample3[i]][0 if weight[i] < 1 / 3 else (2 if weight[i] > 2 / 3 else 1)] for i in\n     range(len(sample1))])\n\nax3[0].hist(trimodal_symmetric_posterior, bins=BIN_SIZE)\nax3[0].set_title('Trimodal symmetric distribution with ' + option + ' HMC sampler')\n\ntrimodal_asymmetric_posterior = hmc_sampling(distributions.trimodal_asym)\nsample1, sample2, sample3, weight = trimodal_asymmetric_posterior[\"sample1\"], trimodal_asymmetric_posterior[\"sample2\"], \\\n                                    trimodal_asymmetric_posterior[\"sample3\"], trimodal_asymmetric_posterior[\"weight\"]\ntrimodal_asymmetric_posterior = np.array(\n    [[sample1[i], sample2[i], sample3[i]][0 if weight[i] < 1 / 3 else (2 if weight[i] > 2 / 3 else 1)] for i in\n     range(len(sample1))])\n\nax3[1].hist(trimodal_asymmetric_posterior, bins=BIN_SIZE)\nax3[1].set_title('Trimodal asymmetric distribution with ' + option + ' HMC sampler')\nplt.savefig(\"figures/sampling_experiments/trimodal_\" + option + \".jpg\")\n","repo_name":"ATMLGroup1-2021/SGHMC","sub_path":"experiments/sampling_experiments.py","file_name":"sampling_experiments.py","file_ext":"py","file_size_in_byte":4464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18765511979","text":"import tensorflow as tf\r\nimport os\r\nimport time\r\n\r\n\r\n# 禁用GPU\r\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\r\n\r\n# 使用GPU\r\nos.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'\r\ngpus = tf.config.experimental.list_physical_devices('GPU')\r\nif gpus:\r\n    try:\r\n        # Currently, memory growth needs to be the same across GPUs\r\n        for gpu in gpus:\r\n            tf.config.experimental.set_memory_growth(gpu, True)\r\n        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\r\n        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n    except RuntimeError as e:\r\n        # Memory growth must be set before GPUs have been initialized\r\n        print(e)\r\n\r\nprint('GPU is available ? ', tf.test.is_gpu_available())\r\n\r\n\r\n# 获取数据集\r\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\r\nx_train, x_test = x_train / 255.0, x_test / 255.0\r\n\r\n# Build model: fully-connected net\r\nmodel = tf.keras.models.Sequential(\r\n    [tf.keras.layers.Flatten(input_shape=(28, 28)),\r\n     tf.keras.layers.Dense(128, activation='relu'),\r\n     tf.keras.layers.Dropout(0.2),\r\n     tf.keras.layers.Dense(10, activation='softmax')])\r\n# Compile model\r\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\r\n\r\n# print(\"-----------------CPU----------------\")\r\n# t0 = time.time()\r\n# model.fit(x_train, y_train, epochs=50)\r\n# model.evaluate(x_test, y_test, verbose=2)\r\n# t1 = time.time()\r\n# print(\"cpu：\" + str(t1 - t0))\r\n\r\nprint(\"-----------------GPU1----------------\")\r\nt1 = time.time()\r\nmodel.fit(x_train, y_train, epochs=50)\r\nmodel.evaluate(x_test, y_test, verbose=2)\r\nt2 = time.time()\r\nprint(\"gpu1：\" + str(t2 - t1))\r\n\r\n#\r\nprint(\"-----------------GPU2----------------\")\r\nmodel.fit(x_train, y_train, epochs=50)\r\nmodel.evaluate(x_test, y_test, verbose=2)\r\nt3 = time.time()\r\nprint(\"gpu2：\" + str(t3 - t2))\r\n# cpu：79.47657585144043\r\n# gpu2：312.5358350276947\r\n# gpu2：308.8298239707947\r\n","repo_name":"AWJ0925/testGpuIsTrue","sub_path":"gpu_codes/gpu_tensorflow_test.py","file_name":"gpu_tensorflow_test.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10821403909","text":"\"\"\"\nProgram: phone_search.py\nAuthor: Jeffrey Cosgrove\nDate: 2/14/2020\n\nThis program allows a user to search a database for the first and last name and\nphone number of the entries listed in the phones.txt file. The program accepts a\nuser input, then converts that input into lower case so that the program can cross\nreference the text file and print out any line that contains the input by <first_name>\n<last_name>, <phone_number>. Finally the program checks to see wether or not the user\nwishes to restart and run another search or quit the program entirely.\n\nInput: The name or phone number that is to be searched, as well as wether or not to\nrestart the program.\n\nOutput: Any line in the text document that matches the user's search query in the format\nof <first_name> <last_name>, <phone_number>.\n\"\"\"\n\n#global varible declaration\ncont = True\none = 1\ntwo = 2\n\n#main program loop\nwhile cont == True:\n    cont = True\n    temp = 0\n    name = input(\"Enter a name you would like to search: \").lower()\n\n#search text file for user input, if found print line, otherwise return nothing\n    f = open(\"phones.txt\", 'r')\n    while True:\n        line = f.readline()\n        lineLow = line.lower()\n        if name in lineLow:\n            firstName, lastName, phoneNum = line.split()\n            print(\"\\nName:  {} {}\\nPhone: {}\".format(firstName, lastName, phoneNum))\n        if line == \"\":\n            break\n    f.close()\n\n#restart program loop\n    while temp == 0:\n        temp = int(input(\"\\nRestart Program? Enter 1 to start again and 2 to quit: \"))\n        if temp == 1:\n            cont = True\n            break\n        if temp == 2:\n            cont = False\n            break\n        else:\n            temp = 0\n","repo_name":"jcosg336/phone_Search","sub_path":"phone_search.py","file_name":"phone_search.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14923624436","text":"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\nslim = tf.contrib.slim\n\nSIMPLE = 'simple'\nRESNET = 'resnet'\nARCHITECTURES = [SIMPLE, RESNET]\n\nSCALE_TRANSLATION = 0.001\nSCALE_ROTATION = 0.01\n\n# Disparity (inverse depth) values range from 0.01 to 10. Note that effectively,\n# this is undone if depth normalization is used, which scales the values to\n# have a mean of 1.\nDISP_SCALING = 10\nMIN_DISP = 0.01\nWEIGHT_DECAY_KEY = 'WEIGHT_DECAY'\nEGOMOTION_VEC_SIZE = 6\n\n\ndef egomotion_net(image_stack, disp_bottleneck_stack, joint_encoder, seq_length,\n                  weight_reg):\n  \"\"\"Predict ego-motion vectors from a stack of frames or embeddings.\n\n  Args:\n    image_stack: Input tensor with shape [B, h, w, seq_length * 3] in order.\n    disp_bottleneck_stack: Input tensor with shape [B, h_hidden, w_hidden,\n        seq_length * c_hidden] in order.\n    joint_encoder: Determines if the same encoder is used for computing the\n        bottleneck layer of both the egomotion and the depth prediction\n        network. If enabled, disp_bottleneck_stack is used as input, and the\n        encoding steps are skipped. If disabled, a separate encoder is defined\n        on image_stack.\n    seq_length: The sequence length used.\n    weight_reg: The amount of weight regularization.\n\n  Returns:\n    Egomotion vectors with shape [B, seq_length - 1, 6].\n  \"\"\"\n  num_egomotion_vecs = seq_length - 1\n  with tf.variable_scope('pose_exp_net') as sc:\n    end_points_collection = sc.original_name_scope + '_end_points'\n    with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],\n                        normalizer_fn=None,\n                        weights_regularizer=slim.l2_regularizer(weight_reg),\n                        normalizer_params=None,\n                        activation_fn=tf.nn.relu,\n                        outputs_collections=end_points_collection):\n      if not joint_encoder:\n        # Define separate encoder. If sharing, we can skip the encoding step,\n        # as the bottleneck layer will already be passed as input.\n        cnv1 = slim.conv2d(image_stack, 16, [7, 7], stride=2, scope='cnv1')\n        cnv2 = slim.conv2d(cnv1, 32, [5, 5], stride=2, scope='cnv2')\n        cnv3 = slim.conv2d(cnv2, 64, [3, 3], stride=2, scope='cnv3')\n        cnv4 = slim.conv2d(cnv3, 128, [3, 3], stride=2, scope='cnv4')\n        cnv5 = slim.conv2d(cnv4, 256, [3, 3], stride=2, scope='cnv5')\n\n      with tf.variable_scope('pose'):\n        inputs = disp_bottleneck_stack if joint_encoder else cnv5\n        cnv6 = slim.conv2d(inputs, 256, [3, 3], stride=2, scope='cnv6')\n        cnv7 = slim.conv2d(cnv6, 256, [3, 3], stride=2, scope='cnv7')\n        pred_channels = EGOMOTION_VEC_SIZE * num_egomotion_vecs\n        egomotion_pred = slim.conv2d(cnv7, pred_channels, [1, 1], scope='pred',\n                                     stride=1, normalizer_fn=None,\n                                     activation_fn=None)\n        egomotion_avg = tf.reduce_mean(egomotion_pred, [1, 2])\n        egomotion_res = tf.reshape(\n            egomotion_avg, [-1, num_egomotion_vecs, EGOMOTION_VEC_SIZE])\n        # Tinghui found that scaling by a small constant facilitates training.\n        egomotion_scaled = tf.concat([egomotion_res[:, 0:3] * SCALE_TRANSLATION,\n                                      egomotion_res[:, 3:6] * SCALE_ROTATION],\n                                     axis=1)\n    return egomotion_scaled\n\n\ndef objectmotion_net(image_stack, disp_bottleneck_stack, joint_encoder,\n                     seq_length, weight_reg):\n  \"\"\"Predict object-motion vectors from a stack of frames or embeddings.\n\n  Args:\n    image_stack: Input tensor with shape [B, h, w, seq_length * 3] in order.\n    disp_bottleneck_stack: Input tensor with shape [B, h_hidden, w_hidden,\n        seq_length * c_hidden] in order.\n    joint_encoder: Determines if the same encoder is used for computing the\n        bottleneck layer of both the egomotion and the depth prediction\n        network. If enabled, disp_bottleneck_stack is used as input, and the\n        encoding steps are skipped. If disabled, a separate encoder is defined\n        on image_stack.\n    seq_length: The sequence length used.\n    weight_reg: The amount of weight regularization.\n\n  Returns:\n    Egomotion vectors with shape [B, seq_length - 1, 6].\n  \"\"\"\n  num_egomotion_vecs = seq_length - 1\n  with tf.variable_scope('pose_exp_net') as sc:\n    end_points_collection = sc.original_name_scope + '_end_points'\n    with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],\n                        normalizer_fn=None,\n                        weights_regularizer=slim.l2_regularizer(weight_reg),\n                        normalizer_params=None,\n                        activation_fn=tf.nn.relu,\n                        outputs_collections=end_points_collection):\n      if not joint_encoder:\n        # Define separate encoder. If sharing, we can skip the encoding step,\n        # as the bottleneck layer will already be passed as input.\n        cnv1 = slim.conv2d(image_stack, 16, [7, 7], stride=2, scope='cnv1')\n        cnv2 = slim.conv2d(cnv1, 32, [5, 5], stride=2, scope='cnv2')\n        cnv3 = slim.conv2d(cnv2, 64, [3, 3], stride=2, scope='cnv3')\n        cnv4 = slim.conv2d(cnv3, 128, [3, 3], stride=2, scope='cnv4')\n        cnv5 = slim.conv2d(cnv4, 256, [3, 3], stride=2, scope='cnv5')\n\n      with tf.variable_scope('pose'):\n        inputs = disp_bottleneck_stack if joint_encoder else cnv5\n        cnv6 = slim.conv2d(inputs, 256, [3, 3], stride=2, scope='cnv6')\n        cnv7 = slim.conv2d(cnv6, 256, [3, 3], stride=2, scope='cnv7')\n        pred_channels = EGOMOTION_VEC_SIZE * num_egomotion_vecs\n        egomotion_pred = slim.conv2d(cnv7, pred_channels, [1, 1], scope='pred',\n                                     stride=1, normalizer_fn=None,\n                                     activation_fn=None)\n        egomotion_avg = tf.reduce_mean(egomotion_pred, [1, 2])\n        egomotion_res = tf.reshape(\n            egomotion_avg, [-1, num_egomotion_vecs, EGOMOTION_VEC_SIZE])\n        # Tinghui found that scaling by a small constant facilitates training.\n        egomotion_scaled = tf.concat([egomotion_res[:, 0:3] * SCALE_TRANSLATION,\n                                      egomotion_res[:, 3:6] * SCALE_ROTATION],\n                                     axis=1)\n    return egomotion_scaled\n\n\ndef disp_net(architecture, image, use_skip, weight_reg, is_training):\n  \"\"\"Defines an encoder-decoder architecture for depth prediction.\"\"\"\n  if architecture not in ARCHITECTURES:\n    raise ValueError('Unknown architecture.')\n  encoder_selected = encoder(architecture)\n  decoder_selected = decoder(architecture)\n\n  # Encode image.\n  bottleneck, skip_connections = encoder_selected(image, weight_reg,\n                                                  is_training)\n  # Decode to depth.\n  multiscale_disps_i = decoder_selected(target_image=image,\n                                        bottleneck=bottleneck,\n                                        weight_reg=weight_reg,\n                                        use_skip=use_skip,\n                                        skip_connections=skip_connections)\n  return multiscale_disps_i, bottleneck\n\n\ndef encoder(architecture):\n  return encoder_resnet if architecture == RESNET else encoder_simple\n\n\ndef decoder(architecture):\n  return decoder_resnet if architecture == RESNET else decoder_simple\n\n\ndef encoder_simple(target_image, weight_reg, is_training):\n  \"\"\"Defines the old encoding architecture.\"\"\"\n  del is_training\n  with slim.arg_scope([slim.conv2d],\n                      normalizer_fn=None,\n                      normalizer_params=None,\n                      weights_regularizer=slim.l2_regularizer(weight_reg),\n                      activation_fn=tf.nn.relu):\n    # Define (joint) encoder.\n    cnv1 = slim.conv2d(target_image, 32, [7, 7], stride=2, scope='cnv1')\n    cnv1b = slim.conv2d(cnv1, 32, [7, 7], stride=1, scope='cnv1b')\n    cnv2 = slim.conv2d(cnv1b, 64, [5, 5], stride=2, scope='cnv2')\n    cnv2b = slim.conv2d(cnv2, 64, [5, 5], stride=1, scope='cnv2b')\n    cnv3 = slim.conv2d(cnv2b, 128, [3, 3], stride=2, scope='cnv3')\n    cnv3b = slim.conv2d(cnv3, 128, [3, 3], stride=1, scope='cnv3b')\n    cnv4 = slim.conv2d(cnv3b, 256, [3, 3], stride=2, scope='cnv4')\n    cnv4b = slim.conv2d(cnv4, 256, [3, 3], stride=1, scope='cnv4b')\n    cnv5 = slim.conv2d(cnv4b, 512, [3, 3], stride=2, scope='cnv5')\n    cnv5b = slim.conv2d(cnv5, 512, [3, 3], stride=1, scope='cnv5b')\n    cnv6 = slim.conv2d(cnv5b, 512, [3, 3], stride=2, scope='cnv6')\n    cnv6b = slim.conv2d(cnv6, 512, [3, 3], stride=1, scope='cnv6b')\n    cnv7 = slim.conv2d(cnv6b, 512, [3, 3], stride=2, scope='cnv7')\n    cnv7b = slim.conv2d(cnv7, 512, [3, 3], stride=1, scope='cnv7b')\n  return cnv7b, (cnv6b, cnv5b, cnv4b, cnv3b, cnv2b, cnv1b)\n\n\ndef decoder_simple(target_image, bottleneck, weight_reg, use_skip,\n                   skip_connections):\n  \"\"\"Defines the old depth decoder architecture.\"\"\"\n  h = target_image.get_shape()[1].value\n  w = target_image.get_shape()[2].value\n  (cnv6b, cnv5b, cnv4b, cnv3b, cnv2b, cnv1b) = skip_connections\n  with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],\n                      normalizer_fn=None,\n                      normalizer_params=None,\n                      weights_regularizer=slim.l2_regularizer(weight_reg),\n                      activation_fn=tf.nn.relu):\n    up7 = slim.conv2d_transpose(bottleneck, 512, [3, 3], stride=2,\n                                scope='upcnv7')\n    up7 = _resize_like(up7, cnv6b)\n    if use_skip:\n      i7_in = tf.concat([up7, cnv6b], axis=3)\n    else:\n      i7_in = up7\n    icnv7 = slim.conv2d(i7_in, 512, [3, 3], stride=1, scope='icnv7')\n\n    up6 = slim.conv2d_transpose(icnv7, 512, [3, 3], stride=2, scope='upcnv6')\n    up6 = _resize_like(up6, cnv5b)\n    if use_skip:\n      i6_in = tf.concat([up6, cnv5b], axis=3)\n    else:\n      i6_in = up6\n    icnv6 = slim.conv2d(i6_in, 512, [3, 3], stride=1, scope='icnv6')\n\n    up5 = slim.conv2d_transpose(icnv6, 256, [3, 3], stride=2, scope='upcnv5')\n    up5 = _resize_like(up5, cnv4b)\n    if use_skip:\n      i5_in = tf.concat([up5, cnv4b], axis=3)\n    else:\n      i5_in = up5\n    icnv5 = slim.conv2d(i5_in, 256, [3, 3], stride=1, scope='icnv5')\n\n    up4 = slim.conv2d_transpose(icnv5, 128, [3, 3], stride=2, scope='upcnv4')\n    up4 = _resize_like(up4, cnv3b)\n    if use_skip:\n      i4_in = tf.concat([up4, cnv3b], axis=3)\n    else:\n      i4_in = up4\n    icnv4 = slim.conv2d(i4_in, 128, [3, 3], stride=1, scope='icnv4')\n    disp4 = (slim.conv2d(icnv4, 1, [3, 3], stride=1, activation_fn=tf.sigmoid,\n                         normalizer_fn=None, scope='disp4')\n             * DISP_SCALING + MIN_DISP)\n    disp4_up = tf.image.resize_bilinear(disp4, [np.int(h / 4), np.int(w / 4)],\n                                        align_corners=True)\n\n    up3 = slim.conv2d_transpose(icnv4, 64, [3, 3], stride=2, scope='upcnv3')\n    up3 = _resize_like(up3, cnv2b)\n    if use_skip:\n      i3_in = tf.concat([up3, cnv2b, disp4_up], axis=3)\n    else:\n      i3_in = tf.concat([up3, disp4_up])\n    icnv3 = slim.conv2d(i3_in, 64, [3, 3], stride=1, scope='icnv3')\n    disp3 = (slim.conv2d(icnv3, 1, [3, 3], stride=1, activation_fn=tf.sigmoid,\n                         normalizer_fn=None, scope='disp3')\n             * DISP_SCALING + MIN_DISP)\n    disp3_up = tf.image.resize_bilinear(disp3, [np.int(h / 2), np.int(w / 2)],\n                                        align_corners=True)\n\n    up2 = slim.conv2d_transpose(icnv3, 32, [3, 3], stride=2, scope='upcnv2')\n    up2 = _resize_like(up2, cnv1b)\n    if use_skip:\n      i2_in = tf.concat([up2, cnv1b, disp3_up], axis=3)\n    else:\n      i2_in = tf.concat([up2, disp3_up])\n    icnv2 = slim.conv2d(i2_in, 32, [3, 3], stride=1, scope='icnv2')\n    disp2 = (slim.conv2d(icnv2, 1, [3, 3], stride=1, activation_fn=tf.sigmoid,\n                         normalizer_fn=None, scope='disp2')\n             * DISP_SCALING + MIN_DISP)\n    disp2_up = tf.image.resize_bilinear(disp2, [h, w], align_corners=True)\n\n    up1 = slim.conv2d_transpose(icnv2, 16, [3, 3], stride=2, scope='upcnv1')\n    i1_in = tf.concat([up1, disp2_up], axis=3)\n    icnv1 = slim.conv2d(i1_in, 16, [3, 3], stride=1, scope='icnv1')\n    disp1 = (slim.conv2d(icnv1, 1, [3, 3], stride=1, activation_fn=tf.sigmoid,\n                         normalizer_fn=None, scope='disp1')\n             * DISP_SCALING + MIN_DISP)\n  return [disp1, disp2, disp3, disp4]\n\n\ndef encoder_resnet(target_image, weight_reg, is_training):\n  \"\"\"Defines a ResNet18-based encoding architecture.\n\n  This implementation follows Juyong Kim's implementation of ResNet18 on GitHub:\n  https://github.com/dalgu90/resnet-18-tensorflow\n\n  Args:\n    target_image: Input tensor with shape [B, h, w, 3] to encode.\n    weight_reg: Parameter ignored.\n    is_training: Whether the model is being trained or not.\n\n  Returns:\n    Tuple of tensors, with the first being the bottleneck layer as tensor of\n    size [B, h_hid, w_hid, c_hid], and others being intermediate layers\n    for building skip-connections.\n  \"\"\"\n  del weight_reg\n  encoder_filters = [64, 64, 128, 256, 512]\n  stride = 2\n\n  # conv1\n  with tf.variable_scope('conv1'):\n    x = _conv(target_image, 7, encoder_filters[0], stride)\n    x = _bn(x, is_train=is_training)\n    econv1 = _relu(x)\n    x = tf.nn.max_pool(econv1, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME')\n\n  # conv2_x\n  x = _residual_block(x, is_training, name='conv2_1')\n  econv2 = _residual_block(x, is_training, name='conv2_2')\n\n  # conv3_x\n  x = _residual_block_first(econv2, is_training, encoder_filters[2], stride,\n                            name='conv3_1')\n  econv3 = _residual_block(x, is_training, name='conv3_2')\n\n  # conv4_x\n  x = _residual_block_first(econv3, is_training, encoder_filters[3], stride,\n                            name='conv4_1')\n  econv4 = _residual_block(x, is_training, name='conv4_2')\n\n  # conv5_x\n  x = _residual_block_first(econv4, is_training, encoder_filters[4], stride,\n                            name='conv5_1')\n  econv5 = _residual_block(x, is_training, name='conv5_2')\n  return econv5, (econv4, econv3, econv2, econv1)\n\n\ndef decoder_resnet(target_image, bottleneck, weight_reg, use_skip,\n                   skip_connections):\n  \"\"\"Defines the depth decoder architecture.\n\n  Args:\n    target_image: The original encoder input tensor with shape [B, h, w, 3].\n                  Just the shape information is used here.\n    bottleneck: Bottleneck layer to be decoded.\n    weight_reg: The amount of weight regularization.\n    use_skip: Whether the passed skip connections econv1, econv2, econv3 and\n              econv4 should be used.\n    skip_connections: Tensors for building skip-connections.\n\n  Returns:\n    Disparities at 4 different scales.\n  \"\"\"\n  (econv4, econv3, econv2, econv1) = skip_connections\n  decoder_filters = [16, 32, 64, 128, 256]\n  default_pad = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])\n  reg = slim.l2_regularizer(weight_reg) if weight_reg > 0.0 else None\n  with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],\n                      normalizer_fn=None,\n                      normalizer_params=None,\n                      activation_fn=tf.nn.relu,\n                      weights_regularizer=reg):\n    upconv5 = slim.conv2d_transpose(bottleneck, decoder_filters[4], [3, 3],\n                                    stride=2, scope='upconv5')\n    upconv5 = _resize_like(upconv5, econv4)\n    if use_skip:\n      i5_in = tf.concat([upconv5, econv4], axis=3)\n    else:\n      i5_in = upconv5\n    i5_in = tf.pad(i5_in, default_pad, mode='REFLECT')\n    iconv5 = slim.conv2d(i5_in, decoder_filters[4], [3, 3], stride=1,\n                         scope='iconv5', padding='VALID')\n\n    upconv4 = slim.conv2d_transpose(iconv5, decoder_filters[3], [3, 3],\n                                    stride=2, scope='upconv4')\n    upconv4 = _resize_like(upconv4, econv3)\n    if use_skip:\n      i4_in = tf.concat([upconv4, econv3], axis=3)\n    else:\n      i4_in = upconv4\n    i4_in = tf.pad(i4_in, default_pad, mode='REFLECT')\n    iconv4 = slim.conv2d(i4_in, decoder_filters[3], [3, 3], stride=1,\n                         scope='iconv4', padding='VALID')\n\n    disp4_input = tf.pad(iconv4, default_pad, mode='REFLECT')\n    disp4 = (slim.conv2d(disp4_input, 1, [3, 3], stride=1,\n                         activation_fn=tf.sigmoid, normalizer_fn=None,\n                         scope='disp4', padding='VALID')\n             * DISP_SCALING + MIN_DISP)\n\n    upconv3 = slim.conv2d_transpose(iconv4, decoder_filters[2], [3, 3],\n                                    stride=2, scope='upconv3')\n    upconv3 = _resize_like(upconv3, econv2)\n    if use_skip:\n      i3_in = tf.concat([upconv3, econv2], axis=3)\n    else:\n      i3_in = upconv3\n    i3_in = tf.pad(i3_in, default_pad, mode='REFLECT')\n    iconv3 = slim.conv2d(i3_in, decoder_filters[2], [3, 3], stride=1,\n                         scope='iconv3', padding='VALID')\n    disp3_input = tf.pad(iconv3, default_pad, mode='REFLECT')\n    disp3 = (slim.conv2d(disp3_input, 1, [3, 3], stride=1,\n                         activation_fn=tf.sigmoid, normalizer_fn=None,\n                         scope='disp3', padding='VALID')\n             * DISP_SCALING + MIN_DISP)\n\n    upconv2 = slim.conv2d_transpose(iconv3, decoder_filters[1], [3, 3],\n                                    stride=2, scope='upconv2')\n    upconv2 = _resize_like(upconv2, econv1)\n    if use_skip:\n      i2_in = tf.concat([upconv2, econv1], axis=3)\n    else:\n      i2_in = upconv2\n    i2_in = tf.pad(i2_in, default_pad, mode='REFLECT')\n    iconv2 = slim.conv2d(i2_in, decoder_filters[1], [3, 3], stride=1,\n                         scope='iconv2', padding='VALID')\n    disp2_input = tf.pad(iconv2, default_pad, mode='REFLECT')\n    disp2 = (slim.conv2d(disp2_input, 1, [3, 3], stride=1,\n                         activation_fn=tf.sigmoid, normalizer_fn=None,\n                         scope='disp2', padding='VALID')\n             * DISP_SCALING + MIN_DISP)\n\n    upconv1 = slim.conv2d_transpose(iconv2, decoder_filters[0], [3, 3],\n                                    stride=2, scope='upconv1')\n    upconv1 = _resize_like(upconv1, target_image)\n    upconv1 = tf.pad(upconv1, default_pad, mode='REFLECT')\n    iconv1 = slim.conv2d(upconv1, decoder_filters[0], [3, 3], stride=1,\n                         scope='iconv1', padding='VALID')\n    disp1_input = tf.pad(iconv1, default_pad, mode='REFLECT')\n    disp1 = (slim.conv2d(disp1_input, 1, [3, 3], stride=1,\n                         activation_fn=tf.sigmoid, normalizer_fn=None,\n                         scope='disp1', padding='VALID')\n             * DISP_SCALING + MIN_DISP)\n\n  return [disp1, disp2, disp3, disp4]\n\n\ndef _residual_block_first(x, is_training, out_channel, strides, name='unit'):\n  \"\"\"Helper function for defining ResNet architecture.\"\"\"\n  in_channel = x.get_shape().as_list()[-1]\n  with tf.variable_scope(name):\n    # Shortcut connection\n    if in_channel == out_channel:\n      if strides == 1:\n        shortcut = tf.identity(x)\n      else:\n        shortcut = tf.nn.max_pool(x, [1, strides, strides, 1],\n                                  [1, strides, strides, 1], 'VALID')\n    else:\n      shortcut = _conv(x, 1, out_channel, strides, name='shortcut')\n    # Residual\n    x = _conv(x, 3, out_channel, strides, name='conv_1')\n    x = _bn(x, is_train=is_training, name='bn_1')\n    x = _relu(x, name='relu_1')\n    x = _conv(x, 3, out_channel, 1, name='conv_2')\n    x = _bn(x, is_train=is_training, name='bn_2')\n    # Merge\n    x = x + shortcut\n    x = _relu(x, name='relu_2')\n  return x\n\n\ndef _residual_block(x, is_training, input_q=None, output_q=None, name='unit'):\n  \"\"\"Helper function for defining ResNet architecture.\"\"\"\n  num_channel = x.get_shape().as_list()[-1]\n  with tf.variable_scope(name):\n    shortcut = x  # Shortcut connection\n    # Residual\n    x = _conv(x, 3, num_channel, 1, input_q=input_q, output_q=output_q,\n              name='conv_1')\n    x = _bn(x, is_train=is_training, name='bn_1')\n    x = _relu(x, name='relu_1')\n    x = _conv(x, 3, num_channel, 1, input_q=output_q, output_q=output_q,\n              name='conv_2')\n    x = _bn(x, is_train=is_training, name='bn_2')\n    # Merge\n    x = x + shortcut\n    x = _relu(x, name='relu_2')\n  return x\n\n\ndef _conv(x, filter_size, out_channel, stride, pad='SAME', input_q=None,\n          output_q=None, name='conv'):\n  \"\"\"Helper function for defining ResNet architecture.\"\"\"\n  if (input_q is None) ^ (output_q is None):\n    raise ValueError('Input/Output splits are not correctly given.')\n\n  in_shape = x.get_shape()\n  with tf.variable_scope(name):\n    # Main operation: conv2d\n    with tf.device('/CPU:0'):\n      kernel = tf.get_variable(\n          'kernel', [filter_size, filter_size, in_shape[3], out_channel],\n          tf.float32, initializer=tf.random_normal_initializer(\n              stddev=np.sqrt(2.0/filter_size/filter_size/out_channel)))\n    if kernel not in tf.get_collection(WEIGHT_DECAY_KEY):\n      tf.add_to_collection(WEIGHT_DECAY_KEY, kernel)\n    conv = tf.nn.conv2d(x, kernel, [1, stride, stride, 1], pad)\n  return conv\n\n\ndef _bn(x, is_train, name='bn'):\n  \"\"\"Helper function for defining ResNet architecture.\"\"\"\n  bn = tf.layers.batch_normalization(x, training=is_train, name=name)\n  return bn\n\n\ndef _relu(x, name=None, leakness=0.0):\n  \"\"\"Helper function for defining ResNet architecture.\"\"\"\n  if leakness > 0.0:\n    name = 'lrelu' if name is None else name\n    return tf.maximum(x, x*leakness, name='lrelu')\n  else:\n    name = 'relu' if name is None else name\n    return tf.nn.relu(x, name='relu')\n\n\ndef _resize_like(inputs, ref):\n  i_h, i_w = inputs.get_shape()[1], inputs.get_shape()[2]\n  r_h, r_w = ref.get_shape()[1], ref.get_shape()[2]\n  if i_h == r_h and i_w == r_w:\n    return inputs\n  else:\n    # TODO(casser): Other interpolation methods could be explored here.\n    return tf.image.resize_bilinear(inputs, [r_h.value, r_w.value],\n                                    align_corners=True)\n","repo_name":"yitu-opensource/MobileNeXt","sub_path":"mobile_deployment/tensorflow/slim/models/research/struct2depth/nets.py","file_name":"nets.py","file_ext":"py","file_size_in_byte":21956,"program_lang":"python","lang":"en","doc_type":"code","stars":146,"dataset":"github-code","pt":"77"}
+{"seq_id":"12372191718","text":"__revision__ = \"$Id$\"\n\n# error messages. (should not happen, except in case of reload, or url\n# altering)\nCFG_WEBMESSAGE_ERROR_MESSAGES = \\\n{   'ERR_WEBMESSAGE_NOTOWNER':  '_(\"This message is not in your mailbox\")',\n    'ERR_WEBMESSAGE_NONICKNAME':'_(\"No nickname or user for uid #%s\")',\n    'ERR_WEBMESSAGE_NOMESSAGE': '_(\"This message doesn\\'t exist\")'\n}\n\n# status of message (table user_msgMESSAGE)\nCFG_WEBMESSAGE_STATUS_CODE = \\\n{\n    'NEW': 'N',\n    'READ': 'R',\n    'REMINDER': 'M'\n}\n# values indicating which results field to display while writing a message\nCFG_WEBMESSAGE_RESULTS_FIELD = \\\n{\n    'USER': 'user',\n    'GROUP': 'group',\n    'NONE': 'none'\n}\n\n# separator used in every list of recipients\nCFG_WEBMESSAGE_SEPARATOR = ','\n\n# list of roles (find them in accROLE table) without quota\nCFG_WEBMESSAGE_ROLES_WITHOUT_QUOTA = ['superadmin']\n\n\n","repo_name":"lbjay/cds-invenio","sub_path":"modules/webmessage/lib/webmessage_config.py","file_name":"webmessage_config.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"25871147023","text":"import math\nimport matplotlib.pyplot as plt\nfrom .Generaldistribution import Distribution\n\nclass Binomial(Distribution):\n    \"\"\" Binomial distributions class for calculating and\n    visualizing a Binomial distributions.\n    \n    Attributes:\n        mean (float) representing the mean value of the distributions\n        stdev (float) representing the standard deviation of the distributions\n        data_list (list of floats) a list of floats to be extracted from the data file\n        p (float) representing the probability of an event occurring\n        n (int) the total number of trials\n    \n    \n    TODO: Fill out all TODOs in the functions below\n            \n    \"\"\"\n    \n    #       A binomial distributions is defined by two variables:\n    #           the probability of getting a positive outcome\n    #           the number of trials\n    \n    #       If you know these two values, you can calculate the mean and the standard deviation\n    #       \n    #       For example, if you flip a fair coin 25 times, p = 0.5 and n = 25\n    #       You can then calculate the mean and standard deviation with the following formula:\n    #           mean = p * n\n    #           standard deviation = sqrt(n * p * (1 - p))\n    \n    #       \n    \n    def __init__(self, prob=.5, size=20):\n\n        Distribution.__init__(self)\n        self.p = prob\n        self.n = size\n\n    \n    def calculate_mean(self):\n    \n        \"\"\"Function to calculate the mean from p and n\n        \n        Args: \n            None\n        \n        Returns: \n            float: mean of the data set\n    \n        \"\"\"\n\n        self.mean = self.p * self.n\n\n        return self.mean\n\n\n\n    def calculate_stdev(self):\n\n        \"\"\"Function to calculate the standard deviation from p and n.\n        \n        Args: \n            None\n        \n        Returns: \n            float: standard deviation of the data set\n    \n        \"\"\"\n\n        self.stdev = math.sqrt(self.n * self.p * (1 - self.p))\n\n        return self.stdev\n        \n        \n        \n    def replace_stats_with_data(self):\n    \n        \"\"\"Function to calculate p and n from the data set\n        \n        Args: \n            None\n        \n        Returns: \n            float: the p value\n            float: the n value\n    \n        \"\"\"\n\n        self.n = len(self.data)\n        self.p = sum(self.data) / self.n\n        self.calculate_mean()\n        self.calculate_stdev()\n\n        return self.p, self.n\n\n\n    def plot_bar(self):\n        \"\"\"Function to output a histogram of the instance variable data using \n        matplotlib pyplot library.\n        \n        Args:\n            None\n            \n        Returns:\n            None\n        \"\"\"\n\n        plt.bar(x = ['0', '1'], height = [(1 - self.p) * self.n, self.p * self.n])\n        plt.xlabel('Data')\n        plt.ylabel('Frequency')\n        plt.title('Bar Chart of Data')\n        plt.show()\n\n\n    def pdf(self, k):\n        \"\"\"Probability density function calculator for the gaussian distributions.\n        \n        Args:\n            k (float): point for calculating the probability density function\n            \n        \n        Returns:\n            float: probability density function output\n        \"\"\"\n\n        comb = math.factorial(self.n) / (math.factorial(k) * math.factorial(self.n - k))\n\n        return comb * (self.p ** k) * ((1 - self.p) ** (self.n - k))\n\n\n    def plot_bar_pdf(self):\n\n        \"\"\"Function to plot the pdf of the binomial distributions\n        \n        Args:\n            None\n        \n        Returns:\n            list: x values for the pdf plot\n            list: y values for the pdf plot\n            \n        \"\"\"\n    \n        # TODO: Use a bar chart to plot the probability density function from\n        # k = 0 to k = n\n        \n        #   Hint: You'll need to use the pdf() method defined above to calculate the\n        #   density function for every value of k.\n        \n        #   Be sure to label the bar chart with a title, x label and y label\n\n        #   This method should also return the x and y values used to make the chart\n        #   The x and y values should be stored in separate lists\n\n        x = []\n        y = []\n\n        for i in range(self.n + 1):\n            x.append(i)\n            y.append(self.pdf(i))\n\n        plt.bar(x, y)\n        plt.xlabel(\"# of Success\")\n        plt.ylabel(\"Probability\")\n        plt.title(\"Probability Distribution of Binomial Data\")\n        plt.show()\n\n\n    def __add__(self, other):\n        \n        \"\"\"Function to add together two Binomial distributions with equal p\n        \n        Args:\n            other (Binomial): Binomial instance\n            \n        Returns:\n            Binomial: Binomial distributions\n            \n        \"\"\"\n        \n        try:\n            assert self.p == other.p, 'p values are not equal'\n        except AssertionError as error:\n            raise\n\n        result = Binomial(self.p, self.n + other.n)\n        result.calculate_mean()\n        result.calculate_stdev()\n\n        return result\n        \n        \n    def __repr__(self):\n    \n        \"\"\"Function to output the characteristics of the Binomial instance\n        \n        Args:\n            None\n        \n        Returns:\n            string: characteristics of the Gaussian\n        \n        \"\"\"\n\n        return \"mean {}, standard deviation {}, p {}, n {}\".format(self.mean, self.stdev, self.p, self.n)\n","repo_name":"chezka-sino/distribution-package","sub_path":"distributions/Binomialdistribution.py","file_name":"Binomialdistribution.py","file_ext":"py","file_size_in_byte":5346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8603800263","text":"import re\nimport random\nimport itertools\nfrom collections import defaultdict, deque\n\nfrom src.utilities import *\nfrom src import debuglog, errlog, plog, users, channels\nfrom src.functions import get_players, get_all_players, get_main_role, get_reveal_role, get_target\nfrom src.decorators import command, event_listener\nfrom src.containers import UserList, UserSet, UserDict, DefaultUserDict\nfrom src.dispatcher import MessageDispatcher\nfrom src.messages import messages\nfrom src.status import try_misdirection, try_exchange, is_silent\n\nfrom src.roles.helper.shamans import setup_variables, get_totem_target, give_totem\n\nTOTEMS, LASTGIVEN, SHAMANS = setup_variables(\"shaman\", knows_totem=True)\n\n@command(\"give\", \"totem\", chan=False, pm=True, playing=True, silenced=True, phases=(\"night\",), roles=(\"shaman\",))\ndef shaman_totem(var, wrapper, message):\n    \"\"\"Give a totem to a player.\"\"\"\n\n    target = get_totem_target(var, wrapper, message, LASTGIVEN)\n    if not target:\n        return\n\n    SHAMANS[wrapper.source] = give_totem(var, wrapper, target, prefix=\"You\", role=\"shaman\", msg=\" of {0}\".format(TOTEMS[wrapper.source]))\n\n@event_listener(\"transition_day_begin\", priority=4)\ndef on_transition_day_begin(evt, var):\n    # Select random totem recipients if shamans didn't act\n    pl = get_players()\n    for shaman in get_players((\"shaman\",)):\n        if shaman not in SHAMANS and not is_silent(var, shaman):\n            ps = pl[:]\n            if shaman in LASTGIVEN:\n                if LASTGIVEN[shaman] in ps:\n                    ps.remove(LASTGIVEN[shaman])\n            if ps:\n                target = random.choice(ps)\n                dispatcher = MessageDispatcher(shaman, shaman)\n\n                SHAMANS[shaman] = give_totem(var, dispatcher, target, prefix=messages[\"random_totem_prefix\"], role=\"shaman\", msg=\" of {0}\".format(TOTEMS[shaman]))\n            else:\n                LASTGIVEN[shaman] = None\n        elif shaman not in SHAMANS:\n            LASTGIVEN[shaman] = None\n\n@event_listener(\"transition_night_end\", priority=2.01)\ndef on_transition_night_end(evt, var):\n    chances = var.CURRENT_GAMEMODE.TOTEM_CHANCES\n    max_totems = sum(x[\"shaman\"] for x in chances.values())\n    ps = get_players()\n    shamans = get_players((\"shaman\",))\n    for s in list(LASTGIVEN):\n        if s not in shamans:\n            del LASTGIVEN[s]\n\n    for shaman in shamans:\n        pl = ps[:]\n        random.shuffle(pl)\n        if LASTGIVEN.get(shaman):\n            if LASTGIVEN[shaman] in pl:\n                pl.remove(LASTGIVEN[shaman])\n\n        target = 0\n        rand = random.random() * max_totems\n        for t in chances:\n            target += chances[t][\"shaman\"]\n            if rand <= target:\n                TOTEMS[shaman] = t\n                break\n        if shaman.prefers_simple():\n            shaman.send(messages[\"shaman_simple\"].format(\"shaman\"))\n            shaman.send(messages[\"totem_simple\"].format(TOTEMS[shaman]))\n        else:\n            shaman.send(messages[\"shaman_notify\"].format(\"shaman\", \"\"))\n            totem = TOTEMS[shaman]\n            tmsg = messages[\"shaman_totem\"].format(totem)\n            tmsg += messages[totem + \"_totem\"]\n            shaman.send(tmsg)\n        shaman.send(messages[\"players_list\"].format(\", \".join(p.nick for p in pl)))\n\n@event_listener(\"get_role_metadata\")\ndef on_get_role_metadata(evt, var, kind):\n    if kind == \"role_categories\":\n        evt.data[\"shaman\"] = {\"Village\", \"Safe\", \"Nocturnal\"}\n    elif kind == \"lycanthropy_role\":\n        evt.data[\"shaman\"] = {\"role\": \"wolf shaman\", \"prefix\": \"shaman\"}\n\n@event_listener(\"default_totems\")\ndef set_shaman_totems(evt, chances):\n    chances[\"death\"]        [\"shaman\"] = 1\n    chances[\"protection\"]   [\"shaman\"] = 1\n    chances[\"silence\"]      [\"shaman\"] = 1\n    chances[\"revealing\"]    [\"shaman\"] = 1\n    chances[\"desperation\"]  [\"shaman\"] = 1\n    chances[\"impatience\"]   [\"shaman\"] = 1\n    chances[\"pacifism\"]     [\"shaman\"] = 1\n    chances[\"influence\"]    [\"shaman\"] = 1\n\n# vim: set sw=4 expandtab:\n","repo_name":"mweinelt/lykos","sub_path":"src/roles/shaman.py","file_name":"shaman.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"9389734870","text":"from django.urls import path\nfrom .views import HomePageView, PlanListView, PlanDetailView\n\napp_name = \"plans\"\n\nurlpatterns = [\n    path(\"\", HomePageView.as_view(), name=\"index\"),\n    path(\"plans/\", PlanListView.as_view(), name=\"plan_list\"),\n    path(\"plan/<uuid:pk>/\", PlanDetailView.as_view(), name=\"plan_detail\"),\n]\n","repo_name":"Sheharyar123/SubscriptionsHouse","sub_path":"plans/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4038076642","text":"import os\nimport re\nimport sys\n\ntry:\n    # when imported as 'module',\n    from .mapping_tables import letter_to_word, number_to_word\nexcept:\n    # when called as 'standalone tool' from CLI,\n    from mapping_tables import letter_to_word, number_to_word\n\nAIRLINE_TABLE_PATH = \"../callsign_table/callsign_table.csv\"\n\n\ndef load_airline_table(airline_table, icao_to_callword=dict()):\n\n    for line in open(airline_table, \"r\"):\n        # replace non-break space by normal space,\n        line = line.replace(\"\\xa0\", \" \")\n\n        # parse table of airlines,\n        if line.strip().split(\"\\t\")[0] == \"ICAO\":\n            continue\n        if line.strip() == \"\":\n            continue\n\n        arr = line.rstrip().split(\"\\t\")\n        if len(arr) < 3:\n            print(arr, file=sys.stderr)\n        icao, name, callword = arr[:3]\n\n        # add to dict, while replacing \"[ -]\" -> \"_\",\n        if icao not in icao_to_callword:\n            icao_to_callword[icao] = set()\n        icao_to_callword[icao].add(re.sub(\"[ -]\", \"_\", callword.lower()))\n\n    return icao_to_callword\n\n\ndef rewrite_by_words(input_string):\n    \"\"\"ICAO standard, 1 letter/digit -> 1 word\"\"\"\n\n    ans = []\n    for char in input_string:\n        if char in letter_to_word:\n            ans.append(letter_to_word[char])\n        elif char in number_to_word:\n            ans.append(number_to_word[char])\n        elif char == \" \":\n            continue\n        else:\n            raise Exception(\n                \"Unknown char '%s' in input_string '%s'\" % (char, input_string)\n            )\n\n    return ans\n\n\ndef rewrite_special(words):\n    \"\"\"Rewrite alredy expanded input to cover special cases\"\"\"\n\n    ans = []\n\n    # 'triple'\n    prev_word = None\n    counter = 1\n    for i, word in enumerate(words):\n        if word == prev_word:\n            counter += 1\n        else:\n            counter = 1\n\n        if counter == 3:\n            ans.append(words[: (i - 2)] + [\"triple\"] + [word] + words[(i + 1) :])\n            if word == \"zero\":\n                ans.append(words[: (i - 2)] + [\"triple\"] + [\"o\"] + words[(i + 1) :])\n            # shortened,\n            ans.append([\"triple\"] + [word] + words[(i + 1) :])\n\n        prev_word = word\n\n    # 'double'\n    prev_word = None\n    counter = 1\n    for i, word in enumerate(words):\n        if word == prev_word:\n            counter += 1\n        else:\n            counter = 1\n\n        next_word = None\n        try:\n            next_word = words[i + 1]\n        except:\n            pass\n\n        if counter == 2 and next_word != word:\n            ans.append(words[: (i - 1)] + [\"double\"] + [word] + words[(i + 1) :])\n            if word == \"zero\":\n                ans.append(words[: (i - 1)] + [\"double\"] + [\"o\"] + words[(i + 1) :])\n            # shortened,\n            ans.append([\"double\"] + [word] + words[(i + 1) :])\n\n        prev_word = word\n\n    return ans\n\n\nclass ExpandCallsign:\n    \"\"\"Class which expands callsigns:\n    'LUF123AB' -> 'lufthansa one two three alfa bravo'\n    it also generates the shortened versions...\n    \"\"\"\n\n    EXP_LVL_STANDARD = 5  # standard ICAO expansion\n    EXP_LVL_SPECIAL = 9  # e.g. BAW777 -> speedbird triple seven\n    EXP_LVL_FULL = 10  # e.g. BAW7703 -> seven zero three\n\n    EXP_LVL_MAPPING = {\n        \"standard\": EXP_LVL_STANDARD,\n        \"special\": EXP_LVL_SPECIAL,\n        \"full\": EXP_LVL_FULL,\n    }\n\n    def __init__(self, airline_table=AIRLINE_TABLE_PATH, level=\"standard\"):\n        # load the callsign mapping tables,\n        self.icao_to_callword = load_airline_table(airline_table)\n        self.level = self.EXP_LVL_MAPPING[level]  # indicates the strategy\n\n        # National prefixes:\n        # https://en.wikipedia.org/wiki/List_of_aircraft_registration_prefixes\n        self.national_prefixes = {\n            \"OK\": \"Czechia\",\n            \"SP\": \"Poland\",\n            \"OE\": \"Austria\",\n            \"OM\": \"Slovakia\",\n            \"HB\": \"Switzerland\",\n            \"D\": \"Germany\",\n            \"OO\": \"Belgium\",\n            \"PH\": \"Netherlands\",\n            \"LU\": \"Luxembourgh\",\n            \"F\": \"France\",\n            \"I\": \"Italy\",\n            \"M\": \"Spain\",\n            \"CR\": \"Portugal\",\n            \"CS\": \"Portugal\",\n            \"K\": \"UK\",\n            \"GE\": \"UK\",\n            \"EI\": \"Ireland\",\n            \"EJ\": \"Ireland\",\n            \"SA\": \"Sweden\",\n            \"LN\": \"Noraway\",\n            \"OH\": \"Finland\",\n            \"RA\": \"Russia\",\n            \"RF\": \"Russia\",\n            \"RR\": \"Russia\",\n            \"N\": \"USA\",\n        }\n\n    def expand_callsign(self, callsign):\n        expansions = self.expand_callsign_impl(callsign)\n\n        if not expansions:\n            return None\n\n        ans = []\n\n        # remove duplicities and keep original ranking,\n        already_have = set()\n        for expan in expansions:\n            key = \",\".join(expan)\n            if key not in already_have:\n                ans.append(expan)\n                already_have.add(key)\n\n        return ans\n\n    def expand_callsign_impl(self, callsign):\n        \"\"\"Expand single callsign.\n\n        input: callsign (i.e. 'LUF123AB')\n        output: list(list(str)) representing variants of 'verbalization'\n                return None, if callsign cannot be expanded\n        \"\"\"\n\n        # callsign with code from \"airline table\",\n        # - e.g. CSA123AB, BAW777C, BAW79\n        three_chars = callsign[:3]\n        if three_chars in self.icao_to_callword:\n            return self.expand_callsign_airline(callsign)\n\n        # callsign with \"national\" prefix (two-char or one-char),\n        # - e.g. OKA2730, OKHTM, N1003F, N1777R\n        two_chars = callsign[:2]\n        one_char = callsign[:1]\n        if (two_chars in self.national_prefixes) or (\n            one_char in self.national_prefixes\n        ):\n            return self.expand_callsign_national(callsign)\n\n        # other callsign, shorter that 5 chars,\n        if len(callsign) <= 5:\n            ans = list()  # return value,\n\n            # standard expansion,\n            arr = rewrite_by_words(callsign)\n            ans.append(arr)\n\n            # special expansions,\n            if self.level >= self.EXP_LVL_SPECIAL:\n                # prepare special version ('double', 'triple' rewritten)\n                for arr_special in rewrite_special(arr[1:]):\n                    ans.append(arr[:1] + arr_special)  # whole callsign (special)\n\n            # non-standard expansions,\n            if self.level >= self.EXP_LVL_FULL:\n                if len(arr) > 3:\n                    # append last three words\n                    ans.append(arr[-3:])\n\n            return ans\n\n        # otherwise throw it away...\n        print(\n            f\"{os.path.basename(__file__)}: Warning! Unrecognized callsign {callsign}\",\n            file=sys.stderr,\n        )\n\n        return None\n\n    def expand_callsign_airline(self, callsign):\n        # return value\n        ans = []\n\n        # three chars encoding airline,\n        three_chars = callsign[:3]\n        # translate the rest,\n        leftover = rewrite_by_words(callsign[3:])\n\n        # prepare special version ('double', 'triple' rewritten)\n        leftover_special = rewrite_special(leftover)\n\n        # there might be more airlines with same ICAO code,\n        # (or shortened airline designators...)\n        for codeword in sorted(self.icao_to_callword[three_chars]):\n\n            # standard expansions,\n            if self.level >= self.EXP_LVL_STANDARD:\n                # whole callsign\n                ans.append([codeword] + leftover)\n\n                # last three chars of leftover,\n                if len(leftover) > 3:\n                    ans.append([codeword] + leftover[-3:])\n\n                # last two chars of leftover,\n                if len(leftover) > 2:\n                    ans.append([codeword] + leftover[-2:])\n\n            # special expansions,\n            # (777 -> triple seven, 00 -> double zero, double o)\n            if self.level >= self.EXP_LVL_SPECIAL:\n                # add special spelling, e.g. 777 -> \"triple seven\"\n                for leftover_ in leftover_special:\n                    ans.append([codeword] + leftover_)\n\n            # non-standard expansions\n            if self.level >= self.EXP_LVL_FULL:\n\n                # with airline designator,\n                if len(leftover) > 3:\n                    # first two chars and last char of leftover,\n                    ans.append([codeword] + leftover[:2] + leftover[-1:])\n                    # first char and last two chars of leftover,\n                    ans.append([codeword] + leftover[:1] + leftover[-2:])\n\n                # no airline designator,\n                ans.append(leftover)\n                if len(leftover) > 3:\n                    # last three chars of leftover (no airline designator)\n                    ans.append(leftover[-3:])\n\n                # with 'triple', 'double' rewritten, no designator,\n                for leftover_ in leftover_special:\n                    ans.append(leftover_)\n\n        # also add callsign fully \"spelled\" by ICAO alphabet,\n        ans.append(rewrite_by_words(callsign))\n\n        return ans\n\n    def expand_callsign_national(self, callsign):\n        # return value\n        ans = []\n\n        # callsign with \"national\" prefix (two-char),\n        two_chars = callsign[:2]\n        if two_chars in self.national_prefixes:\n            arr = rewrite_by_words(callsign)\n\n            # standard expansions,\n            ans.append(arr)  # whole callsign,\n\n            if len(arr) > 5:\n                # first 2, last 3 words\n                ans.append(arr[:2] + arr[-3:])\n            if len(arr) > 4:\n                # first 2, last 2 words\n                ans.append(arr[:2] + arr[-2:])\n\n            # special expansion,\n            if self.level >= self.EXP_LVL_SPECIAL:\n                # prepare special version ('double', 'triple' rewritten)\n                for arr_special in rewrite_special(arr[2:]):\n                    ans.append(arr[:2] + arr_special)  # whole callsign (special)\n\n            # non-standard expansions,\n            if self.level >= self.EXP_LVL_FULL:\n                if len(arr) > 5:\n                    # first word, last 3 words\n                    ans.append(arr[:1] + arr[-3:])\n                    # last 3 words only,\n                    ans.append(arr[-3:])\n                if len(arr) > 4:\n                    # first word, last 2 words\n                    ans.append(arr[:1] + arr[-2:])\n                # special without national prefix,\n                for arr_special in rewrite_special(arr[2:]):\n                    ans.append(arr_special)\n\n            return ans\n\n        # callsign with \"national\" prefix (one-char),\n        one_char = callsign[0]\n        if one_char in self.national_prefixes:\n            arr = rewrite_by_words(callsign)\n\n            # standard expansions,\n            ans.append(arr)  # whole callsign,\n            if len(arr) > 4:\n                # first 1, last 3 words\n                ans.append([arr[0]] + arr[-3:])\n            if len(arr) > 3:\n                # first 1, last 2 words\n                ans.append([arr[0]] + arr[-2:])\n\n            # special expansion,\n            if self.level >= self.EXP_LVL_SPECIAL:\n                # prepare special version ('double', 'triple' rewritten)\n                for arr_special in rewrite_special(arr[1:]):\n                    ans.append(arr[:1] + arr_special)  # whole callsign (special)\n\n            # non-standard expansions\n            if self.level >= self.EXP_LVL_FULL:\n                if len(arr) > 4:\n                    ans.append(arr[-3:])\n                # special without national prefix,\n                for arr_special in rewrite_special(arr[1:]):\n                    ans.append(arr_special)\n\n            return ans\n\n\ndef main():\n    \"\"\"Command line interface...\"\"\"\n    import argparse\n\n    lvl_mapping = ExpandCallsign.EXP_LVL_MAPPING\n\n    parser = argparse.ArgumentParser(description=\"CLI for callsign expansion.\")\n    parser.add_argument(\n        \"--expansion-level\", choices=list(lvl_mapping.keys()), default=\"standard\"\n    )\n    args = parser.parse_args()\n\n    exp_csl = ExpandCallsign(level=args.expansion_level)\n\n    # do the translation,\n    for line in sys.stdin:\n\n        # remove whitespace,\n        callsign = line.strip()\n\n        # skip header,\n        if callsign == \"callsigns\":\n            continue\n        # skip empty line,\n        if callsign == \"\":\n            continue\n\n        # expand callsign,\n        expanded = exp_csl.expand_callsign(callsign)\n\n        # print all variants,\n        if not expanded:\n            continue  # None returned?\n        for variant in expanded:\n            print(\" \".join(variant))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"idiap/atco2-corpus","sub_path":"lm/resources/callsign_expansion/expand_callsign.py","file_name":"expand_callsign.py","file_ext":"py","file_size_in_byte":12639,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"77"}
+{"seq_id":"40352180935","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\nimport math\nimport os\nimport random\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport pickle\nimport json\nfrom dataloaders.rawvideo_util import RawVideoExtractor\nimport multiprocessing\n\ndef process_question(question):\n    \"\"\"Process the question to make it canonical.\"\"\"\n    return question.strip(\" \").strip(\"?\").lower() + \"?\"\n\nclass PredictionDataset(Dataset):\n    def __init__(self, choice_video_ids, negative_pool, _get_rawvideo):\n        self.negative_pool = negative_pool\n        self._get_rawvideo = _get_rawvideo\n        self.choice_video_ids = choice_video_ids\n\n    def __len__(self):\n        return len(self.negative_pool)\n\n    def __getitem__(self, i):\n        neg = random.choice(self.negative_pool)\n        video, video_mask = self._get_rawvideo(self.choice_video_ids, math.floor(neg[0]), math.ceil(neg[1]))\n        return video, video_mask\n\nclass Ego4d_DataLoader_same_video(Dataset):\n    \"\"\"Ego4d dataset loader.\"\"\"\n    def __init__(\n            self,\n            subset,\n            data_path,\n            features_path,\n            tokenizer,\n            max_words=30,\n            feature_framerate=1,\n            max_frames=100,\n            image_resolution=224,\n            frame_order=0,\n            slice_framepos=0,\n    ):\n        self.data_path = data_path\n        self.features_path = features_path\n        self.feature_framerate = feature_framerate\n        self.max_words = max_words\n        self.max_frames = max_frames\n        self.tokenizer = tokenizer\n        # 0: ordinary order; 1: reverse order; 2: random order.\n        self.frame_order = frame_order\n        assert self.frame_order in [0, 1, 2]\n        # 0: cut from head frames; 1: cut from tail frames; 2: extract frames uniformly.\n        self.slice_framepos = slice_framepos\n        assert self.slice_framepos in [0, 1, 2]\n\n        self.subset = subset\n        assert self.subset in [\"train\", \"val\", \"test\"]\n\n        annotation_dict = {}\n        annotation_dict[\"train\"] = os.path.join(self.data_path, \"nlq_train_neg_10s.json\")\n        annotation_dict[\"val\"] = os.path.join(self.data_path, \"nlq_val_10s.json\")\n        annotation_dict[\"test\"] = os.path.join(self.data_path, \"nlq_val_10s.json\")\n\n        with open(annotation_dict[self.subset], mode=\"r\", encoding=\"utf-8\") as f:\n            split_data = json.load(f)\n\n        self.item_dict = {}\n        self.sentences_dict = {}\n        self.video_dict = {}\n        for video_datum in split_data[\"videos\"]:\n            for clip_datum in video_datum[\"clips\"]:\n                clip_uid = clip_datum[\"clip_uid\"]\n                self.video_dict[clip_uid] = os.path.join(self.features_path, f'{clip_uid}.mp4')\n                for ann_datum in clip_datum[\"annotations\"]:\n                    for index, datum in enumerate(ann_datum[\"language_queries\"]):\n                        if \"query\" not in datum or not datum[\"query\"]:\n                            continue\n                        new_dict = {\n                            \"video_id\": clip_uid,\n                            \"start_time\": math.floor(datum[\"clip_start_sec\"]),\n                            \"end_time\": math.ceil(datum[\"clip_end_sec\"]),\n                            \"sentence\": process_question(datum[\"query\"]),\n                            \"negatives\": datum['negatives'],\n                        }\n\n                        self.sentences_dict[len(self.sentences_dict)] = new_dict\n\n        print(\"Total videos: {}\".format(len(self.video_dict)))\n        print(\"Total Pairs: {}\".format(len(self.sentences_dict)))\n\n        self.rawVideoExtractor = RawVideoExtractor(framerate=feature_framerate, size=image_resolution)\n        self.SPECIAL_TOKEN = {\"CLS_TOKEN\": \"<|startoftext|>\", \"SEP_TOKEN\": \"<|endoftext|>\",\n                              \"MASK_TOKEN\": \"[MASK]\", \"UNK_TOKEN\": \"[UNK]\", \"PAD_TOKEN\": \"[PAD]\"}\n\n    def __len__(self):\n        return len(self.sentences_dict)\n\n    def _get_text(self, video_id, caption):\n        k = 1\n        choice_video_ids = [video_id]\n        pairs_text = np.zeros((k, self.max_words), dtype=np.long)\n        pairs_mask = np.zeros((k, self.max_words), dtype=np.long)\n        pairs_segment = np.zeros((k, self.max_words), dtype=np.long)\n\n        for i, video_id in enumerate(choice_video_ids):\n            words = self.tokenizer.tokenize(caption)\n\n            words = [self.SPECIAL_TOKEN[\"CLS_TOKEN\"]] + words\n            total_length_with_CLS = self.max_words - 1\n            if len(words) > total_length_with_CLS:\n                words = words[:total_length_with_CLS]\n            words = words + [self.SPECIAL_TOKEN[\"SEP_TOKEN\"]]\n\n            input_ids = self.tokenizer.convert_tokens_to_ids(words)\n            input_mask = [1] * len(input_ids)\n            segment_ids = [0] * len(input_ids)\n            while len(input_ids) < self.max_words:\n                input_ids.append(0)\n                input_mask.append(0)\n                segment_ids.append(0)\n            assert len(input_ids) == self.max_words\n            assert len(input_mask) == self.max_words\n            assert len(segment_ids) == self.max_words\n\n            pairs_text[i] = np.array(input_ids)\n            pairs_mask[i] = np.array(input_mask)\n            pairs_segment[i] = np.array(segment_ids)\n\n        return pairs_text, pairs_mask, pairs_segment, choice_video_ids\n\n    def _get_rawvideo(self, choice_video_ids, start_time, end_time):\n        video_mask = np.zeros((len(choice_video_ids), self.max_frames), dtype=np.long)\n        max_video_length = [0] * len(choice_video_ids)\n\n        # Pair x L x T x 3 x H x W\n        video = np.zeros((len(choice_video_ids), self.max_frames, 1, 3,\n                          self.rawVideoExtractor.size, self.rawVideoExtractor.size), dtype=np.float)\n\n        for i, video_id in enumerate(choice_video_ids):\n            video_path = self.video_dict[video_id]\n\n            raw_video_data = self.rawVideoExtractor.get_video_data(video_path, start_time, end_time)\n            raw_video_data = raw_video_data['video']\n\n            if len(raw_video_data.shape) > 3:\n                raw_video_data_clip = raw_video_data\n                # L x T x 3 x H x W\n                raw_video_slice = self.rawVideoExtractor.process_raw_data(raw_video_data_clip)\n                if self.max_frames < raw_video_slice.shape[0]:\n                    if self.slice_framepos == 0:\n                        video_slice = raw_video_slice[:self.max_frames, ...]\n                    elif self.slice_framepos == 1:\n                        video_slice = raw_video_slice[-self.max_frames:, ...]\n                    else:\n                        sample_indx = np.linspace(0, raw_video_slice.shape[0] - 1, num=self.max_frames, dtype=int)\n                        video_slice = raw_video_slice[sample_indx, ...]\n                else:\n                    video_slice = raw_video_slice\n\n                video_slice = self.rawVideoExtractor.process_frame_order(video_slice, frame_order=self.frame_order)\n\n                slice_len = video_slice.shape[0]\n                max_video_length[i] = max_video_length[i] if max_video_length[i] > slice_len else slice_len\n                if slice_len < 1:\n                    pass\n                else:\n                    video[i][:slice_len, ...] = video_slice\n            else:\n                print(\"video path: {} error. video id: {}\".format(video_path, video_id))\n\n        for i, v_length in enumerate(max_video_length):\n            video_mask[i][:v_length] = [1] * v_length\n\n        return video, video_mask\n\n    def __getitem__(self, idx):\n        item = self.sentences_dict[idx]\n        pairs_text, pairs_mask, pairs_segment, choice_video_ids = self._get_text(item[\"video_id\"], item[\"sentence\"])\n        video, video_mask = self._get_rawvideo(choice_video_ids, item[\"start_time\"], item[\"end_time\"])\n        negative_videos = []\n        negative_video_masks = []\n        negatives = random.sample(item['negatives'], 10)\n        for neg in negatives:\n            s = math.floor(neg[0])\n            e = math.ceil(neg[1])\n            vn, vmn = self._get_rawvideo(choice_video_ids, s, min(s+50, e))\n            negative_videos.append(vn)\n            negative_video_masks.append(vmn)\n        negative_videos = np.stack(negative_videos, axis=0)\n        negative_video_masks = np.stack(negative_video_masks, axis=0)\n\n        # predictionDataset = PredictionDataset(choice_video_ids, item['negatives'], self._get_rawvideo)\n        # prediction_loader = DataLoader(\n        #     predictionDataset,\n        #     batch_size=10,\n        #     num_workers=10,\n        #     pin_memory=False,\n        #     shuffle=False,\n        # )\n        # negative_batch, negative_mask_batch = next(iter(prediction_loader))\n        #\n        # print(negative_batch.shape, negative_mask_batch.shape)\n\n        return pairs_text, pairs_mask, pairs_segment, video, video_mask, negative_videos, negative_video_masks\n","repo_name":"md-mohaiminul/NLQ","sub_path":"CLIP4Clip/dataloaders/data_loader_ego4d_same_video.py","file_name":"data_loader_ego4d_same_video.py","file_ext":"py","file_size_in_byte":9013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2055630095","text":"from copy import deepcopy\nfrom enum import Enum, auto\nfrom typing import Any\n\ntry:\n    from msvcrt import kbhit, getwch\nexcept ModuleNotFoundError:\n    raise ModuleNotFoundError(\"msvcrt module not found!\\nThis module is windows only!\")\n\n\nclass Get_key_modes(Enum):\n    NO_IGNORE = auto()\n    IGNORE_HORIZONTAL = auto()\n    IGNORE_VERTICAL = auto()\n    IGNORE_ESCAPE = auto()\n    IGNORE_ENTER = auto()\n    IGNORE_ARROWS = auto()\n    ONLY_ARROWS = auto()\n\n\n\nclass Keys(Enum):\n    ESCAPE = 0\n    UP = 1\n    DOWN = 2\n    LEFT = 3\n    RIGHT = 4\n    ENTER = 5\n\n\ndef get_key(mode:Get_key_modes=Get_key_modes.NO_IGNORE, key_map:tuple[list[list[list[str]]], list[str]]|None=None, allow_buffered_inputs=False):\n    \"\"\"\n    Function for detecting a keypress (mainly arrow keys)\\n\n    Returns a value from the `Key` enum depending on the key type.\\n\n    If `allow_buffered_inputs` is `False`, if the user pressed some buttons before this function was called the function will not register those button presses.\\n\n    Depending on the mode, it ignores some keys:\\n\n    \\tNO_IGNORE: don't ignore any key\n    \\tIGNORE_HORIZONTAL: ignore left/right\n    \\tIGNORE_VERTICAL: ignore up/down\n    \\tIGNORE_ESCAPE: ignore escape\n    \\tIGNORE_ENTER: ignore enter\\n\n    You can set custom keys keybinds by providing a key_map:\\n\n    [[a list for each value in the `Key` enum, with each list having 2 lists of keys (the 1. list containing the keys that aren't arrow keys, the 2. containing the ones that are)], [double (arrow) key 1. halfs]]\\n\n    Examles:\\n\n    \\tdefault: ([[[\"\\\\x1b\"]], [[], [\"H\"]], [[], [\"P\"]], [[], [\"K\"]], [[], [\"M\"]], [[\"\\\\r\"]]], [\"\\\\xe0\", \"\\\\x00\"])\n    \\tarrow/WASD: ([[[\"\\\\x1b\", \"e\"]], [[\"w\"], [\"H\"]], [[\"s\"], [\"P\"]], [[\"a\"], [\"K\"]], [[\"d\"], [\"M\"]], [[\"\\\\r\", \" \"]]], [\"\\\\xe0\", \"\\\\x00\"])\n    \\tonly W, A, and D without setting the mode: ([[], [[\"w\"]], [], [[\"a\"]], [[\"d\"]], []])\n    \\tunintended/compressed: ([[\"\\\\x1b\"], [[], \"H\"], [[], \"P\"], [[], \"K\"], [[], \"M\"], [\"\\\\r\"]], \"\\\\xe0\\\\x00\")\n    \"\"\"\n    \n    ignore_list = [Get_key_modes.IGNORE_ESCAPE, Get_key_modes.IGNORE_VERTICAL, Get_key_modes.IGNORE_VERTICAL,\n                   Get_key_modes.IGNORE_HORIZONTAL, Get_key_modes.IGNORE_HORIZONTAL, Get_key_modes.IGNORE_ENTER]\n    response_list = [Keys.ESCAPE, Keys.UP, Keys.DOWN, Keys.LEFT, Keys.RIGHT, Keys.ENTER]\n    \n    if not allow_buffered_inputs:\n        while kbhit():\n            getwch()\n\n    arrow = False\n    if key_map is None:\n        key_map = ([[[\"\\x1b\"]], [[], [\"H\"]], [[], [\"P\"]], [[], [\"K\"]], [[], [\"M\"]], [[\"\\r\"]]], [\"\\xe0\", \"\\x00\"])\n    while True:\n        key = getwch()\n        # print(key)\n        arrow = False\n        if len(key_map) != 1 and len(key_map) > 1 and key in key_map[1]:\n            arrow = True\n            key = getwch()\n            # print(\"arrow\", key)\n        \n        for x, response in enumerate(response_list):\n            if ((not arrow and len(key_map[0][x]) > 0 and key in key_map[0][x][0]) or\n                (arrow and len(key_map[0][x]) > 1 and key in key_map[0][x][1])) and mode != ignore_list[x]:\n                return response\n\n\ndef imput(ask=\"Num: \"):\n    \"\"\"Input but only accepts whole numbers.\"\"\"\n    while True:\n        try: return int(input(ask))\n        except ValueError: print(\"Not a number!\")\n\n\nclass Key_action:\n    \"\"\"\n    Object for `Keybinds` for `get_key_with_obj()`.\\n\n    If the current key_mode is in the `ignore_modes` tuple, the keypress will be ignores if it is a normal key and the key mode is in the first list, or it is an arrow key and the key mode is in the secound list.\\n\n    If a normal list is passed in, it will act as the ignore mode list for both normal and arrow keys. (IGNORE_ARROWS and ONLY_ARROWS is added automatically)\\n\n    Raises `ValueError` if both `normal_keys` and `arrow_keys` are empty lists.\n    \"\"\"\n    def __init__(self, response:Any, normal_keys:list[str]|None=None, arrow_keys:list[str]|None=None, ignore_modes:list[Get_key_modes]|tuple[list[Get_key_modes], list[Get_key_modes]]|None=None):\n        if normal_keys is None:\n            normal_keys = []\n        if arrow_keys is None:\n            arrow_keys = []\n        if len(normal_keys) == 0 and len(arrow_keys) == 0:\n            raise ValueError(\"Both normal_keys and arrow_keys can't be empty lists.\")\n        self.normal_keys = normal_keys\n        self.arrow_keys = arrow_keys\n        if ignore_modes is None:\n            ignore_modes_tuple:tuple[list[Get_key_modes], list[Get_key_modes]] = ([], [])\n        elif type(ignore_modes) is list:\n            ignore_modes_tuple = (deepcopy(ignore_modes), deepcopy(ignore_modes))\n        else:\n            ignore_modes_tuple = (deepcopy(ignore_modes[0]), deepcopy(ignore_modes[1]))\n        ignore_modes_tuple[0].append(Get_key_modes.ONLY_ARROWS)\n        ignore_modes_tuple[1].append(Get_key_modes.IGNORE_ARROWS)\n        self.ignore_modes:tuple[list[Get_key_modes], list[Get_key_modes]] = ignore_modes_tuple\n        self.response = response\n\n\n\nclass Keybinds:\n    \"\"\"\n    Object for `get_key_with_obj()`.\\n\n    Raises `ValueError` if `actions` is an empty list.\n    \"\"\"\n    def __init__(self, actions:list[Key_action], arrow_key_modifiers:list[str]|None=None):\n        if len(actions) == 0:\n            raise ValueError(\"actions can't be an empty list.\")\n        self._actions = actions\n        if arrow_key_modifiers is None:\n            arrow_key_modifiers = []\n        self._arrow_key_modifiers = list(arrow_key_modifiers)\n    \n    \n    def get_result_list(self):\n        \"\"\"\n        Returns the `result_list` for `UI_list` or `options_ui` functions.\n        \"\"\"\n        result_list:list[Any] = []\n        for action in self._actions:\n            result_list.append(action.response)\n        return tuple(result_list)\n\n\n\ndef get_key_with_obj(mode:Get_key_modes|list[Get_key_modes]=Get_key_modes.NO_IGNORE, keybinds:Keybinds|None=None, allow_buffered_inputs=False):\n    \"\"\"\n    Function for detecting one key from a list of keypresses.\\n\n    Depending on the mode, it ignores some keys.\\n\n    Returns the `response` of the `Key_action` object that maches the key the user pressed.\\n\n    If `allow_buffered_inputs` is `False`, if the user pressed some buttons before this function was called the function will not register those button presses.\\n\n    \"\"\"\n    if type(mode) is Get_key_modes:\n        mode_list:list[Get_key_modes] = [mode]\n    else:\n        mode_list = mode\n\n    if keybinds is None:\n        keybinds = Keybinds([\n            Key_action(Keys.ESCAPE, [\"\\x1b\"], [], [Get_key_modes.IGNORE_ESCAPE]),\n            Key_action(Keys.UP, [], [\"H\"], [Get_key_modes.IGNORE_VERTICAL]),\n            Key_action(Keys.DOWN, [], [\"P\"], [Get_key_modes.IGNORE_VERTICAL]),\n            Key_action(Keys.LEFT, [], [\"K\"], [Get_key_modes.IGNORE_HORIZONTAL]),\n            Key_action(Keys.RIGHT, [], [\"M\"], [Get_key_modes.IGNORE_HORIZONTAL]),\n            Key_action(Keys.ENTER, [\"\\r\"], [], [Get_key_modes.IGNORE_ENTER]),\n        ], [\"\\xe0\", \"\\x00\"])\n        \n\n    if not allow_buffered_inputs:\n        while kbhit():\n            getwch()\n\n    arrow = False\n    while True:\n        key = getwch()\n        arrow = False\n        if key in keybinds._arrow_key_modifiers:\n            arrow = True\n            key = getwch()\n        \n        for action in keybinds._actions:\n            ignore = False\n            for mod in mode_list:\n                if mod in action.ignore_modes[int(arrow)]:\n                    ignore = True\n                    break\n            if (not arrow and not ignore and key in action.normal_keys) or (arrow and not ignore and key in action.arrow_keys):\n                return action.response\n","repo_name":"Kalandor01/Save_File_Manager","sub_path":"save_file_manager/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15962580331","text":"from discord.ext import commands\n\n\nclass Help(commands.Cog):\n\n    def __init__(self, client):\n        self.client = client\n\n    @commands.command()\n    async def help(self, ctx):\n        await ctx.send(\n            'You can check out https://github.com/iMeisa/Bloatbot/wiki/Bloatbot-Commands for my command page')\n\n\ndef setup(client):\n    client.add_cog(Help(client))\n","repo_name":"iMeisa/Bloatbot","sub_path":"cogs/Help.py","file_name":"Help.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71248611769","text":"import cv2\r\nimport math\r\nimport numpy as np\r\nfrom operator import itemgetter\r\n\r\nimg = cv2.imread(\"E:/a_computer_design/test_picture/market_door/market_door_1.jpg\")#读取存储路径\r\nimg = cv2.resize(img, (0, 0), fx=0.2, fy=0.2)\r\n\r\nhsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\r\n\r\nkernal_d = np.ones((3,3), np.uint8)\r\nkernal_e = np.ones((3,3), np.uint8)\r\n\r\nlower = np.array([101, 99, 127])\r\nupper = np.array([120, 255, 255])\r\n\r\nmask = cv2.inRange(hsv, lower, upper)\r\nimgblur1 = cv2.GaussianBlur(mask,(3,3),10,10)\r\ncv2.imshow('img_blur', imgblur1)\r\n#img_erosion = cv2.erode(imgblur1, kernal_e, iterations=1)\r\n#cv2.imshow('img_er', img_erosion)\r\ndilate = cv2.dilate(imgblur1, kernal_d, iterations=2)\r\ncv2.imshow('img_di', dilate)\r\n\r\n#imgand = cv2.bitwise_and(img, img, mask=mask)\r\n\r\ncontours, hierarchy = cv2.findContours(dilate, mode=cv2.RETR_EXTERNAL,\r\n                                       method=cv2.CHAIN_APPROX_NONE)\r\n# cv2.drawContours(dilate,contours,-1,(0,255,255),10)\r\n\r\nxlist = []\r\nylist = []\r\narea = 0\r\n\r\nfor cn in contours:\r\n    M = cv2.moments(cn)\r\n    area += cv2.contourArea(cn)\r\n    print('area = ')\r\n    print(area)\r\n    if area > 2000:\r\n        if M[\"m00\"] != 0:\r\n            cX = int(M[\"m10\"] / M[\"m00\"])  # 计算质心\r\n            cY = int(M[\"m01\"] / M[\"m00\"])\r\n            cv2.circle(img, (cX, cY), 7, (255, 0, 255), -1)\r\n            #cv2.putText(img, \"word\", (cX - 20, cY - 20),\r\n            #            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)\r\n            print(cX, cY)\r\n            xlist.append(cX)\r\n            ylist.append(cY)\r\n        # area += cv2.contourArea(cn)\r\n        # print('area = ')\r\n        # print(area)\r\n\r\n\r\n\r\n# cv2.circle(img, (100, 300), 7, (0, 0, 255), -1)\r\n\r\n\r\nz = []\r\nfor i in range(len(xlist)):\r\n    z.append((xlist[i],ylist[i]))\r\n\r\nz = sorted(z)\r\n\r\n#cv2.line(img, (z[7][0], z[7][1]), (z[9][0], z[9][1]), (0, 255, 0), 2)\r\n\r\n\r\ncv2.imshow('img', img)\r\n#cv2.imshow('imghsv', hsv)\r\ncv2.imshow('mask', mask)\r\ncv2.imshow('dilate', dilate)\r\n# print(xlist)\r\nprint(z)\r\n# print(prepareline)\r\n# print(line)\r\ncv2.waitKey(0)","repo_name":"xin887/Loongson","sub_path":"find_real_life.py","file_name":"find_real_life.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33705486772","text":"from django.urls import path,include\nfrom vora import vora_views\nfrom users import users_views, users_api_views\nfrom django.contrib import admin\napp_name = 'vora'\nurlpatterns = [\n  path('admin_base/', admin.site.urls),\n    # added by raj\n    path('api/register', users_api_views.RegisterUserAPIView.as_view()),\n    path('api/update_register_data/<int:pk>/', users_api_views.update_register_dataUserAPIView.as_view()),\n    path('api/checkEmailWithToken', users_api_views.checkEmailWithToken.as_view()),\n    path('api/checkEmailWithSendMail', users_api_views.checkEmailWithSendMail.as_view()),\n    path('api/changePassword/<int:pk>/', users_api_views.changePassword.as_view()),\n    path('api/changeoldtonewPassword/<int:pk>/', users_api_views.changeoldtonewPassword.as_view()),\n    path('api/update_verify_data/<int:pk>/', users_api_views.update_verify_dataUserAPIView.as_view()),\n    \n    # path('loginapi/', users_api_views.LoginView.as_view()),\n    path('api/add-order-detail', users_api_views.CreateOrderAPIView.as_view()),\n    path('api/send_mail', users_api_views.send_mail.as_view()),\npath('api/active_project_email', users_api_views.active_project_email.as_view()),\npath('api/draft_project_email', users_api_views.draft_project_email.as_view()),\npath('api/new_project_email', users_api_views.new_project_email.as_view()),\n\npath('api/confirm_send_mail', users_api_views.confirm_send_mail.as_view()),\n\n    path('back/users/', users_views.users, name=\"users\"),\n    path('back/customers/', users_views.customers, name=\"customers\"),\n    path('back/inactiveCustomers/', users_views.customers, name=\"inactiveCustomers\"),\n    path('back/category/', users_views.category, name=\"category\"),\n    path('back/user_history/', users_views.login_history, name=\"login_history\"),\n    \n    path('back/interest-area/', users_views.interest_area, name=\"interest-area\"),\n    path('back/industry-filter/', users_views.industry_filter, name=\"industry-filter\"),\n    path('back/sub-category/', users_views.sub_category, name=\"sub-category\"),\n    path('back/solutions/', users_views.solutions, name=\"solutions\"),\n    path('back/all-orders/', users_views.all_orders, name=\"all-orders\"),\n    path('back/all-active-orders/', users_views.active_orders, name=\"all-active-orders\"),\n    path('back/all-draft-orders/', users_views.draft_orders, name=\"all-draft-orders\"),\n    path('back/all-complete-orders/', users_views.complete_orders, name=\"all-complete-orders\"),\n    \n    path('back/view-order-detail/<int:id>/', users_views.view_order_solution, name=\"view-order-detail\"),\n    path('back/view-customer-detail/<int:id>/', users_views.view_customer, name=\"view-customer-detail\"),\n    path('back/archieved-solutions/', users_views.archieved_solutions, name=\"archieved-solutions\"),\n    path('back/tax/', users_views.tax, name=\"tax\"),\n\n    path('back/user-details/<int:id>/', users_views.user_details, name=\"user-details\"),\n    path('back/add-user/', users_views.add_user, name=\"add-user\"),\n    path('back/add-category/', users_views.add_category, name=\"add-category\"),\n\n    path('back/add-interest-area/', users_views.add_interest_area, name=\"add-interest-area\"),\n\n    path('back/add-industry-filter/', users_views.add_industry_filter, name=\"add-industry-filter\"),\n\n    path('back/add-solution/', users_views.add_solution, name=\"add-solution\"),\n\n    path('back/add-tax/', users_views.add_tax, name=\"add-tax\"),\n\n    path('back/add-sub-category/', users_views.add_sub_category, name=\"add-sub-category\"),\n\n    path('back/edit-user/<int:id>/', users_views.edit_user, name=\"edit-user\"),\n    path('back/edit-solution/<int:id>/', users_views.edit_solution, name=\"edit-solution\"),\n    path('back/edit-category/<int:id>/', users_views.edit_category, name=\"edit-category\"),\n\n    path('back/edit-interest-area/<int:id>/', users_views.edit_interest_area, name=\"edit-interest-area\"),\n\n    path('back/edit-sub-category/<int:id>/', users_views.edit_sub_category, name=\"edit-sub-category\"),\n    path('back/edit-tax/<int:id>/', users_views.edit_tax, name=\"edit-tax\"),\n    path('back/edit-industry-filter/<int:id>/', users_views.edit_industry_filter, name=\"edit-industry-filter\"),\n\n    path('back/delete-user/<int:id>/', users_views.delete_user, name=\"delete-user\"),\n    path('back/delete-multiple-user/', users_views.delete_multiple_user, name=\"delete-multiple-user\"),\n\n    path('back/delete-solution/<int:id>/', users_views.delete_solution, name=\"delete-solution\"),\n    path('back/change-order-status/<int:id>/', users_views.change_order_status, name=\"change-order-status\"),\n    path('back/active-multiple-solution/', users_views.active_multiple_solution, name=\"active-multiple-solution\"),\n    path('back/delete-category/<int:id>/', users_views.delete_category, name=\"delete-category\"),\n    path('back/delete-industry-filter/<int:id>/', users_views.delete_industry_filter, name=\"delete-industry-filter\"),\n\n    path('back/delete-interest-area/<int:id>/', users_views.delete_interest_area, name=\"delete-interest-area\"),\n\n    path('back/delete-sub-category/<int:id>/', users_views.delete_sub_category, name=\"delete-sub-category\"),\n    path('back/delete-tax/<int:id>/', users_views.delete_tax, name=\"delete-tax\"),\n\n    path('back/delete-multiple-category/', users_views.delete_multiple_category, name=\"delete-multiple-category\"),\n    path('back/delete-multiple-industry-filter/', users_views.delete_multiple_industry_filter,\n         name=\"delete-multiple-industry-filter\"),\n\n    path('back/delete-multiple-interest-area/', users_views.delete_multiple_interest_area,\n         name=\"delete-multiple-interest-area\"),\n\n    path('delete-multiple-sub-category/', users_views.delete_multiple_sub_category,\n         name=\"delete-multiple-sub-category\"),\n    path('delete-multiple-tax/', users_views.delete_multiple_tax, name=\"delete-multiple-tax\"),\n\n    path('admin_login/', users_views.login_user, name=\"login\"),\n    path('back/logout/', users_views.logout_user, name=\"logout\"),\n    path('back/groups/', users_views.groups_list, name=\"groups\"),\n    path('back/group-edit/<int:id>/', users_views.group_edit, name=\"group-edit\"),\n    path('back/group-delete/<int:id>/', users_views.group_delete, name=\"group-delete\"),\n    path('back/group-add/', users_views.group_add, name=\"group-add\"),\n    path('back/permissions/', users_views.permissions, name=\"permissions\"),\n    path('back/edit-permissions/<int:id>/', users_views.edit_permissions, name=\"edit-permissions\"),\n    path('back/delete-permissions/<int:id>/', users_views.delete_permissions, name=\"delete-permissions\"),\n    path('back/assign-permissions-to-user/<int:id>/', users_views.assign_permissions_to_user,\n         name=\"assign-permissions-to-user\"),\n    path('back/signup/', users_views.signup, name=\"signup\"),\n    path('back/activate/<uidb64>/<token>/', users_views.activate, name='activate'),\n\n#    path('', vora_views.index, name=\"index\"),\n    path('back/index/', vora_views.index, name=\"index\"),\n    path('back/index_2/', vora_views.index_2, name=\"index_2\"),\n    path('back/projects/', vora_views.projects, name=\"projects\"),\n    path('back/contacts/', vora_views.contacts, name=\"contacts\"),\n    path('back/kanban/', vora_views.kanban, name=\"kanban\"),\n\n    path('back/calendar/', vora_views.calendar, name=\"calendar\"),\n    path('back/messages/', vora_views.messages, name=\"messages\"),\n\n    path('back/app-profile/', vora_views.app_profile, name=\"app-profile\"),\n    path('back/post-details/', vora_views.post_details, name=\"post-details\"),\n    path('back/email-compose/', vora_views.email_compose, name=\"email-compose\"),\n    path('back/email-inbox/', vora_views.email_inbox, name=\"email-inbox\"),\n    path('back/email-read/', vora_views.email_read, name=\"email-read\"),\n    path('back/app-calender/', vora_views.app_calender, name=\"app-calender\"),\n    path('back/ecom-product-grid/', vora_views.ecom_product_grid, name=\"ecom-product-grid\"),\n    path('back/ecom-product-list/', vora_views.ecom_product_list, name=\"ecom-product-list\"),\n    path('back/ecom-product-detail/', vora_views.ecom_product_detail, name=\"ecom-product-detail\"),\n    path('back/ecom-product-order/', vora_views.ecom_product_order, name=\"ecom-product-order\"),\n    path('back/ecom-checkout/', vora_views.ecom_checkout, name=\"ecom-checkout\"),\n    path('back/ecom-invoice/', vora_views.ecom_invoice, name=\"ecom-invoice\"),\n    path('back/ecom-customers/', vora_views.ecom_customers, name=\"ecom-customers\"),\n    path('back/chart-flot/', vora_views.chart_flot, name=\"chart-flot\"),\n    path('back/chart-morris/', vora_views.chart_morris, name=\"chart-morris\"),\n    path('back/chart-chartjs/', vora_views.chart_chartjs, name=\"chart-chartjs\"),\n    path('back/chart-chartist/', vora_views.chart_chartist, name=\"chart-chartist\"),\n    path('back/chart-sparkline/', vora_views.chart_sparkline, name=\"chart-sparkline\"),\n    path('back/chart-peity/', vora_views.chart_peity, name=\"chart-peity\"),\n\n    path('back/ui-accordion/', vora_views.ui_accordion, name=\"ui-accordion\"),\n    path('back/ui-alert/', vora_views.ui_alert, name=\"ui-alert\"),\n    path('back/ui-badge/', vora_views.ui_badge, name=\"ui-badge\"),\n    path('back/ui-button/', vora_views.ui_button, name=\"ui-button\"),\n    path('back/ui-modal/', vora_views.ui_modal, name=\"ui-modal\"),\n    path('back/ui-button-group/', vora_views.ui_button_group, name=\"ui-button-group\"),\n    path('back/ui-list-group/', vora_views.ui_list_group, name=\"ui-list-group\"),\n    path('back/ui-media-object/', vora_views.ui_media_object, name=\"ui-media-object\"),\n    path('back/ui-card/', vora_views.ui_card, name=\"ui-card\"),\n    path('back/ui-carousel/', vora_views.ui_carousel, name=\"ui-carousel\"),\n    path('back/ui-dropdown/', vora_views.ui_dropdown, name=\"ui-dropdown\"),\n    path('back/ui-popover/', vora_views.ui_popover, name=\"ui-popover\"),\n    path('back/ui-progressbar/', vora_views.ui_progressbar, name=\"ui-progressbar\"),\n    path('back/ui-tab/', vora_views.ui_tab, name=\"ui-tab\"),\n    path('back/ui-typography/', vora_views.ui_typography, name=\"ui-typography\"),\n    path('back/ui-pagination/', vora_views.ui_pagination, name=\"ui-pagination\"),\n    path('back/ui-grid/', vora_views.ui_grid, name=\"ui-grid\"),\n\n    path('back/uc-select2/', vora_views.uc_select2, name=\"uc-select2\"),\n    path('back/uc-nestable/', vora_views.uc_nestable, name=\"uc-nestable\"),\n    path('back/uc-noui-slider/', vora_views.uc_noui_slider, name=\"uc-noui-slider\"),\n    path('back/uc-sweetalert/', vora_views.uc_sweetalert, name=\"uc-sweetalert\"),\n    path('back/uc-toastr/', vora_views.uc_toastr, name=\"uc-toastr\"),\n    path('back/map-jqvmap/', vora_views.map_jqvmap, name=\"map-jqvmap\"),\n    path('back/uc-lightgallery/', vora_views.uc_lightgallery, name=\"uc-lightgallery\"),\n    path('back/uc-lightgallery/', vora_views.uc_lightgallery, name=\"uc-lightgallery\"),\n    path('back/widget-basic/', vora_views.widget_basic, name=\"widget-basic\"),\n\n    path('back/form-element/', vora_views.form_element, name=\"form-element\"),\n    path('back/form-wizard/', vora_views.form_wizard, name=\"form-wizard\"),\n    path('back/form-editor-ckeditor/', vora_views.form_editor_ckeditor, name=\"form-editor-ckeditor\"),\n    path('back/form-pickers/', vora_views.form_pickers, name=\"form-pickers\"),\n    path('back/form-validation/', vora_views.form_validation, name=\"form-validation\"),\n\n    path('back/table-bootstrap-basic/', vora_views.table_bootstrap_basic, name=\"table-bootstrap-basic\"),\n    path('back/table-datatable-basic/', vora_views.table_datatable_basic, name=\"table-datatable-basic\"),\n\n    path('back/page-lock-screen/', vora_views.page_lock_screen, name=\"page-lock-screen\"),\n    path('back/page-error-400/', vora_views.page_error_400, name=\"page-error-400\"),\n    path('back/page-error-403/', vora_views.page_error_403, name=\"page-error-403\"),\n    path('back/page-error-404/', vora_views.page_error_404, name=\"page-error-404\"),\n    path('back/page-error-500/', vora_views.page_error_500, name=\"page-error-500\"),\n    path('back/page-error-503/', vora_views.page_error_503, name=\"page-error-503\"),\n]\n","repo_name":"kerav-3657/django_flwk","sub_path":"dashboard/vora/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":11956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9857716905","text":"\n\n\n#\n# Important note the order from gameinfo IS preserved!\n#\n\n# important todo: this is not very realiable. There are many stupid assumptions\ndef gameinfo_paths_lookup(cl):\n\tfrom pathlib import Path\n\timport json\n\tfrom ...utils.lizard_tail.lizard_tail import lizard_tail\n\tfrom ...utils.shared import app_command_send, where_addon_root\n\t# first evaluate gameinfo\n\tgminfo = lizard_tail(Path(cl).read_text())\n\t\n\t# ASSUME that gameinfo lays in the root of the client (as it fucking has to)\n\tclient = Path(cl).parent\n\n\t# print(gminfo.search_paths)\n\t# store resulting shit here\n\tresult = {\n\t\t'content_folders': {\n\t\t\t'materials': [],\n\t\t\t'models': [],\n\t\t\t'scripts': [],\n\t\t\t'sound': [],\n\t\t\t'resource': [],\n\t\t\t'cfg': []\n\t\t}\n\t}\n\n\tlook_for_vpks_here = []\n\t# process entries one by one\n\t# find places to look for vpks at\n\tfor entry in gminfo.search_paths:\n\t\t# the path entry of this key\n\t\trawpath = entry['value']\n\n\t\t# it could be that the path is absolute. Account for that\n\t\tif '.vpk' in rawpath:\n\t\t\ttrypath = Path(rawpath.replace('.vpk', '_dir.vpk'))\n\t\t\tif trypath.is_file():\n\t\t\t\tlook_for_vpks_here.append(trypath)\n\t\t\t\tcontinue\n\n\t\t# base path\n\t\tbasepath = client\n\t\t\n\t\t# if this is present in the entry - go up one directory\n\t\t# every other case means that the base path is the gameinfo path\n\t\tif '|all_source_engine_paths|' in rawpath.lower():\n\t\t\tbasepath = client.parent\n\n\t\t# count how many times do we have to go up\n\t\tup_parents = rawpath.count('../')\n\n\t\t# if not zero - go up\n\t\tif up_parents != 0:\n\t\t\t# minues one because zero counts too\n\t\t\tbasepath = basepath.parents[up_parents - 1]\n\n\t\t# now join the base path and the entry path\n\t\tjoined_path = basepath / rawpath.lower().replace('|gameinfo_path|', '').replace('|all_source_engine_paths|', '').replace('../', '')\n\t\tprint(joined_path)\n\t\tlook_for_vpks_here.append(joined_path)\n\n\tprint('break')\n\t\n\t# Now process all the paths and treat them accordingly\n\tfound_vpks = []\n\tfor lookpath in look_for_vpks_here:\n\t\t# if it's an absolute path - just add it right away\n\t\tif lookpath.is_file():\n\t\t\tfound_vpks.append(lookpath)\n\t\t\tcontinue\n\n\t\t# if it wasnt an absoulte path in the first place, but became one after joining\n\t\tlate_abs = lookpath.parent / lookpath.name.replace('.vpk', '_dir.vpk')\n\t\tif late_abs.is_file():\n\t\t\tfound_vpks.append(late_abs)\n\t\t\tcontinue\n\n\t\t# if it's a directory WITHOUT /* - scan it NOT recursively\n\t\tif lookpath.is_dir():\n\t\t\tfor globvpk in lookpath.glob('*.vpk'):\n\t\t\t\tif '_dir.vpk' in globvpk.name.lower():\n\t\t\t\t\tfound_vpks.append(globvpk)\n\t\t\t\n\t\t\t#\n\t\t\t# Look for content folders\n\t\t\t#\n\t\t\tfor cf in result['content_folders']:\n\t\t\t\tif (lookpath / cf).is_dir():\n\t\t\t\t\tresult['content_folders'][cf].append((lookpath / cf))\n\n\t\t\tcontinue\n\n\t\t# if it's a directory WITH /* - scan it Recursively\n\t\tif lookpath.name.strip() == '*' and lookpath.parent.is_dir():\n\t\t\tfor globvpk in lookpath.parent.rglob('*.vpk'):\n\t\t\t\tif '_dir.vpk' in globvpk.name.lower():\n\t\t\t\t\tfound_vpks.append(globvpk)\n\t\t\t\n\t\t\tcflook = [sex for sex in lookpath.parent.rglob('*') if sex.is_dir()]\n\t\t\t\n\t\t\t#\n\t\t\t# Look for content folders\n\t\t\t#\n\t\t\tfor cf in cflook:\n\t\t\t\t# a content folder only counts if there's no folder with the same name up its parent tree\n\t\t\t\t# RELATIVE to the base look path\n\t\t\t\trelative = cf.relative_to(lookpath.parent)\n\t\t\t\tif cf.name in result['content_folders'] and not cf in [cfname.name for cfname in relative.parents]:\n\t\t\t\t\tresult['content_folders'][cf].append(lookpath.parent / relative)\n\n\t\t\tcontinue\n\n\t#\n\t# Быть или не быть: Сразу делать словарь или потом удалять дубликаты из массива? У массивов сиськи большие... :(\n\t#\n\tfound_vpks = list(dict.fromkeys(found_vpks))\n\tresult['vpks'] = found_vpks\n\n\t# remove content folder duplicates\n\tfor rmdupli in result['content_folders']:\n\t\tresult['content_folders'][rmdupli] = list(dict.fromkeys(result['content_folders'][rmdupli]))\n\n\treturn result\n\n\n# imporant: this is hardcoded to work with the reuslt of gameinfo_paths_lookup\n# for now, thr whole chain past gameinfo_paths_lookup is hardcoded to work as a system to show skyboxes in the skyboxer app\n\n# takes gameinfo location\n# and path to the engine folder with with bin (vpk.exe n shit)\ndef find_skyboxes(gminfopath):\n\timport subprocess, json\n\tfrom pathlib import Path\n\tfrom ...utils.lizard_tail.lizard_tail import lizard_tail\n\tfrom ...utils.shared import app_command_send, where_addon_root\n\taddon_root_dir = where_addon_root(__file__)\n\n\tlookup_paths = gameinfo_paths_lookup(gminfopath['gameinfo'])\n\tapp_command_send({\n\t\t'app_module': 'echo_status',\n\t\t'mod_action': '',\n\t\t'payload': str(lookup_paths)\n\t})\n\tvpkexe = addon_root_dir / 'bins' / 'vpk' / 'vpk.exe'\n\n\tfound_skyboxes = []\n\n\t# first - lookup vpks\n\tfor vpk in lookup_paths['vpks']:\n\t\t# parameters to dump the contents of the vpk\n\t\tvpk_exe_prms = [\n\t\t\t# vpk exexutable\n\t\t\tstr(vpkexe),\n\t\t\t# l = list contents. Fun fact: it's blazing fast\n\t\t\t'l',\n\t\t\t# vpk location\n\t\t\tstr(vpk)\n\t\t]\n\n\t\t# get list of all the files in vpk\n\t\tvpkexe_output = subprocess.run(vpk_exe_prms, capture_output=True)\n\t\t# flist = [Path(file.strip()) for file in vpkexe_output.stdout.decode().split('\\n') if not 'models/' in file and not 'sounds/' in file and not '.vmt' in file]\n\t\t\n\t\t# important todo: make an extremely rude assumption that all skyboxes are stored in the skybox folder\n\t\tflist = [Path(file.strip()) for file in vpkexe_output.stdout.decode().split('\\n') if 'skybox/' in file]\n\t\tapp_command_send({\n\t\t\t'app_module': 'echo_status',\n\t\t\t'mod_action': '',\n\t\t\t'payload': len(flist)\n\t\t})\n\n\t\tcsides = [\n\t\t\t'ft',\n\t\t\t'lf',\n\t\t\t'bk',\n\t\t\t'up',\n\t\t\t'rt'\n\t\t]\n\n\t\thdr_dict = [\n\t\t\t'hdr',\n\t\t\t'hdr-',\n\t\t\t'hdr_',\n\t\t\t'hdr ',\n\t\t]\n\n\t\tldrfall_dict = [\n\t\t\t'',\n\t\t\t'-',\n\t\t\t'_',\n\t\t\t' ',\n\n\t\t\t'ldr',\n\t\t\t'-ldr',\n\t\t\t'_ldr',\n\t\t\t' ldr'\n\t\t]\n\n\t\tregistry = [None]\n\n\t\t# some skies may not have the bottom\n\t\tfor sky in flist:\n\t\t\t# tiny little filter\n\t\t\tif 'bk.vtf' in sky.name.lower():\n\t\t\t\tclear_name = sky.name.replace('bk.vtf', '')\n\t\t\t\t# print('processing', clear_name)\n\t\t\t\tfolder = sky.parent\n\n\t\t\t\t# if this skybox is in the registry - continue\n\t\t\t\t# if (folder / clear_name) in registry:\n\t\t\t\t# \tcontinue\n\n\n\t\t\t\t# check if all sides are actually there no matter the HDR whatsoever\n\t\t\t\teligible = True\n\t\t\t\tfor side in csides:\n\t\t\t\t\tif not (folder / (clear_name + side + '.vtf')) in flist:\n\t\t\t\t\t\teligible = False\n\t\t\t\t\t\tbreak\n\t\t\t\tif eligible == False:\n\t\t\t\t\tcontinue\n\n\n\t\t\t\thdr_path = None\n\t\t\t\tldr_path = None\n\n\t\t\t\t# all sides are there, good\n\t\t\t\t# now check if it is OR has HDR\n\t\t\t\thdrhits = 0\n\t\t\t\thdr_ns = None\n\n\t\t\t\t#\n\t\t\t\t# check if it's HDR\n\t\t\t\t#\n\t\t\t\tfor side in csides:\n\t\t\t\t\t# check for all naming schemas per side\n\t\t\t\t\tfor ns in hdr_dict:\n\t\t\t\t\t\t# there have to be 5 hits in total\n\t\t\t\t\t\tif (folder / (clear_name.rstrip(ns) + ns + side + '.vtf')) in flist:\n\t\t\t\t\t\t\t# We have a hit. Write down naming schema and check next side\n\t\t\t\t\t\t\thdrhits += 1\n\t\t\t\t\t\t\t# write down naming schema\n\t\t\t\t\t\t\thdr_ns = ns\n\t\t\t\t\t\t\tbreak\n\t\t\t\tishdr = True if hdrhits >= 5 else False\n\t\t\t\tprint(clear_name, ishdr)\n\n\t\t\t\t# if it's not HDR, but eligible - we found an LDR version of this skybox\n\t\t\t\t# now check if it has an HDR version\n\t\t\t\t# check for all naming schemas per side\n\t\t\t\tldr_ns = None\n\t\t\t\thdrhits = 0\n\t\t\t\tif ishdr == False:\n\t\t\t\t\t# check if every side is present\n\t\t\t\t\tfor side in csides:\n\t\t\t\t\t\t# for every HDR schema\n\t\t\t\t\t\tfor hs in hdr_dict:\n\t\t\t\t\t\t\t# check every LDR schema\n\t\t\t\t\t\t\tfor ld in ldrfall_dict:\n\t\t\t\t\t\t\t\t# there have to be 5 hits in total\n\t\t\t\t\t\t\t\t# add ldr schema --> add hdr schema --> check\n\t\t\t\t\t\t\t\tif (folder / (clear_name + ld + hs + side + '.vtf')) in flist:\n\t\t\t\t\t\t\t\t\thdrhits += 1\n\t\t\t\t\t\t\t\t\t# write down naming schema\n\t\t\t\t\t\t\t\t\tldr_ns = ld + hs\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\thas_hdr = True if hdrhits >= 5 else False\n\t\t\t\t\t# if not all sides are present - write down skybox as LDR only and proceed to the next skybox\n\t\t\t\t\thd_path = (folder / (clear_name + str(ldr_ns))).as_posix() if has_hdr == True else None\n\t\t\t\t\tld_path = (folder / clear_name).as_posix()\n\t\t\t\t\t\n\t\t\t\t\tif ld_path in registry or (hd_path in registry and None in registry):\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tfound_skyboxes.append({\n\t\t\t\t\t\t'hdr': hd_path,\n\t\t\t\t\t\t'ldr': ld_path,\n\t\t\t\t\t\t'vpk': True,\n\t\t\t\t\t\t'vpk_path': str(vpk)\n\t\t\t\t\t})\n\t\t\t\t\tregistry.append(ld_path)\n\t\t\t\t\tregistry.append(hd_path)\n\n\t\t\t\t\tcontinue\n\n\n\t\t\t\t#\n\t\t\t\t# if it IS HDR - we found the HDR, now try finding LDR fallback\n\t\t\t\t#\n\t\t\t\thas_ldr_fallback = False\n\t\t\t\tif ishdr == True:\n\t\t\t\t\tldr_fallback_hits = 0\n\t\t\t\t\tfor side in csides:\n\t\t\t\t\t\t# compare against LDR naming schemas\n\t\t\t\t\t\tfor lds in ldrfall_dict:\n\t\t\t\t\t\t\t# strip HDR schema --> strip LDR schema --> check\n\t\t\t\t\t\t\tif (folder / (clear_name.rstrip(hdr_ns).rstrip(lds) + side + '.vtf')) in flist:\n\t\t\t\t\t\t\t\t# set schema\n\t\t\t\t\t\t\t\tldr_ns = lds\n\t\t\t\t\t\t\t\t# add hit\n\t\t\t\t\t\t\t\tldr_fallback_hits += 1\n\t\t\t\t\thas_ldr_fallback = True if ldr_fallback_hits >= 5 else False\n\n\t\t\t\t\thd_path = (folder / clear_name).as_posix()\n\t\t\t\t\tld_path = (folder / (clear_name.strip(hdr_ns).strip(ldr_ns))).as_posix() if has_ldr_fallback == True else None\n\t\t\t\t\t\n\t\t\t\t\tif ld_path in registry or (hd_path in registry and None in registry):\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t# we found an HDR skybox with LDR fallback\n\t\t\t\t\tfound_skyboxes.append({\n\t\t\t\t\t\t'hdr': hd_path,\n\t\t\t\t\t\t'ldr': ld_path,\n\t\t\t\t\t\t'vpk': True,\n\t\t\t\t\t\t'vpk_path': str(vpk)\n\t\t\t\t\t})\n\t\t\t\t\tregistry.append(ld_path)\n\t\t\t\t\tregistry.append(hd_path)\n\t\t\t\t\tcontinue\n\n\n\n\n\n\n\n\n\t# folders have priority over vpks\n\tregistry = [None]\n\t# then - lookup folders\n\tfor matfolder in lookup_paths['content_folders']['materials']:\n\t\tmatfolder = Path(matfolder)\n\t\tfor rawsky in matfolder.rglob('*.vtf'):\n\t\t\t# tiny little filter\n\t\t\tif 'bk.vtf' in rawsky.name.lower():\n\t\t\t\tclear_name = rawsky.name.replace('bk.vtf', '')\n\t\t\t\t# print('processing', clear_name)\n\t\t\t\tfolder = rawsky.parent\n\n\n\t\t\t\t# if this skybox is in the registry - continue\n\t\t\t\tif (folder / clear_name) in registry:\n\t\t\t\t\tcontinue\n\t\t\t\tregistry.append(folder / clear_name)\n\n\t\t\t\t# check if all sides are actually there no matter the HDR whatsoever\n\t\t\t\teligible = True\n\t\t\t\tfor side in csides:\n\t\t\t\t\tif not (folder / (clear_name + side + '.vtf')).is_file():\n\t\t\t\t\t\teligible = False\n\t\t\t\t\t\tbreak\n\t\t\t\tif eligible == False:\n\t\t\t\t\tcontinue\n\n\n\t\t\t\thdr_path = None\n\t\t\t\tldr_path = None\n\n\t\t\t\t# all sides are there, good\n\t\t\t\t# now check if it is OR has HDR\n\t\t\t\thdrhits = 0\n\t\t\t\thdr_ns = None\n\n\t\t\t\t#\n\t\t\t\t# check if it's HDR\n\t\t\t\t#\n\t\t\t\tfor side in csides:\n\t\t\t\t\t# check for all naming schemas per side\n\t\t\t\t\tfor ns in hdr_dict:\n\t\t\t\t\t\t# there have to be 5 hits in total\n\t\t\t\t\t\tif (folder / (clear_name.rstrip(ns) + ns + side + '.vtf')).is_file():\n\t\t\t\t\t\t\t# We have a hit. Write down naming schema and check next side\n\t\t\t\t\t\t\thdrhits += 1\n\t\t\t\t\t\t\t# write down naming schema\n\t\t\t\t\t\t\thdr_ns = ns\n\t\t\t\t\t\t\tbreak\n\t\t\t\tishdr = True if hdrhits >= 5 else False\n\t\t\t\tprint(clear_name, ishdr)\n\n\t\t\t\t# if it's not HDR, but eligible - we found an LDR version of this skybox\n\t\t\t\t# now check if it has an HDR version\n\t\t\t\t# check for all naming schemas per side\n\t\t\t\tldr_ns = None\n\t\t\t\thdrhits = 0\n\t\t\t\tif ishdr == False:\n\t\t\t\t\t# check if every side is present\n\t\t\t\t\tfor side in csides:\n\t\t\t\t\t\t# for every HDR schema\n\t\t\t\t\t\tfor hs in hdr_dict:\n\t\t\t\t\t\t\t# check every LDR schema\n\t\t\t\t\t\t\tfor ld in ldrfall_dict:\n\t\t\t\t\t\t\t\t# there have to be 5 hits in total\n\t\t\t\t\t\t\t\t# add ldr schema --> add hdr schema --> check\n\t\t\t\t\t\t\t\tif (folder / (clear_name + ld + hs + side + '.vtf')).is_file():\n\t\t\t\t\t\t\t\t\thdrhits += 1\n\t\t\t\t\t\t\t\t\t# write down naming schema\n\t\t\t\t\t\t\t\t\tldr_ns = ld + hs\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\thas_hdr = True if hdrhits >= 5 else False\n\t\t\t\t\t# if not all sides are present - write down skybox as LDR only and proceed to the next skybox\n\n\t\t\t\t\thd_path = (folder / (clear_name + str(ldr_ns))).as_posix() if has_hdr == True else None\n\t\t\t\t\tld_path = (folder / clear_name).as_posix()\n\t\t\t\t\t\n\t\t\t\t\tif ld_path in registry or (hd_path in registry and None in registry):\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tfound_skyboxes.append({\n\t\t\t\t\t\t'hdr': hd_path,\n\t\t\t\t\t\t'ldr': ld_path,\n\t\t\t\t\t\t'vpk': False,\n\t\t\t\t\t\t'vpk_path': str(vpk)\n\t\t\t\t\t})\n\t\t\t\t\tregistry.append(ld_path)\n\t\t\t\t\tregistry.append(hd_path)\n\t\t\t\t\tcontinue\n\n\n\t\t\t\t#\n\t\t\t\t# if it IS HDR - we found the HDR, now try finding LDR fallback\n\t\t\t\t#\n\t\t\t\thas_ldr_fallback = False\n\t\t\t\tif ishdr == True:\n\t\t\t\t\tldr_fallback_hits = 0\n\t\t\t\t\tfor side in csides:\n\t\t\t\t\t\t# compare against LDR naming schemas\n\t\t\t\t\t\tfor lds in ldrfall_dict:\n\t\t\t\t\t\t\t# strip HDR schema --> strip LDR schema --> check\n\t\t\t\t\t\t\tif (folder / (clear_name.rstrip(hdr_ns).rstrip(lds) + side + '.vtf')).is_file():\n\t\t\t\t\t\t\t\t# set schema\n\t\t\t\t\t\t\t\tldr_ns = lds\n\t\t\t\t\t\t\t\t# add hit\n\t\t\t\t\t\t\t\tldr_fallback_hits += 1\n\t\t\t\t\thas_ldr_fallback = True if ldr_fallback_hits >= 5 else False\n\n\t\t\t\t\thd_path = (folder / clear_name).as_posix()\n\t\t\t\t\tld_path = (folder / (clear_name.strip(hdr_ns).strip(ldr_ns))).as_posix() if has_ldr_fallback == True else None\n\t\t\t\t\t\n\t\t\t\t\tif ld_path in registry or (hd_path in registry and None in registry):\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t# we found an HDR skybox with LDR fallback\n\t\t\t\t\tfound_skyboxes.append({\n\t\t\t\t\t\t'hdr': hd_path,\n\t\t\t\t\t\t'ldr': ld_path,\n\t\t\t\t\t\t'vpk': False\n\t\t\t\t\t})\n\t\t\t\t\tregistry.append(ld_path)\n\t\t\t\t\tregistry.append(hd_path)\n\t\t\t\t\tcontinue\n\n\n\tprint('Found all skyboxes')\n\treturn found_skyboxes\n\n\n\n\n# continuation of the chain\n# takes skybox meta as an input generated by find_skyboxes\n# returns bitmaps\ndef load_sky_bitmap(skyinfo):\n\timport subprocess, base64, json\n\tfrom pathlib import Path \n\tfrom ...utils.lizard_tail.lizard_tail import lizard_tail\n\tfrom ...utils.shared import app_command_send, where_addon_root\n\taddon_root_dir = where_addon_root(__file__)\n\n\tmagix = addon_root_dir / 'bins' / 'imgmagick' / 'magick.exe'\n\tffmpeg = addon_root_dir / 'bins' / 'ffmpeg' / 'bin' / 'ffmpeg.exe'\n\n\t# todo: this is ungly\n\tskyinfo = skyinfo['skyinfo']\n\n\tvpkexe = addon_root_dir / 'bins' / 'vpk' / 'vpk.exe'\n\tvtfcmdexe = addon_root_dir / 'bins' / 'vtf_cmd' / 'bin' / 'x64' / 'VTFCmd.exe'\n\text_dir = vpkexe.parent\n\tskysides = [\n\t\t'ft',\n\t\t'lf',\n\t\t'bk',\n\t\t'up',\n\t\t'rt',\n\t\t'dn'\n\t]\n\n\thl_dict = [\n\t\t'ldr',\n\t\t'hdr'\n\t]\n\n\tresult_sky = {}\n\n\t#\n\t# again, start with vpks\n\t#\n\tif skyinfo['vpk'] == True:\n\n\t\t#\n\t\t# create folder structure\n\t\t#\n\n\t\t# create hdr, if any\n\t\tif skyinfo['hdr'] != None:\n\t\t\t(ext_dir / Path(skyinfo['hdr']).parent).mkdir(parents=True, exist_ok=True)\n\t\t# create ldr, if any\n\t\tif skyinfo['ldr'] != None:\n\t\t\t(ext_dir / Path(skyinfo['ldr']).parent).mkdir(parents=True, exist_ok=True)\n\n\t\t#\n\t\t# Extract files one by one, because apparently vpk.exe crashes when it cannot fine a file\n\t\t#\n\t\tvpk_exe_prms = [\n\t\t\t# vpk exexutable\n\t\t\tstr(vpkexe),\n\t\t\t# x = extract content\n\t\t\t# important: before extracting the file - it's required to have the folder structre leading to that file\n\t\t\t'x',\n\t\t\t# vpk location\n\t\t\tstr(skyinfo['vpk_path']),\n\t\t\tNone\n\t\t]\n\t\tvtfcmd_prms = [\n\t\t\t# vtfcmd exexutable\n\t\t\tstr(vtfcmdexe),\n\t\t\t# this is required\n\t\t\t'-file',\n\t\t\t# vtf location\n\t\t\tNone\n\t\t]\n\n\t\tfor extr in skysides:\n\t\t\t# try extracting HDR\n\t\t\tvpk_exe_prms[-1] = str(Path(str(skyinfo['hdr']) + extr + '.vtf'))\n\t\t\t# execute extraction\n\t\t\textr_echo = subprocess.run(vpk_exe_prms, capture_output=True, cwd=str(ext_dir))\n\t\t\tprint(extr_echo.stdout)\n\n\n\t\t\t# try extracting LDR\n\t\t\tvpk_exe_prms[-1] = str(Path(str(skyinfo['ldr']) + extr + '.vtf'))\n\t\t\t# execute extraction\n\t\t\textr_echo = subprocess.run(vpk_exe_prms, capture_output=True, cwd=str(ext_dir))\n\t\t\tprint(extr_echo.stdout)\n\n\t\t#\n\t\t# now that files are extracted - convert them to tga with vtfcmd and store in memory as base64\n\t\t#\n\t\tfor svtf in skysides:\n\t\t\t# try both HDR and LDR for a side\n\t\t\tfor hdld in hl_dict:\n\t\t\t\t# only proceed if hdr/ldr exists\n\t\t\t\tif skyinfo[hdld] == None:\n\t\t\t\t\tcontinue\n\n\t\t\t\t# important todo: Do NOT convert both HDR and LDR for previews\n\n\t\t\t\t# construct the path to vtf\n\t\t\t\ttgt_vtf = (ext_dir / Path(skyinfo[hdld] + svtf + '.vtf'))\n\t\t\t\t# only proceed further if this vtf exists\n\t\t\t\tif not tgt_vtf.is_file():\n\t\t\t\t\tresult_sky[svtf + '_' + hdld] = None\n\t\t\t\t\tcontinue\n\t\t\t\tprint('vtf file exists')\n\t\t\t\tvtfcmd_prms[-1] = str(tgt_vtf)\n\t\t\t\tvtf_conv_echo = subprocess.run(vtfcmd_prms, capture_output=True)\n\n\t\t\t\t# conversion done. Check success by checking the file existence\n\t\t\t\tif tgt_vtf.with_suffix('.tga').is_file():\n\t\t\t\t\t# convert shite with magick\n\t\t\t\t\tmagix_prms = [\n\t\t\t\t\t\tstr(magix),\n\t\t\t\t\t\tstr(tgt_vtf.with_suffix('.tga')),\n\n\t\t\t\t\t\t# webp parameters\n\t\t\t\t\t\t'-quality', '0',\n\t\t\t\t\t\t'-define', 'webp:lossless=true',\n\t\t\t\t\t\t'-define', 'webp:partition-limit=0',\n\t\t\t\t\t\t'-define', 'webp:thread-level=1',\n\t\t\t\t\t\tstr('webp:')\n\t\t\t\t\t]\n\n\t\t\t\t\t# ffmpeg is 10 times faster\n\t\t\t\t\tffmpeg_prms = [\n\t\t\t\t\t\t# mpeg\n\t\t\t\t\t\tstr(ffmpeg),\n\t\t\t\t\t\t# input\n\t\t\t\t\t\t'-i', str(tgt_vtf.with_suffix('.tga')),\n\n\t\t\t\t\t\t# resize\n\t\t\t\t\t\t# '-hwaccel', 'cuda',\n\t\t\t\t\t\t# '-hwaccel_output_format', 'cuda',\n\t\t\t\t\t\t# '--enable-nvenc',\n\t\t\t\t\t\t# '--enable-ffnvcodec',\n\t\t\t\t\t\t# '-h', 'encoder=h264_nvenc',\n\t\t\t\t\t\t\n\t\t\t\t\t\t# does nothing\n\t\t\t\t\t\t# (it works, but it's only for videos)\n\t\t\t\t\t\t'-vcodec', 'h264_nvenc',\n\t\t\t\t\t\t\n\t\t\t\t\t\t# change size\n\t\t\t\t\t\t# this will upscale sometimes\n\t\t\t\t\t\t# '-vf', 'scale=500:-1',\n\n\t\t\t\t\t\t# while this is smart\n\t\t\t\t\t\t# this is quite a hires preview\n\t\t\t\t\t\t# '-vf', 'scale=w=min(iw\\\\,500):h=-2',\n\t\t\t\t\t\t# this one is smaller\n\t\t\t\t\t\t'-vf', 'scale=w=min(iw\\\\,300):h=-2, vflip',\n\n\t\t\t\t\t\t# format\n\t\t\t\t\t\t'-c:v', 'webp',\n\t\t\t\t\t\t# ffmpeg encoding type\n\t\t\t\t\t\t'-f', 'image2pipe',\n\t\t\t\t\t\t# lossless\n\t\t\t\t\t\t# quite a heavy load\n\t\t\t\t\t\t# '-lossless', '0',\n\t\t\t\t\t\t# make it take even less space\n\t\t\t\t\t\t'-lossless', '0',\n\t\t\t\t\t\t# lossless compression\n\t\t\t\t\t\t# (now is lossy)\n\t\t\t\t\t\t'-compression_level', '0',\n\t\t\t\t\t\t# '-compression_level', '0',\n\t\t\t\t\t\t'-qscale', '50',\n\t\t\t\t\t\t# output to stdout\n\t\t\t\t\t\t'pipe:'\n\t\t\t\t\t]\n\n\t\t\t\t\ttowebp = subprocess.run(ffmpeg_prms, capture_output=True)\n\n\t\t\t\t\tprint('tga file exists')\n\t\t\t\t\t# if file exists - read its contents into buffer\n\t\t\t\t\tresult_sky[svtf + '_' + hdld] = base64.b64encode(towebp.stdout).decode()\n\n\n\n\n\n\t#\n\t# Continue with files\n\t#\n\tif skyinfo['vpk'] == False:\n\n\t\t#\n\t\t# convert sides to tga with vtfcmd and store in memory as base64\n\t\t#\n\t\tfor svtf in skysides:\n\t\t\t# try both HDR and LDR for a side\n\t\t\tfor hdld in hl_dict:\n\t\t\t\t# only proceed if hdr/ldr exists\n\t\t\t\tif skyinfo[hdld] == None:\n\t\t\t\t\tcontinue\n\t\t\t\t# construct the path to vtf\n\t\t\t\ttgt_vtf = (ext_dir / Path(skyinfo[hdld] + svtf + '.vtf'))\n\t\t\t\t# only proceed further if this vtf exists\n\t\t\t\tif not tgt_vtf.is_file():\n\t\t\t\t\tresult_sky[svtf + '_' + hdld] = None\n\t\t\t\t\tcontinue\n\t\t\t\tprint('vtf file exists')\n\t\t\t\tvtfcmd_prms[-1] = str(tgt_vtf)\n\t\t\t\tvtf_conv_echo = subprocess.run(vtfcmd_prms, capture_output=True)\n\n\t\t\t\t# conversion done. Check success by checking the file existence\n\t\t\t\tif tgt_vtf.with_suffix('.tga').is_file():\n\t\t\t\t\t# convert shite with magick\n\t\t\t\t\tmagix_prms = [\n\t\t\t\t\t\tstr(magix),\n\t\t\t\t\t\tstr(tgt_vtf.with_suffix('.tga')),\n\n\t\t\t\t\t\t# webp parameters\n\t\t\t\t\t\t'-quality', '0',\n\t\t\t\t\t\t'-define', 'webp:lossless=true',\n\t\t\t\t\t\t'-define', 'webp:partition-limit=0',\n\t\t\t\t\t\t'-define', 'webp:thread-level=1',\n\t\t\t\t\t\tstr('webp:')\n\t\t\t\t\t]\n\n\t\t\t\t\t# ffmpeg is 10 times faster\n\t\t\t\t\tffmpeg_prms = [\n\t\t\t\t\t\t# mpeg\n\t\t\t\t\t\tstr(ffmpeg),\n\t\t\t\t\t\t# input\n\t\t\t\t\t\t'-i', str(tgt_vtf.with_suffix('.tga')),\n\n\t\t\t\t\t\t# resize\n\t\t\t\t\t\t# '-hwaccel', 'cuda',\n\t\t\t\t\t\t# '-hwaccel_output_format', 'cuda',\n\t\t\t\t\t\t# '--enable-nvenc',\n\t\t\t\t\t\t# '--enable-ffnvcodec',\n\t\t\t\t\t\t# '-h', 'encoder=h264_nvenc',\n\t\t\t\t\t\t\n\t\t\t\t\t\t# does nothing\n\t\t\t\t\t\t# (it works, but it's only for videos)\n\t\t\t\t\t\t'-vcodec', 'h264_nvenc',\n\t\t\t\t\t\t\n\t\t\t\t\t\t# change size\n\t\t\t\t\t\t# this will upscale sometimes\n\t\t\t\t\t\t# '-vf', 'scale=500:-1',\n\n\t\t\t\t\t\t# while this is smart\n\t\t\t\t\t\t# this is quite a hires preview\n\t\t\t\t\t\t# '-vf', 'scale=w=min(iw\\\\,500):h=-2',\n\t\t\t\t\t\t# this one is smaller\n\t\t\t\t\t\t'-vf', 'scale=w=min(iw\\\\,300):h=-2, vflip',\n\n\t\t\t\t\t\t# format\n\t\t\t\t\t\t'-c:v', 'webp',\n\t\t\t\t\t\t# ffmpeg encoding type\n\t\t\t\t\t\t'-f', 'image2pipe',\n\t\t\t\t\t\t# lossless\n\t\t\t\t\t\t# quite a heavy load\n\t\t\t\t\t\t# '-lossless', '0',\n\t\t\t\t\t\t# make it take even less space\n\t\t\t\t\t\t'-lossless', '0',\n\t\t\t\t\t\t# lossless compression\n\t\t\t\t\t\t# (now is lossy)\n\t\t\t\t\t\t'-compression_level', '6',\n\t\t\t\t\t\t# '-compression_level', '0',\n\t\t\t\t\t\t'-qscale', '50',\n\t\t\t\t\t\t# output to stdout\n\t\t\t\t\t\t'pipe:'\n\t\t\t\t\t]\n\n\t\t\t\t\ttowebp = subprocess.run(ffmpeg_prms, capture_output=True)\n\n\t\t\t\t\tprint('non-vpk tga file exists')\n\t\t\t\t\t# if file exists - read its contents into buffer\n\t\t\t\t\tresult_sky[svtf + '_' + hdld] = base64.b64encode(towebp.stdout).decode()\n\n\n\n\n\n\n\n\treturn result_sky\n\n\n\n\ndef mdma():\n\tpass\n\t# import json\n\t# sk = find_skyboxes(r'E:\\Gamess\\steamapps\\common\\Source SDK Base 2013 Singleplayer\\ded123\\gameinfo.txt', r\"C:\\Users\\DrHax\\AppData\\Roaming\\Blender Foundation\\Blender\\3.1\\scripts\\addons\\blender_foil\\bins\\vpk\\vpk.exe\")\n\t# print(json.dumps(sk))\n\t# sex = load_sky_bitmap(sk[0], r\"C:\\Users\\DrHax\\AppData\\Roaming\\Blender Foundation\\Blender\\3.1\\scripts\\addons\\blender_foil\\bins\\vpk\\vpk.exe\", r\"C:\\Users\\DrHax\\AppData\\Roaming\\Blender Foundation\\Blender\\3.1\\scripts\\addons\\blender_foil\\bins\\vtf_cmd\\bin\\x64\\VTFCmd.exe\")\n\t# print(sex)\n# mdma()","repo_name":"MrKleiner/blender_foil","sub_path":"mods/skyboxer/skybox_loader.py","file_name":"skybox_loader.py","file_ext":"py","file_size_in_byte":20777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"66321230","text":"import io\nimport os\n\nfrom setuptools import setup\n\n# Package meta-data.\nNAME = 'smplx'\nDESCRIPTION = 'PyTorch module for loading the SMPLX body model'\nURL = 'http://smpl-x.is.tuebingen.mpg.de'\nEMAIL = 'vassilis.choutas@tuebingen.mpg.de'\nAUTHOR = 'Vassilis Choutas'\nREQUIRES_PYTHON = '>=3.6.0'\nVERSION = '0.1.28'\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\ntry:\n    FileNotFoundError\nexcept NameError:\n    FileNotFoundError = IOError\n\n# Import the README and use it as the long-description.\n# Note: this will only work if 'README.md' is present in your MANIFEST.in file!\ntry:\n    with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:\n        long_description = '\\n' + f.read()\nexcept FileNotFoundError:\n    long_description = DESCRIPTION\n\n# Load the package's __version__.py module as a dictionary.\nabout = {}\nif not VERSION:\n    with open(os.path.join(here, NAME, '__version__.py')) as f:\n        exec(f.read(), about)\nelse:\n    about['__version__'] = VERSION\n\npyrender_reqs = ['pyrender>=0.1.23', 'trimesh>=2.37.6', 'shapely']\nmatplotlib_reqs = ['matplotlib']\nopen3d_reqs = ['open3d-python']\n\nsetup(name=NAME,\n      version=about['__version__'],\n      description=DESCRIPTION,\n      long_description=long_description,\n      long_description_content_type='text/markdown',\n      author=AUTHOR,\n      author_email=EMAIL,\n      python_requires=REQUIRES_PYTHON,\n      url=URL,\n      install_requires=[\n          'numpy>=1.16.2',\n          'torch>=1.0.1.post2',\n      ],\n      extras_require={\n          'pyrender': pyrender_reqs,\n          'open3d': open3d_reqs,\n          'matplotlib': matplotlib_reqs,\n          'all': pyrender_reqs + matplotlib_reqs + open3d_reqs\n      },\n      packages=['smplx'])\n","repo_name":"vchoutas/smplx","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":1407,"dataset":"github-code","pt":"77"}
+{"seq_id":"29541337247","text":"import sys\r\nfrom math import sqrt\r\n\r\ndef matmul(A, B):\r\n    # 2 by 2\r\n    return [[sum(A[i][k]*B[k][j] for k in range(2))%1000 for j in range(2)] for i in range(2)]\r\n\r\ndef pow(m):\r\n    global a\r\n    t = [[1, 0], [0, 1]]\r\n    stk = []\r\n    while m:\r\n        stk.append(m%2)\r\n        m //= 2\r\n    while stk:\r\n        e = stk.pop()\r\n        if e:\r\n            t = matmul(matmul(t, t), a)\r\n        else:\r\n            t = matmul(t, t)\r\n    return t\r\n\r\n\r\nT = int(sys.stdin.readline())\r\na = [[6, -4], [1, 0]]\r\nfor i in range(1, T+1):\r\n    n = int(sys.stdin.readline())\r\n    res = pow(n-1)[1]\r\n    ans = (28*res[0] + 6*res[1]-1)%1000\r\n    ans = str(ans)\r\n    ans = \"0\"*(3-len(ans)) + ans\r\n    print(f\"Case #{i}: {ans}\")\r\n\r\n    ","repo_name":"hamelin92/ps","sub_path":"백준/Platinum/12728. n제곱 계산/n제곱 계산.py","file_name":"n제곱 계산.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21446476267","text":"from django.urls import path\nfrom .import views\nfrom .views import *\n\n\nurlpatterns = [\n    path('', views.get_all_teachers, name='teachers_list'),\n    path('create/', TeacherCreate.as_view(), name='create'),\n    path('delete/<int:id>/', views.delete_teacher_by_id, name='delete_teacher'),\n    path('update/<int:id>', views.teacher_put, name='update'),\n    path('clean/', views.delete_all_teachers, name='delete_all_teachers'),\n]\n","repo_name":"SviatikKh/dj-stud-table","sub_path":"statement/teacher/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13950837000","text":"def get_top_elf_calories(input_file):\n    with open(input_file) as f:\n        lines = f.readlines()\n\n    highest = 0\n    current = 0\n    n_lines = len(lines)\n\n    for idx, line in enumerate(lines):\n        if line == \"\\n\" or idx == n_lines - 1:\n            if current > highest:\n                highest = current\n                current = 0\n            else:\n                current = 0\n        else:\n            current += int(line)\n\n    return highest\n\n\ndef get_top_three_elves_calories(input_file):\n    with open(input_file) as f:\n        lines = f.readlines()\n\n    top_three = []\n\n    current = 0\n    n_lines = len(lines)\n\n    for idx, line in enumerate(lines):\n        if line == \"\\n\" or idx == n_lines - 1:\n            if current > min(top_three, default=0):\n                top_three.sort()\n                if len(top_three) == 3:\n                    top_three[0] = current\n                else:\n                    top_three.append(current)\n                current = 0\n            else:\n                current = 0\n        else:\n            current += int(line)\n\n    return sum(top_three)\n\n\nprint(f\"Solution to Part One: {get_top_elf_calories('day1.txt')}\\n\")\nprint(f\"Solution to Part Two: {get_top_three_elves_calories('day1.txt')}\")\n","repo_name":"blairnangle/advent-of-code-2022","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19774875344","text":"import numpy as np\nfrom tools import distance\n\n\nclass KMeans:\n    @property\n    def centers(self):\n        return self.centers\n\n    def fit(self, X, n_clusters, epochs):\n        \"\"\"\n        Parameters\n        ----------\n        X : shape (n_samples, n_features)\n            Training data\n        n_clusters : The number of clusters\n        epochs : The number of epochs\n        Returns\n        -------\n        y : shape (n_samples,)\n            Predicted cluster label per sample.\n        \"\"\"\n        n_samples = X.shape[0]\n        self.centers = X[np.random.choice(n_samples, n_clusters)]\n\n        for _ in range(epochs):\n            labels = self.predict(X)\n            self.centers = [np.mean(X[np.flatnonzero(labels == i)], axis=0) for i in range(n_clusters)]\n\n        return self.predict(X)\n\n    def predict(self, X):\n        \"\"\"\n        Parameters\n        ----------\n        X : shape (n_samples, n_features)\n            Predicting data\n        Returns\n        -------\n        y : shape (n_samples,)\n            Predicted cluster label per sample.\n        \"\"\"\n        distances = np.apply_along_axis(distance.euclidean_distance, 1, self.centers, X).T\n        return np.argmin(distances, axis=1)\n\n    @centers.setter\n    def centers(self, value):\n        self._centers = value\n\n\nclass BisectingKMeans(KMeans):\n    def fit(self, X, n_clusters):\n        \"\"\"\n        Parameters\n        ----------\n        X : shape (n_samples, n_features)\n            Training data\n        n_clusters : The number of clusters\n        Returns\n        -------\n        y : shape (n_samples,)\n            Predicted cluster label per sample.\n        \"\"\"\n        n_samples = X.shape[0]\n\n        data = X\n        clusters = []\n        while True:\n            model = KMeans()\n            label = model.fit(data, 2, 100)\n\n            clusters.append(np.flatnonzero(label == 0))\n            clusters.append(np.flatnonzero(label == 1))\n\n            if len(clusters) == n_clusters:\n                break\n\n            sse = [np.var(data[cluster]) for cluster in clusters]\n            data = data[clusters[np.argmax(sse)]]\n            del clusters[np.argmax(sse)]\n\n        y = np.zeros(n_samples)\n        for i in range(len(clusters)):\n            y[clusters[i]] = i\n\n        return y","repo_name":"conquerv0/Pynaissance","sub_path":"7. Machine Learning/k_means.py","file_name":"k_means.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"77"}
+{"seq_id":"5472594461","text":"import sys\n\n# 찾고자 하는 값이 처음 등장하는 위치 반환\ndef lower_bound(m, lst, size):\n    start = 0\n    end = size - 1\n\n    while end > start:\n        mid = (start + end) // 2\n        if lst[mid] >= m: end = mid\n        else: start = mid + 1\n    \n    return end\n\n\n\n# 찾고자 하는 값보다 큰 값이 처음 등장하는 위치 반환\ndef upper_bound(m, lst, size):\n    start = 0\n    end = size - 1\n\n    while end > start:\n        mid = (start + end) // 2\n        if lst[mid] > m: end = mid\n        else: start = mid + 1\n    \n    return end\n\n\n# 입력이 최대 1,000,002번 들어올 수 있으므로\n# sys.stdin.readline 사용하여 시간 최적화\nN = int(sys.stdin.readline())\nN_lst = list(map(int, sys.stdin.readline().split()))\nM = int(sys.stdin.readline())\nM_lst = list(map(int, sys.stdin.readline().split()))\n# 이진탐색을 위한 정렬\nN_lst.sort()\n\n# 출력\n# N이 1일때 예외처리 \nif N == 1:\n    for i in M_lst: \n        if N_lst[0] == i: \n            print(\"1\", end=' ')\n        else: \n            print(\"0\", end=' ')\n    print()\nelse:\n    for i in M_lst: \n        Upper = upper_bound(i, N_lst, N)\n        Lower = lower_bound(i, N_lst, N)\n        # Upper가 배열의 마지막일 경우 \n        if Upper == N-1 and N_lst[N-1] == i:\n            Upper += 1\n        print(Upper-Lower, end=' ')\n    print()\n","repo_name":"UJHa/Codeit-Study","sub_path":"(10) 알고리즘 기초 - Sort, Binary Search/04) 10816 숫자 카드 2/ljh.py","file_name":"ljh.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19117552328","text":"\"\"\"Test case for the BoundingBox class\r\n\r\nSee https://code.visualstudio.com/docs/python/testing for how to set up \r\ntesting in VSCode\"\"\"\r\n\r\n__author__ = \"François d'Entremont\"\r\n\r\nimport unittest\r\n\r\nfrom pyproj import CRS\r\n\r\nimport bbox\r\n \r\n\r\nclass TestBoundingBox(unittest.TestCase):\r\n    \"\"\"Bounding box test case\"\"\"\r\n\r\n    def test_contains_true(self):\r\n        # Create a new BoundingBox\r\n        box = bbox.BoundingBox('', 0, 0, 2, 2, None)\r\n        # A coordinate with the values 1,1 should be inside\r\n        self.assertTrue(box.contains(1, 1))\r\n\r\n    def test_contains_false(self):\r\n        # Create a new BoundingBox\r\n        box = bbox.BoundingBox('', 0, 0, 2, 2, None)\r\n        # A coordinate with the values 3,2 should be outside\r\n        self.assertFalse(box.contains(3, 2))\r\n\r\n    def test_contains_touch_min_true(self):\r\n        # A point that is on the edge should be considered inside\r\n        box = bbox.BoundingBox('', 0, 0, 2, 2, None)\r\n        # A coordinate with the values 3,2 should be outside\r\n        self.assertTrue(box.contains(0, 0))\r\n\r\n    def test_contains_touch_max_true(self):\r\n        # A point that is on the edge should be considered inside\r\n        box = bbox.BoundingBox('', 0, 0, 2, 2, None)\r\n        # A coordinate with the values 3,2 should be outside\r\n        self.assertTrue(box.contains(2, 2))\r\n\r\n    def test_transform_to(self):\r\n        # Create pyproj CRS\r\n        crs = CRS.from_epsg(2961)\r\n        # Create a bounding box\r\n        box = bbox.BoundingBox('BBOX-1', 329000.0, 4972000.0,\r\n         330000.0, 4973000.0, crs)\r\n        # CRS that we will transform to\r\n        epsg4617 = CRS.from_epsg(4617)\r\n        box.transform_to(epsg4617)\r\n        # The CRS should now be epsg4617\r\n        self.assertTrue(box._crs == epsg4617)\r\n        # Test the coordinates\r\n        self.assertEqual(44.880895860232485, box._min_axis_0)\r\n        self.assertEqual(-65.16514569988556, box._min_axis_1)\r\n        self.assertEqual(44.890130890718865, box._max_axis_0)\r\n        self.assertEqual(-65.1528286442206, box._max_axis_1)\r\n","repo_name":"frankdent/PROG5000","sub_path":"BoundingBox_Assignment/bbox_test.py","file_name":"bbox_test.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22517510457","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nfrom typing import List\n\nclass Solution1:\n    def smallerNumbersThanCurrent(self, nums: List[int]) -> List[int]:\n        res = [0] * len(nums)\n        for n_i, n in enumerate(nums):\n            for e_n in nums:\n                if e_n < n:\n                    res[n_i] += 1\n        return res\n\n\nclass Solution2:\n\n    def quickSort(self, nums: List[tuple], left: int, right:int):\n        '''\n        快排，注意输入数组的格式，是[(n, i) for i, n in enumerate(nums)]\n        '''\n        if left >= right: return\n\n        i = d = left + 1\n        while i <= right:\n            if nums[i][0] < nums[left][0]:    # 基准值比较时只用[0]\n                nums[i], nums[d] = nums[d], nums[i]    # 交换时整个元组交换\n                d += 1\n            i += 1\n        nums[d - 1], nums[left] = nums[left], nums[d - 1]    # 交换时整个元组交换\n\n        self.quickSort(nums, left, d - 2)\n        self.quickSort(nums, d, right)\n\n\n    def smallerNumbersThanCurrent(self, nums: List[int]) -> List[int]:\n        nums_with_i = [(n, i) for i, n in enumerate(nums)]\n        self.quickSort(nums_with_i, 0, len(nums) - 1)\n\n        # 此时，nums还是原数组，而nums_with_i是排序好的扩展数组\n        res = [0] * len(nums)\n        prev = nums_with_i[0][0]\n        i = 1\n        while i < len(nums):\n            if nums_with_i[i][0] == prev:    # 如果和上一个数字相同，则沿用上个数字的res值\n                res[nums_with_i[i][1]] = res[nums_with_i[i-1][1]]\n            else:    # 当前数字左边共有几个数字，也就是下标i的值\n                res[nums_with_i[i][1]] = i\n            prev = nums_with_i[i][0]\n            i += 1\n\n        return res","repo_name":"ftakanashi/JobProjects","sub_path":"LeetCode/1365.有多少小于当前数字的数字/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"35573099967","text":"# -*- coding: utf-8 -*-#\n\n#-------------------------------------------------------------------------------\n# Name:         create_dictionary\n# Description:  question->word->dictionary for validation & training\n# Author:       Boliu.Kelvin\n# Date:         2020/4/5\n#-------------------------------------------------------------------------------\n\nimport argparse\nimport json\nimport os\nimport pandas as pd\nimport numpy as np\nimport _pickle as cPickle\nimport sys\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nclass Dictionary(object):\n    def __init__(self, word2idx=None, idx2word=None):\n        if word2idx is None:\n            word2idx = {}\n        if idx2word is None:\n            idx2word = []\n        self.word2idx = word2idx\n        self.idx2word = idx2word\n\n    @property\n    def ntoken(self):\n        return len(self.word2idx)\n\n    @property\n    def padding_idx(self):\n        return len(self.word2idx)\n\n    def tokenize(self, sentence, add_word):\n        sentence = sentence.lower()\n        if \"? -yes/no\" in sentence:\n            sentence = sentence.replace(\"? -yes/no\", \"\")\n        if \"? -open\" in sentence:\n            sentence = sentence.replace(\"? -open\", \"\")\n        if \"? - open\" in sentence:\n            sentence = sentence.replace(\"? - open\", \"\")\n        sentence = sentence.replace(',', '').replace('?', '').replace('\\'s', ' \\'s').replace('...', '').replace('x ray', 'x-ray').replace('.', '')\n        words = sentence.split()\n        tokens = []\n        if add_word:\n            for w in words:\n                tokens.append(self.add_word(w))\n        else:\n            for w in words:\n                # if a word is not in dictionary, it will be replaced with the last word of dictionary.\n                tokens.append(self.word2idx.get(w, self.padding_idx-1))\n        return tokens\n\n    def dump_to_file(self, path):\n        cPickle.dump([self.word2idx, self.idx2word], open(path, 'wb'))\n        print('dictionary dumped to %s' % path)\n\n    @classmethod\n    def load_from_file(cls, path):\n        print('loading dictionary from %s' % path)\n        word2idx, idx2word = cPickle.load(open(path, 'rb'))\n        d = cls(word2idx, idx2word)\n        return d\n\n    def add_word(self, word):\n        if word not in self.word2idx:\n            self.idx2word.append(word)\n            self.word2idx[word] = len(self.idx2word) - 1\n        return self.word2idx[word]\n\n    def __len__(self):\n        return len(self.idx2word)\n\n\ndef create_dictionary(dataroot, dataset_name, train_file, test_file):\n    dictionary = Dictionary()\n    questions = []\n    files = [train_file, test_file]\n    for path in files:\n        data_path = os.path.join(data, path)\n        with open(data_path) as f:\n            d = json.load(f)\n        df = pd.DataFrame(d)\n        if dataset_name.lower() in [\"slake\", \"vqa-slake\", \"vqa_slake\"]:\n            df = df[df['q_lang']==\"en\"]\n        print(\"processing the {}\".format(path))\n        for id, row in df.iterrows():\n            dictionary.tokenize(row['question'], True)     #row[0]: id , row[1]: question , row[2]: answer\n\n    return dictionary\n\ndef create_glove_embedding_init(idx2word, glove_file):\n    print('creating glove embeddings...')\n    word2emb = {}\n    with open(glove_file, 'r') as f:\n        entries = f.readlines()\n    emb_dim = len(entries[0].split(' ')) - 1\n    weights = np.zeros((len(idx2word), emb_dim), dtype=np.float32)\n\n    for entry in entries:\n        vals = entry.split(' ')\n        word = vals[0]\n        vals = list(map(float, vals[1:]))\n        word2emb[word] = np.array(vals)\n    for idx, word in enumerate(idx2word):\n        if word not in word2emb:\n            continue\n        weights[idx] = word2emb[word]\n    return weights, word2emb\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(description=\"Med VQA\")\n    parser.add_argument(\"inputpath\", type=str, help=\"Path to input data\")\n    parser.add_argument(\"--dataset\", type=str, help=\"Name of the dataset\", default=\"slake\")\n    parser.add_argument(\"--trainfile\", type=str, help=\"Name of the train file\", default=\"train.json\")\n    parser.add_argument(\"--testfile\", type=str, help=\"Name of the test file\", default=\"test.json\")\n    args = parser.parse_args()\n    data = args.inputpath\n    dataset = args.dataset\n    train_file = args.trainfile\n    test_file = args.testfile\n    d = create_dictionary(data, dataset, train_file, test_file)\n    d.dump_to_file(data + '/dictionary.pkl')\n\n    d = Dictionary.load_from_file(data + '/dictionary.pkl')\n    emb_dim = 300\n    glove_path = data[:data.rindex('/')]\n    glove_file = glove_path + '/glove.6B/glove.6B.%dd.txt' % emb_dim\n    weights, word2emb = create_glove_embedding_init(d.idx2word, glove_file)\n    np.save(data + '/glove6b_init_%dd.npy' % emb_dim, weights)\n    print(\"Process finished successfully!\")\n","repo_name":"sarahESL/PubMedCLIP","sub_path":"QCR_PubMedCLIP/lib/utils/create_dictionary.py","file_name":"create_dictionary.py","file_ext":"py","file_size_in_byte":4814,"program_lang":"python","lang":"en","doc_type":"code","stars":97,"dataset":"github-code","pt":"77"}
+{"seq_id":"19697911708","text":"#!/usr/bin/env python3\n\nimport sys\nsys.path.append(\"src\")\nimport misc.plot_utils\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\nimport json\n\nargs = argparse.ArgumentParser()\nargs.add_argument(\"--logfile\", default=\"computed/analyzespans_onemare.json\")\nargs = args.parse_args()\n\nwith open(args.logfile, \"r\") as f:\n    data = json.load(f)\n\ndata_w = [[int(k)]*v for k,v in data[\"words\"].items()]\ndata_w = [x for l in data_w for x in l]\ndata_c = [[int(k)]*v for k,v in data[\"chars\"].items()]\ndata_c = [x for l in data_c for x in l]\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7, 3.5))\n\nxpoints_w = np.arange(0, 100+1, 5)\n\nax1.hist(\n    data_w,\n    bins=xpoints_w,\n    label=\"Words\",\n    edgecolor=\"black\",\n)\nax1.set_xticks(xpoints_w[::2], xpoints_w[::2])\nax1.set_ylim(0, 550000)\nax1.set_xlabel(\"Words\")\nax1.set_ylabel(\"Count\")\n\nxpoints_c = np.arange(0, 900+1, 50)\n\nax2.hist(\n    data_c,\n    bins=xpoints_c,\n    label=\"Chars\",\n    edgecolor=\"black\",\n)\nax2.set_xticks(xpoints_c[::2], xpoints_c[::2])\nax2.set_ylim(0, 550000)\nax2.get_yaxis().set_visible(False)\nax2.set_xlabel(\"Chars\")\n\nplt.tight_layout()\nplt.savefig(f\"figures/filter_distribution.pdf\")\nplt.show()\n","repo_name":"zouharvi/kb-shrink","sub_path":"src/reduce_count/figures/distribution.py","file_name":"distribution.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"33909184332","text":"from django.shortcuts import render, redirect\nfrom .models import List\nfrom .forms import ListForm\nfrom django.contrib import messages\n\ndef index(request):\n    if request.method == 'POST':\n        form = ListForm(request.POST or None)\n        if form.is_valid:\n            form.save()\n            all_items = List.objects.all\n            messages.success(request, (\"Your Schedule has been posted\"))\n            context = {'title':'Index', 'content':'Routine Index', 'all_items':all_items}\n            return render(request, 'index.html', context)\n        \n\n    else:\n        all_items = List.objects.all\n        context = {'title': 'Index',\n                    'content': 'Routine Index',\n                    'all_items': all_items}\n        return render(request, 'index.html', context)\n\ndef about(request):\n    context = {'title': 'About',\n                'content': 'About Us'}\n    return render(request, 'about.html', context)\n\n\ndef delete(request, list_id):\n    item = List.objects.get(pk = list_id)\n    item.delete()\n    messages.success(request, (\"Schedule has been deleted from List\"))\n    return redirect('index')\n\n\ndef check_in(request, list_id):\n    item = List.objects.get(pk = list_id)\n    item.completed = True\n    item.save()\n    return redirect('index')\n\ndef check_out(request, list_id):\n    item = List.objects.get(pk = list_id)\n    item.completed = False\n    item.save()\n    return redirect('index')\n\ndef edit(request, list_id):\n    if request.method == 'POST':\n        all_items = List.objects.get(pk = list_id)\n        form = ListForm(request.POST or None, instance= all_items)\n        if form.is_valid:\n            form.save()\n            \n            messages.success(request, (\"Schedule edited\"))\n            context = {'title':'Edit', 'content':'Edit Page', 'all_items':all_items}\n            return redirect('index')\n        \n\n    else:\n        all_items = List.objects.get(pk=list_id)\n        context = {'title': 'Edit',\n                    'content': 'Routine Index',\n                    'all_items': all_items}\n        return render(request, 'edit.html', context)\n\n\n\n","repo_name":"Mobinul341/Routine_App_Django","sub_path":"riutine_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38120352496","text":"import re\n\nimport __main__\n__main__.pymol_argv = ['pymol', '-qc'] # Pymol: quiet and no GUI\nimport pymol\n\nfrom align import align_res_nums\n\n\npymol.finish_launching()\n\n\ndef check_if_site_unbound(pdb_of_interest_loc, pdb_of_interest_chain_id,\n                          ref_pdb_loc, ref_chain_id, ref_extended_site,\n                          ref_ligand_resn, ref_ligand_resi):\n    \"\"\"Check that no ligand is bound to a site & no site residues are missing.\n\n    Parameters\n    ----------\n    pdb_of_interest_loc : string\n        The location of the PDB to be checked.\n    pdb_of_interest_chain_id : string\n        The chain to be checked.\n    ref_pdb_loc : string\n        The location of a structure that has a ligand bound at the cryptic\n        site.\n    ref_chain_id : string\n        The chain of the structure at ref_pdb_loc that has the cryptic site.\n    ref_extended_site : list of strings\n        A list of residue numbers of all residues in ref_chain_id of the\n        ref_pdb_loc structure that are within 9 Angstroms of the center of the\n        ligand.  Each entry should be a string-typed number; it should not list\n        the chain.  This list can be created by the function\n        get_extended_site.\n    ref_ligand_resn : string\n        The name of the ligand in ref_pdb_loc.  It should exactly match the\n        name in the file.\n    ref_ligand_resi : string\n        The string-formatted residue number of the ligand in ref_pdb_loc.\n\n\n    Returns\n    -------\n    site_is_unbound : bool\n        True if no ligand is present at the site and no residues within 9\n        Angstroms of the site are missing.  False if there is a ligand or if\n        residues are missing.\n    \"\"\"\n\n    pymol.cmd.reinitialize()\n    # Abbreviations: int is pdb_of_interest; ref is ref_pdb.\n    # Determine which residues in pdb_of_interest were part of the extended site in the\n    # reference structure.\n    ref_pdb_to_int_pdb_dict = align_res_nums(ref_pdb_loc, ref_chain_id,\n                                             pdb_of_interest_loc,\n                                             pdb_of_interest_chain_id)\n    int_extended_site = []\n    for ref_site_res in ref_extended_site:\n        # ref_pdb_to_int_pdb_dict doesn't have any residues that are HETATM in the\n        # ref_pdb.  But ref_extended_site has these residues.  I ignore them, because\n        # this is simple.\n        if ref_site_res in ref_pdb_to_int_pdb_dict:\n            int_site_res = ref_pdb_to_int_pdb_dict[ref_site_res]\n            if int_site_res == \"NA\":\n                return False\n            int_extended_site.append(int_site_res)\n    # Align the reference structure's extended site with the same residues in\n    # pdb_of_interest.\n    pymol.cmd.load(ref_pdb_loc, \"reference\")\n    pymol.cmd.load(pdb_of_interest_loc, \"interest\")\n    # Iteratively create the selection string.  Select the binding-site residues.\n    ref_site_sel_string = \"reference and chain %s and resi \" %(ref_chain_id)\n    for ref_site_res in ref_extended_site:\n        if ref_site_res in ref_pdb_to_int_pdb_dict: # See above for explanation.\n            ref_site_sel_string += \"%s+\" %(ref_site_res)\n    ref_site_sel_string = ref_site_sel_string[:-1] # Remove the trailing + symbol.\n    pymol.cmd.select(\"ref_extended_site\", ref_site_sel_string)\n\n    # Select the binding-site residues in the pdb_of_interest.\n    int_site_sel_string = \"interest and chain %s and resi \" %(pdb_of_interest_chain_id)\n    for int_site_res in int_extended_site:\n        int_site_sel_string += \"%s+\" %(int_site_res)\n    int_site_sel_string = int_site_sel_string[:-1] # Remove the trailing + symbol.\n    pymol.cmd.select(\"int_extended_site\", int_site_sel_string)\n\n    # Align the PDBs.  Select the reference structure's ligand.  Check for any ligands\n    # (except water and metals) in the pdb_of_interest within 5 Angstroms of the\n    # reference structure's ligand.\n    pymol.cmd.align(\"int_extended_site\", \"ref_extended_site\")\n    ref_lig_sel_string = (\"reference and resn %s and resi %s and \"\n                          \"(byres all within 10 of \"\n                          \"(reference and chain %s and resi %s))\"\n                          %(ref_ligand_resn, ref_ligand_resi, ref_chain_id,\n                            ref_extended_site[0]))\n    pymol.cmd.select(\"ref_lig\", ref_lig_sel_string)\n    nearby_ligand_sel_string = (\"not metal and not (resn HOH) and not polymer.protein \"\n                                \"and (interest within 5 of ref_lig)\")\n    pymol.cmd.select(\"int_lig_nearby\", nearby_ligand_sel_string)\n    \n    pymol.stored.num_nearby_lig_atoms = 0\n    pymol.cmd.iterate(\"int_lig_nearby\", \"pymol.stored.num_nearby_lig_atoms += 1\")\n    if pymol.stored.num_nearby_lig_atoms > 0:\n        site_is_unbound = False\n    else:\n        site_is_unbound = True\n    return site_is_unbound\n","repo_name":"Albert-Lau-Lab/tactics_protein_analysis","sub_path":"train_model/generate_training_database/ext_crypto_database/check_if_site_unbound.py","file_name":"check_if_site_unbound.py","file_ext":"py","file_size_in_byte":4810,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"22253505903","text":"from bs4 import BeautifulSoup\n\n\ndef find_price_values(response) -> (float, str):\n    price_values = []\n\n    page_soup = BeautifulSoup(response, \"html.parser\")\n    price_values_scraper = page_soup.find('div', class_='col-6')\n\n    for elem in price_values_scraper:\n        price_values.append(elem.text)\n\n    price_usd = float(price_values[3].split()[0][1:])\n    price_increase = price_values[3].split()[4][1:-1]\n    return price_usd, price_increase\n\n\ndef find_market_cap(response) -> float:\n    button_values = []\n\n    page_soup = BeautifulSoup(response, \"html.parser\")\n    buttons = page_soup.find_all('button')\n\n    for elem in buttons:\n        button_values.append(elem.text)\n\n    market_cap = float(button_values[2][2:].replace(',', ''))\n    return market_cap\n","repo_name":"LuZeusMM/streamlit_apps","sub_path":"million_token/scrap_token_values.py","file_name":"scrap_token_values.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"72701238330","text":"'''\n1. Ejercicio: Imprime los números del 1 al 10 usando un bucle for .\n2. Ejercicio: Imprime los números pares del 1 al 20 usando un bucle while .\n3. Ejercicio: Usa un bucle para calcular la suma de los números del 1 al 100.'''\n\nfor i in range(1,11):\n    print(i)\n    \nx = 1\nwhile x <= 20:\n    if x % 2 == 0:\n        print(x) \n    x +=1\n    \nsuma = 0\nfor numero in range(1,101):\n    suma += numero\nprint(\"La suma de los números del 1 al 110 es:\", suma)","repo_name":"frangarcia8/PreworkPython-FranGarcia","sub_path":"bucles.py","file_name":"bucles.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2596788095","text":"# Not used, maybe in the future\n\nimport nodes\n\ndef createNestedNodeClass(node_def):\n    # Create a new class with the node name\n    node_name = node_def[\"name\"]\n\n    # Inputs\n    @classmethod\n    def INPUT_TYPES(s):\n        return node_def[\"inputs\"]\n\n    # Entry point\n    nested_workflow = node_def[\"description\"][\"nestedNodes\"]\n    def execute_nested_node(self, **kwargs):\n        for node in self.nested_workflow:\n            nodes.NODE_CLASS_MAPPINGS[node[\"type\"]].execute(node, **kwargs)\n\n    node_class = type(node_name, (object,), {\n        \"INPUT_TYPES\": INPUT_TYPES,\n        \"RETURN_TYPES\": node_def[\"output\"],\n        \"CATEGORY\": \"Nested Nodes\",\n        \"FUNCTION\": \"execute_nested_node\",\n        \"execute_nested_node\": execute_nested_node,\n        # instance variables\n        \"nested_workflow\": nested_workflow,\n    })\n\n    return node_class\n","repo_name":"ssitu/ComfyUI_NestedNodeBuilder","sub_path":"nested_node.py","file_name":"nested_node.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"77"}
+{"seq_id":"29379103399","text":"def validar_secuencia_adn(secuencia):\n    # Convertir la secuencia a mayúsculas\n    secuencia = secuencia.upper()\n\n    # Verificar si la secuencia contiene solo las letras A, C, T y G\n    for letra in secuencia:\n        if letra not in 'ACTG':\n            return False\n\n    return True\n\n# Obtener la secuencia del usuario\nsecuencia_input = input(\"Ingresa la secuencia de ADN: \")\n\n# Validar la secuencia y mostrar el resultado\nif validar_secuencia_adn(secuencia_input):\n    print(\"Secuencia correcta\")\nelse:\n    print(\"Secuencia incorrecta\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej2/hito2_ej2_f6442c8eb1aee058df26af9c389c6ac3.py","file_name":"hito2_ej2_f6442c8eb1aee058df26af9c389c6ac3.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40423484769","text":"from PyQt4 import QtCore, QtGui\n\nfrom Ui_MKBSubclassEditor import Ui_MKBSubclassEditorDialog\nfrom library.DialogBase import CDialogBase\nfrom library.ItemsListDialog import CItemEditorDialog\nfrom library.TableModel import CTableModel, CTextCol\nfrom library.Utils import toVariant\n\n\nclass CMKBSubclass(CDialogBase, Ui_MKBSubclassEditorDialog):\n    def __init__(self, parent):\n        CDialogBase.__init__(self, parent)\n        self.setupUi(self)\n        self.order = ['id']\n        colsSubclass = [\n            CTextCol(u'Код', ['code'], 5),\n            CTextCol(u'Субклассификация', ['name'], 30),\n            ]\n        self.modelSubclass = CTableModel(self, colsSubclass, 'rbMKBSubclass')\n        colsSubclass_Item = [\n            CTextCol(u'пятый знак', ['code'], 5),\n            CTextCol(u'Субклассификация', ['name'], 30),\n            ]\n        self.modelSubclass_Item = CTableModel(self, colsSubclass_Item, 'rbMKBSubclass_Item')\n        self.select()\n        self.btnDel_Item.setVisible(False)\n        self.btnDel.setVisible(False)\n        self.tblSubclass.addPopupDelRow()\n        self.tblSubclass_Item.addPopupDelRow()\n\n    def select(self):\n        table = self.modelSubclass.table()\n        idList = QtGui.qApp.db.getIdList(table.name(), 'id', '', self.order)\n        self.modelSubclass.setIdList(idList)\n        self.tblSubclass.setModel(self.modelSubclass)\n\n    def select_Item(self):\n        id=self.tblSubclass.currentItemId()\n        cond='master_id=%s' % (str(id) if id else 'null')\n        idList=QtGui.qApp.db.getIdList('rbMKBSubclass_Item', 'id', cond, 'id')\n        self.modelSubclass_Item.setIdList(idList)\n        self.tblSubclass_Item.setModel(self.modelSubclass_Item)\n\n    def editSubclass(self, itemId):\n        dialog = CItemEditorDialog(self, 'rbMKBSubclass')\n        if itemId:\n            dialog.load(itemId)\n        dialog.exec_()\n\n    def editSubclass_Item(self, master_id, itemId):\n        dialog = CMKBSubclassItemEditorDialog(self, master_id)\n        if itemId:\n            dialog.load(itemId)\n        dialog.exec_()\n\n    @QtCore.pyqtSlot(QtCore.QModelIndex)\n    def on_tblSubclass_activated(self, index):\n        self.select_Item()\n\n    @QtCore.pyqtSlot(QtCore.QModelIndex)\n    def on_tblSubclass_clicked(self, index):\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnClose_clicked(self):\n        self.close()\n\n    @QtCore.pyqtSlot()\n    def on_btnAdd_clicked(self):\n        self.editSubclass(None)\n        self.select()\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnEdit_clicked(self):\n        id=self.tblSubclass.currentItemId()\n        self.editSubclass(id)\n        self.select()\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnDel_clicked(self):\n        self.select()\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnAdd_Item_clicked(self):\n        master_id=self.tblSubclass.currentItemId()\n        self.editSubclass_Item(master_id, None)\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnEdit_Item_clicked(self):\n        master_id=self.tblSubclass.currentItemId()\n        id=self.tblSubclass_Item.currentItemId()\n        self.editSubclass_Item(master_id, id)\n        self.select_Item()\n\n    @QtCore.pyqtSlot()\n    def on_btnDel_Item_clicked(self):\n        self.select_Item()\n\nclass CMKBSubclassItemEditorDialog(CItemEditorDialog):\n    def __init__(self, parent, master_id):\n        CItemEditorDialog.__init__(self, parent, 'rbMKBSubclass_Item')\n        self.master_id=master_id\n\n    def getRecord(self):\n        record = CItemEditorDialog.getRecord(self)\n        record.setValue('master_id',  toVariant(self.master_id))\n        return record","repo_name":"dio4/vista_1","sub_path":"RefBooks/MKBSubclass.py","file_name":"MKBSubclass.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16222047043","text":"time = list()\ndados = dict()\nresultados = list()\ntotal = 0\nwhile True:\n    dados.clear()\n    dados['jogador'] = str(input('Nome do jogador: '))\n    partidas = int(input('Numero de partidas: '))\n    resultados.clear()\n    for g in range(0, partidas):\n        resultados.append(int(input(f'   Quantos gols na partida {g + 1}? ')))\n    dados['gols'] = resultados[:]\n    dados['total'] = sum(resultados)\n    time.append(dados.copy())\n    while True:\n        resp = str(input('Quer continuar? [S/N]')).strip().upper()[0]\n        if resp in 'SN':\n            break\n        print('Erro. Responda apenas S ou N.')\n    if resp == 'N':\n        break\nprint('=-' * 30)\nprint('cod', end='  ')\nfor i in dados.keys():\n    print(f'{i:<15}', end='')\nprint()\nprint('-' * 40)\nfor k, v in enumerate(time):\n    print(f'{k:>3} ', end='')\n    for d in v.values():\n        print(f'{str(d):<15}', end='')\n    print()\nprint('-' * 40)\n","repo_name":"danilohcb/PythonExercises","sub_path":"ex095.py","file_name":"ex095.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33071295177","text":"import cv2\nimport json\nimport glob\nimport numpy as np\nfrom os.path import join\nfrom os import listdir\n\nvisual = True\n\nLaSOT_base_path = './LaSOT'\n\nfor video_set in sorted(listdir(LaSOT_base_path)):\n    if 'txt' not in video_set:\n        videos = sorted(listdir(join(LaSOT_base_path, video_set)))\n        for vi, video in enumerate(videos):\n            print('video_set: {}, video id: {:04d} / {:04d}'.format(video_set, vi, len(videos)))\n            video_base_path = join(LaSOT_base_path, video_set, video)\n            gts_path = join(video_base_path, 'groundtruth.txt')\n            gts = np.loadtxt(open(gts_path, \"rb\"), delimiter=',')\n\n            # get all im name\n            jpgs = sorted(glob.glob(join(video_base_path, 'img', '*.jpg')))\n\n            for idx, img_path in enumerate(jpgs):\n\n                gt = gts[idx]\n                bbox = [int(g) for g in gt]   # (x_min,y_min,w,h)\n\n                bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]] # (x_min, y_min, x_max, y_max)\n\n                if visual:\n                    im = cv2.imread(img_path)\n                if visual:\n                    pt1 = (int(bbox[0]), int(bbox[1]))\n                    pt2 = (int(bbox[2]), int(bbox[3]))\n                    cv2.rectangle(im, pt1, pt2, (0, 0 ,255), 3)\n                if visual:\n                    cv2.imshow('img', im)\n                    cv2.waitKey(1)\n","repo_name":"hqucv/siamban","sub_path":"training_dataset/lasot/visual.py","file_name":"visual.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":254,"dataset":"github-code","pt":"77"}
+{"seq_id":"29420814539","text":"# completa el código de la función\ndef amigos(a,b):\n    listaa=[]\n    listab=[]\n    sumaa=0\n    sumab=0\n    for i in range(1,a):\n        if (a%i==0):\n            listaa.append(i)\n    for x in range(1,b):\n        if (b%x==0):\n            listab.append(x)\n    for i in range(0,len(listaa)):\n        sumaa+=listaa[i]\n    for x in range(0,len(listab)):\n        sumab+=listab[x]\n    if sumaa==b and sumab==a:\n        return True\n    else:\n        return False\n            \n  \n\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej2/tema2_ej2_9c6b60691fecf66d4932da12fca44038.py","file_name":"tema2_ej2_9c6b60691fecf66d4932da12fca44038.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73015142327","text":"__author__ = 'mcorriere@draper.com'\r\n\r\nimport argparse as argp\r\nimport os, time\r\n\r\nsuffix = '_checked_'\r\ntimestamp = time.strftime(\"%Y%m%d_%H%M%S\")\r\n\r\ndef writeOut(filename, directory):\r\n\r\n    if directory[-1] != '/':\r\n        directory = directory + '/'\r\n\r\n    out_file = directory + filename.rstrip('.dxf') + suffix + timestamp\r\n\r\n    dxf  = out_file + '.dxf'\r\n    pdf  = out_file + '.pdf'\r\n    json = out_file + '.json'\r\n\r\n    dxf_out  = open(dxf, 'w+')\r\n    pdf_out  = open(pdf, 'w+')\r\n    json_out = open(json, 'w+')\r\n    \r\n    dxf_out.close()\r\n    pdf_out.close()\r\n    json_out.close()\r\n\r\n\r\ndef main():\r\n\r\n    parser = argp.ArgumentParser()\r\n    parser.add_argument('filename',\r\n                        help='The .dxf file to process')\r\n    parser.add_argument('in_dir',\r\n                        help='The directory the file resides in')\r\n    parser.add_argument('out_dir',\r\n                        help='The directory where results will be written')\r\n    args = parser.parse_args()\r\n\r\n    filename = args.filename\r\n    inputDirectory = args.in_dir\r\n    outputDirectory = args.out_dir\r\n\r\n    writeOut(filename, outputDirectory)\r\n\r\nif __name__ == '__main__':\r\n  main()\r\n","repo_name":"draperlaboratory/design_rule_check","sub_path":"sembler.py","file_name":"sembler.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"17813378412","text":"##This file contains functions that plots mauna_loa carbon dioxide data\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndef get_df(filename):\n    '''\n    INPUT:filename e.g. mauna_loa.csv\n    OUTPUT: Pandas Dataframe\n    '''\n    df=pd.read_csv(filename)\n    first_date=(df['decimal_date'][0])\n    df['years_since_first']=df['decimal_date']-first_date\n    df=df[['years_since_first','C02']]\n    return df\n   \n\ndef plot_df(df):\n    '''\n    INPUT: Pandas DataFrame\n    OUTPUT: handle to plot axis\n    '''\n    plt.plot(df['years_since_first'],df['C02'])\n    plt.xlabel('Years since 1958')\n    plt.ylabel('CO2 levels')\n    \n    ax=plt.gca()\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    \n    \n    \n    \n    ","repo_name":"sophiebaur/mauna_loa","sub_path":"ml.py","file_name":"ml.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73519452407","text":"from nicegui import ui\n\nfrom language.language import lang\n\n\nclass DialogContainer:\n\n    def __init__(\n            self,\n            handler_button_delete,\n            handler_button_close = None,\n            def_btn_color_delete: str = 'red',\n            def_btn_color_close: str = 'primary',\n            def_btn_text_delete: str = lang.Delete,\n            def_btn_text_close: str = lang.Close,\n            ) -> None:\n        # Defaults\n        self.def_btn_color_delete = def_btn_color_delete\n        self.def_btn_color_close = def_btn_color_close\n        self.def_btn_text_delete = def_btn_text_delete\n        self.def_btn_text_close = def_btn_text_close\n        # Widgets\n        self.dlg = None  # type: ui.dialog\n        self.crd = None  # type: ui.card\n        self.row_1 = None  # type: ui.row\n        self.lbl_title = None  # type: ui.label\n        self.lbl_location = None  # type: ui.label\n        self.lbl_description = None  # type: ui.label\n        self.lbl_items = None  # type: ui.label\n        self.btn_delete = None  # type: ui.button\n        self.btn_close = None  # type: ui.button\n        # Create widgets\n        self._create_widget(\n            handler_button_delete=handler_button_delete,\n            handler_button_close=handler_button_close,\n        )\n        pass\n\n    def __enter__(self):\n        self.open()\n\n    def __exit__(self):\n        self.close()\n\n    def append_close_to_handler(self, handler):\n        def fn():\n            handler()\n            self.dlg.close()\n        return fn if handler is not None else self.dlg.close\n\n    def _create_widget(\n            self,\n            handler_button_delete,\n            handler_button_close,\n            ) -> None:\n        # Create objects that are containers for other\n        self.dlg = ui.dialog(value=False)\n        self.dlg.classes('w-full no-wrap items-center')\n        with self.dlg, ui.card().classes('w-full no-wrap') as self.crd:\n            # Main text\n            self.lbl_title = ui.label(text='lbl_title')\n            self.lbl_title.classes('items-center')\n            self.lbl_location = ui.label(text='lbl_location')\n            self.lbl_description = ui.label(text='lbl_description')\n            self.lbl_items = ui.label(text='lbl_items')\n            # Buttons\n            self.row_1 = ui.row()\n            self.row_1.classes('w-full no-wrap items-center')\n            with ui.row().classes('w-full no-wrap items-center'):\n                # Update handlers\n                handler_button_delete = self.append_close_to_handler(handler_button_delete)\n                handler_button_close = self.append_close_to_handler(handler_button_close)\n                # Cancel button\n                self.btn_close = ui.button(\n                    text=self.def_btn_text_close,\n                    color=self.def_btn_color_close,\n                    on_click=handler_button_close\n                )\n                self.btn_close.classes('w-1/2')\n                # Delete button\n                self.btn_delete = ui.button(\n                    text=self.def_btn_text_delete,\n                    color=self.def_btn_color_delete,\n                    on_click=handler_button_delete,\n                )\n                self.btn_delete.classes('w-1/2')\n\n    def open(self) -> None:\n        self.dlg.open()\n\n    def close(self) -> None:\n        self.dlg.close()\n\n    def load_item(self, item, organizer) -> None:\n        # Update UI\n        self.lbl_title.set_text(f'{lang.ID}: {str(item.id)}')\n        self.lbl_location.set_text(f'{lang.Location}: {str(item.location)}')\n        self.lbl_description.set_text(f'{lang.Description}: {str(item.description)}')\n        # TODO: Load items for that container - represent as table\n        stored_items = organizer.get_stored_items_in_container(containerid=item.id)\n        count = len(stored_items)\n        self.lbl_items.set_text(f'{lang.Items_in_container}: {str(count)}')\n\n    async def await_choice(self) -> bool:\n        choice = await self.dlg\n        return choice\n","repo_name":"WasuMrTomass0/Organizer","sub_path":"src/gui/dialog_container.py","file_name":"dialog_container.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"28895183039","text":"import os\nimport sys\n\nsys.path.insert(0,os.path.abspath('.'))\nsys.path.insert(0,os.path.abspath('..'))\nsys.path.insert(0,os.path.abspath('../pyreg'))\nsys.path.insert(0,os.path.abspath('../pyreg/libraries'))\nimport matplotlib as matplt\nfrom pyreg.config_parser import MATPLOTLIB_AGG\nif MATPLOTLIB_AGG:\n    matplt.use('Agg')\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport random\nfrom pyreg.utils import apply_affine_transform_to_map_multiNC, get_inverse_affine_param, compute_warped_image_multiNC, \\\n    update_affine_param, identity_map_multiN\nimport pyreg.simple_interface as SI\nimport pyreg.fileio as FIO\ntorch.cuda.set_device(3)\n    \ndef setup_pair_register():\n    \"\"\"\n    in this step, we do some stepup for mermaid\n    :return:\n    \"\"\"\n    register_param = {}\n    si = SI.RegisterImagePair()\n    register_param['si'] = si\n    register_param['model0_name']= 'affine_map'\n    register_param['model1_name']= 'svf_vector_momentum_map'\n\n    return register_param\n\n\ndef read_source_and_moving_img(moving_path, target_path):\n    \"\"\"\n    read img into numpy\n    :return:\n    \"\"\"\n    im_io = FIO.ImageIO()\n\n    moving, hdrc0, spacing0, _ = im_io.read_to_nc_format(filename=moving_path, intensity_normalize=True)\n    target, hdrc, spacing1, _ = im_io.read_to_nc_format(filename=target_path, intensity_normalize=True)\n\n    return moving, target, spacing1, hdrc0\n\ndef register_pair_img(moving, target, spacing, register_param, hdrc0=None):\n    si = register_param['si']\n    model0_name = register_param['model0_name']\n    model1_name = register_param['model1_name']\n\n    #moving[np.where(moving==0)]=0.6\n    # si.set_initial_map(None)\n    # si.register_images(moving, target, spacing,\n    #                         model_name=model0_name,\n    #                         use_multi_scale=True,\n    #                    visualize_step=None,\n    #                         rel_ftol=1e-7,\n    #                         json_config_out_filename='output_cur_settings.json',\n    #                         compute_inverse_map=True,\n    #                         params='cur_settings.json')\n    #\n    # Ab = si.opt.optimizer.ssOpt.model.Ab\n    # affine_param = Ab.detach().cpu().numpy().reshape((4, 3))\n    # affine_param = np.transpose(affine_param)\n    # print(\" the affine param is {}\".format(affine_param))\n    #\n    # inv_Ab = get_inverse_affine_param(Ab.detach())\n    # #id_Ab = update_affine_param(Ab,inv_Ab)\n    # identity_map = torch.Tensor(identity_map_multiN(moving.shape, spacing)).cuda()\n    # inversed_affine_map = apply_affine_transform_to_map_multiNC(inv_Ab, identity_map)\n    #\n    #\n    #\n    #\n    # print(\"let's come to step 2 \")\n    #\n    # affine_map = si.opt.optimizer.ssOpt.get_map()\n    # si.opt = None\n    # si.set_initial_map(affine_map.detach())\n\n    si.register_images(moving, target, spacing,\n                            model_name=model1_name,\n                            use_multi_scale=True,\n                       visualize_step=None,\n                            rel_ftol=1e-7,\n                            json_config_out_filename='output_settings_lbfgs.json',\n                            compute_inverse_map=True,\n                       similarity_measure_type='lncc',\n                            params='cur_settings_lbfgs.json')\n\n    warped_image = si.get_warped_image()\n    inversed_map_svf = si.get_inverse_map().detach()\n    inversed_map =  compute_warped_image_multiNC(inversed_affine_map,inversed_map_svf,spacing,1)\n\n    #inv_Ab = update_affine_param(inv_Ab, inv_Ab)\n    #inversed_map = apply_affine_transform_to_map_multiNC(inv_Ab, inversed_map_svf)\n    #forward_map = si.get_map()\n    forward_map=si.opt.optimizer.ssOpt.get_map()\n    forward_inversed_map =  compute_warped_image_multiNC(forward_map, inversed_map,spacing,1)\n    difference = forward_inversed_map.detach().cpu().numpy()-identity_map.detach().cpu().numpy()\n    print(difference)\n    print(np.sum(difference))\n\n    return inversed_map, warped_image\n\n\ndef read_mesh_into_tensor():\n    ##########  your code here ############33\n\n    # example\n    #  write a new function     read_mesh_into_tensor    B*3*N*1*1\n    ##################    using randomized mesh for debugging   ###############################3\n    mesh = torch.rand(1, 3, 200, 1, 1).cuda()\n\n    return mesh\n\ndef do_points_interoplation(inversed_map, points, spacing):\n    if isinstance(points, np.ndarray):\n        points = torch.from_numpy(points)\n\n    #points_wraped = compute_warped_image_multiNC(inversed_map, poipointsnts, spacing=spacing, spline_order=1, zero_boundary=True)\n    points_wraped = F.grid_sample(inversed_map, points.permute([0, 2, 3, 4, 1]), mode='bilinear', padding_mode='border')\n    return points_wraped\n\n\ndef main():\n    moving_img_path = '/playpen/zhenlinx/Data/OAI_segmentation/Nifti_rescaled/9085290_20040915_SAG_3D_DESS_LEFT_016610243503_image.nii.gz'\n    target_img_path = '/playpen/zhenlinx/Data/OAI_segmentation/Nifti_rescaled/9085290_20051103_SAG_3D_DESS_LEFT_016610952703_image.nii.gz'\n\n    register_param = setup_pair_register()\n    moving,target, spacing, _ = read_source_and_moving_img(moving_img_path, target_img_path)\n    inversed_map = register_pair_img(moving,target, spacing, register_param)\n    mesh = read_mesh_into_tensor()\n    interoplated_result = do_points_interoplation(inversed_map, mesh, spacing)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"uncbiag/pregis_net","sub_path":"mermaid/demos/demo_for_mesh_interpolation.py","file_name":"demo_for_mesh_interpolation.py","file_ext":"py","file_size_in_byte":5358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41188050015","text":"#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\nfrom setuptools import setup, find_packages\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', ]\n\ntest_requirements = ['pytest>=3', ]\n\nsetup(\n    author=\"Chinmay Shrinivas Joshi\",\n    author_email='chinmaysjoshi@gmail.com',\n    python_requires='>=3.6',\n    classifiers=[\n        'Development Status :: 2 - Pre-Alpha',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\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    ],\n    description=\"Shunya is all about creating something from nothing, a blank slate per se with some helpers on adrenaline\",\n    entry_points={\n        'console_scripts': [\n            'shunya=shunya.cli:main',\n        ],\n    },\n    install_requires=requirements,\n    license=\"MIT license\",\n    long_description=readme + '\\n\\n' + history,\n    include_package_data=True,\n    keywords='shunya',\n    name='shunya',\n    packages=find_packages(include=['shunya', 'shunya.*']),\n    test_suite='tests',\n    tests_require=test_requirements,\n    url='https://github.com/chinmaysjoshi/shunya',\n    version='0.0.1',\n    zip_safe=False,\n)\n","repo_name":"chinmaysjoshi/shunya","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3290942704","text":"import os\nimport pytest\nimport shutil\nimport subprocess\nimport sys\n\nif sys.version_info >= (3, 0):\n    from io import StringIO\nelse:\n    from StringIO import StringIO\n\nfrom codeclimate_test_reporter.components.runner import Runner\nfrom codeclimate_test_reporter.__init__ import __version__ as reporter_version\nfrom .utils import ApiMock\n\n\ndef test_version():\n    out = StringIO()\n    runner = Runner([\"--version\"], out=out)\n\n    return_code = runner.run()\n\n    assert(return_code == 0)\n    assert(out.getvalue().strip() == reporter_version)\n\ndef test_run():\n    os.environ[\"CODECLIMATE_API_HOST\"] = \"http://example.com\"\n\n    api_mock = ApiMock()\n    api_mock.setup(200)\n\n    runner = Runner([\"--file\", \"./coverage.txt\", \"--token\", \"token\"])\n\n    orig_dir = os.getcwd()\n    os.chdir(\"./tests/fixtures\")\n\n    subprocess.call([\"git\", \"init\"])\n    subprocess.call([\"git\", \"config\", \"user.name\", \"Test User\"])\n    subprocess.call([\"git\", \"config\", \"user.email\", \"test@example.com\"])\n    subprocess.call([\"git\", \"commit\", \"--allow-empty\", \"--message\", \"init\"])\n\n    try:\n        return_code = runner.run()\n\n        assert(return_code == 0)\n    finally:\n        del os.environ[\"CODECLIMATE_API_HOST\"]\n        os.chdir(orig_dir)\n        shutil.rmtree(\"./tests/fixtures/.git\")\n        api_mock.cleanup()\n\ndef test_run_api_500_error():\n    os.environ[\"CODECLIMATE_API_HOST\"] = \"http://example.com\"\n\n    api_mock = ApiMock()\n    api_mock.setup(500)\n\n    err = StringIO()\n    runner = Runner([\"--file\", \"./coverage.txt\", \"--token\", \"token\"], err=err)\n\n    orig_dir = os.getcwd()\n    os.chdir(\"./tests/fixtures\")\n\n    subprocess.call([\"git\", \"init\"])\n    subprocess.call([\"git\", \"config\", \"user.name\", \"Test User\"])\n    subprocess.call([\"git\", \"config\", \"user.email\", \"test@example.com\"])\n    subprocess.call([\"git\", \"commit\", \"--allow-empty\", \"--message\", \"init\"])\n\n    try:\n        return_code = runner.run()\n\n        assert(return_code == 1)\n        assert(\"500 Server Error\" in err.getvalue().strip())\n    finally:\n        del os.environ[\"CODECLIMATE_API_HOST\"]\n        os.chdir(orig_dir)\n        shutil.rmtree(\"./tests/fixtures/.git\")\n        api_mock.cleanup()\n","repo_name":"codeclimate/python-test-reporter","sub_path":"tests/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"8094459455","text":"# 5063_TGN.py\n# 구현\n\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\nfor i in range(n):\n    r, e, c = map(int, input().split())\n    if e-c > r:\n        print('advertise')\n    elif e-c == r:\n        print('does not matter')\n    else:\n        print('do not advertise')","repo_name":"ParkHyeonChae/Algorithm","sub_path":"baekjoon/bronze3/5063_TGN.py","file_name":"5063_TGN.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26056727438","text":"import os #Libreria para funciones de limpiar pantalla y pausa\r\nfrom random import randint #Libreria para generar opcion aleatoria de la computadora\r\n# importar el tipo de dato para utilizar pilas y colas\r\nfrom collections import deque\r\n\r\nCiclo = True #Ciclo para Repetir el juego \r\n# definir la pila\r\nHistorial = deque()\r\nwhile Ciclo:\r\n    respuesta_pc = randint(1, 3) # 1 piedra 2 papel 3 tijera\r\n    os.system(\"cls\")\r\n    print (\"****************** Bienvenidos *********************\")\r\n    print (\"************ Juego Piedra, Papel o Tijera **********\")\r\n    print (\"Ingrese su opción: \") #Menu para selección del jugador\r\n    print (\"1. Piedra\")\r\n    print (\"2. Papel\")\r\n    print (\"3. Tijera\")\r\n    Opcion = int(input(\"Su opción: \"))\r\n\r\n    if Opcion > 0 and Opcion < 4: #Validación de opción correcta\r\n        os.system(\"cls\")\r\n        if Opcion == 1:\r\n            Jugador = \"Jugador: Piedra\"\r\n        elif Opcion == 2:\r\n            Jugador = \"Jugador: Papel\"\r\n        elif Opcion == 3:\r\n            Jugador = \"Jugador: Tijera\"\r\n\r\n        if respuesta_pc == 1:\r\n            PC = \"Computadora: Piedra\"\r\n        elif respuesta_pc == 2:\r\n            PC = \"Computadora: Papel\"\r\n        elif respuesta_pc == 3:\r\n            PC = \"Computadora: Tijera\"\r\n\r\n        if respuesta_pc == Opcion: #Resultados del juego\r\n            Resultado = \"Resultado: ¡Empate!\"\r\n        if respuesta_pc == 1 and Opcion == 2:\r\n            Resultado = \"Resultado: ¡Gana Jugador!\"    \r\n        if respuesta_pc == 1 and Opcion == 3:\r\n            Resultado = \"Resultado: ¡Gana Computadora!\"\r\n        if respuesta_pc == 2 and Opcion == 1:\r\n            Resultado = \"Resultado: ¡Gana Computadora!\"\r\n        if respuesta_pc == 2 and Opcion == 3:\r\n            Resultado = \"Resultado: ¡Gana Jugador!\"\r\n        if respuesta_pc == 3 and Opcion == 1:\r\n            Resultado = \"Resultado: ¡Gana Jugador!\"\r\n        if respuesta_pc == 3 and Opcion == 2:\r\n           Resultado = \"Resultado: ¡Gana Computadora!\"\r\n\r\n        print(Jugador) #Mostrar resultados en pantalla\r\n        print(PC)\r\n        print(Resultado)\r\n        os.system(\"pause\")   \r\n \r\n        Historial.append([Jugador, PC, Resultado])  #Almacenar datos a la pila\r\n\r\n    else:\r\n        print(\"Escriba un opción válida\")\r\n        os.system(\"pause\")\r\n\r\n    CicloMenu = True #Ciclo de menu \r\n    while CicloMenu:\r\n        os.system(\"cls\")\r\n        print(\"Deseas?\") #Menu\r\n        print(\"1. Volver a jugar\")\r\n        print(\"2. Ver Resultado Anterior\")\r\n        print(\"3. Salir\")\r\n        Menu = int(input())\r\n\r\n        if Menu == 1:\r\n            CicloMenu = False\r\n        elif Menu == 2:\r\n            os.system(\"cls\")\r\n            if len(Historial) > 0: #Mostrar el historial almacenado\r\n                Ultima = Historial.pop()\r\n                for elem in Ultima:\r\n                    print(elem)\r\n\r\n                os.system(\"pause\")\r\n            else:\r\n                print(\"¡El historial esta vacio!\")\r\n                os.system(\"pause\")    \r\n        elif Menu == 3: #Finalizar el juego\r\n            CicloMenu = False\r\n            Ciclo = False  \r\n\r\n        else:\r\n            print(\"Ingrese un Valor Correcto\")\r\n            os.system(\"pause\")         ","repo_name":"Jrikrd/PROYECTO_2_GRUPO3","sub_path":"pilas-colas/pilasPPT.py","file_name":"pilasPPT.py","file_ext":"py","file_size_in_byte":3196,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30907461823","text":"import time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\n\n\nclass AutoComplete():\n\n    def test(self):\n        baseUrl = \"https://www.goibibo.com\"\n        serv_obj = Service(\"/home/stinger/Downloads/chromedriver_linux64/chromedriver\")\n\n        driver = webdriver.Chrome(service=serv_obj)\n        driver.maximize_window()\n        driver.get(baseUrl)\n        driver.implicitly_wait(3)\n        partialText = \"Dar\"\n        textToSelect = \"Darbhanga, India(DBR)\"  # this thing should be matched\n\n        textElement = driver.find_element(By.XPATH,\"//*[@id='root']/div/div[2]/div[2]/div/div[1]/div[1]/div/div[2]/div/input\")\n        textElement.send_keys(partialText)\n\n        ulElement = driver.find_element(By.ID,\"autoSuggest-list\")\n        inner_html = ulElement.get_attribute(\"innerHTML\")\n        print(inner_html)\n\n        liElements= ulElement.find_elements(By.TAG_NAME, \"li\")\n        time.sleep(2)\n\n        for element in liElements:\n\n            #print(element.text)\n            if textToSelect in element.text:\n                element.click()\n                break\n        time.sleep(4)\n\n\n\n        driver.quit()\n\n\nff=AutoComplete()\n\nff.test()\n\n","repo_name":"Ajayush96/selenium-learning","sub_path":"advance/auto_complete.py","file_name":"auto_complete.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28024844414","text":"dict_number = {}\r\nimport re\r\n\r\nwith open('poem.txt', 'r', encoding='UTF-8') as file:\r\n    data = file.read()\r\n    patt = re.compile('[,?!. :«»—…\\n]')\r\n    word_list = re.split(patt, data.lower(), maxsplit=0)\r\n    print(word_list)\r\n    for words in word_list:\r\n        if words in dict_number and len(words) > 0:\r\n            dict_number[words] += 1\r\n        else:\r\n            dict_number[words] = 1\r\n\r\nfinal_dict = sorted(dict_number.items(),\r\n                    reverse=True,\r\n                    key=lambda kv_pair: kv_pair[1])\r\n# print(final_dict)\r\n\r\nwith open('poem_result.txt', 'w', encoding='UTF-8') as file_new:\r\n    file_new.write(\"Частотный словарь стихотворения\")\r\n    for items in final_dict:\r\n        print(items)\r\n        print(items[0])\r\n        line = \"\\n\" + items[0] + \": \" + str(items[1])\r\n        file_new.write(line)\r\n\r\n# import matplotlib.pyplot as plt\r\n# import pandas as pd\r\n# import seaborn as sns\r\n# df = pd.DataFrame(dict_number, columns=[\"word\", \"freq\"])\r\n#\r\n# fig = plt.figure(figsize=(15, 5))\r\n# ax = sns.barplot(x=\"word\", y=\"freq\", data=df)\r\n# plt.xticks(rotation=45)\r\n# plt.show()\r\n","repo_name":"AnastasiaDobrynina/py21project","sub_path":"list_poem.py","file_name":"list_poem.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27781921157","text":"\"\"\"add_user_role\n\nRevision ID: 0697a5c45603\nRevises: bc8564bdf511\nCreate Date: 2020-10-28 14:25:30.110880\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '0697a5c45603'\ndown_revision = 'bc8564bdf511'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('users', sa.Column('role', sa.String(length=128)))\n\n    op.execute('UPDATE users SET role=\\'guest\\' WHERE role IS NULL')\n\n    # needed due to sqlite limitations\n    with op.batch_alter_table('users') as batch_op:\n        batch_op.alter_column('role', nullable=False)\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('users', 'role')\n    # ### end Alembic commands ###\n","repo_name":"BookBnB/users-service","sub_path":"migrations/versions/0697a5c45603_add_user_role.py","file_name":"0697a5c45603_add_user_role.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10803835587","text":"import os\nimport platform\nfrom enum import Enum\n\n# judge the current operating system\nbase = r'C:' if platform.system() == \"Windows\" else r'/data/wjs'\n\nenv_list = [\n    'RollerBall',\n    '3DBall',\n    'Boat'\n]\n\nreset_config = [None, {\n    'copy': 10\n}]\n\nclass algorithms(Enum):\n    ppo_sep_ac = 1  # AC, stochastic\n    ppo_com = 2  # AC, stochastic\n    # boundary, about the way of calculate `discounted reward`\n    sac = 3  # AC+Q, stochastic, off-policy\n    sac_no_v = 4\n    ddpg = 5  # AC+Q, deterministic, off-policy\n    td3 = 6  # AC+Q, deterministic, off-policy\n\n\nunity_file = [\n    r'C:/UnityBuild/RollerBall/OneFloor/RollerBall-custom.exe',#0\n    r'3dball',#1\n    r'C:/UnityBuild/Boat/first/BoatTrain.exe',#2\n    r'C:/UnityBuild/Boat/second/BoatTrain.exe',#3\n    r'C:/UnityBuild/Boat/interval1/BoatTrain.exe',#4\n    r'C:/UnityBuild/Boat/no_border/BoatTrain.exe',#5\n    r'C:/UnityBuild/Boat/no_border2/BoatTrain.exe'#6\n]\n\nmax_episode = 50000  # max episode num or step num, depend on whether episode-update or step-update\n\nconfig = {\n    'hyper parameters': {\n        # set the temperature of SAC, auto adjust or not\n        'alpha': 0.2,\n        'auto_adaption': True,\n\n        'ployak': 0.995, # range from 0. to 1.\n        'epsilon': 0.2, # control the learning stepsize of clip-ppo \n        'beta': 1.0e-3, # coefficient of entropy regularizatione\n        'lr': 5.0e-4,\n        'actor_lr': 0.0001,\n        'critic_lr': 0.0002,\n        'tp_lr': 0.001,\n        'reward_lr': 0.001,\n        'gamma': 0.99,\n        'lambda': 0.95,\n        'action_bound': 1,\n        'decay_rate': 0.7,\n        'decay_steps': 100,\n        'stair': False,\n        'max_episode': max_episode,\n        'base_sigma': 0.1, # only work on stochastic policy\n        'assign_interval': 4 # not use yet\n    },\n\n    'train config': {\n        # choose algorithm\n        'algorithm': algorithms.sac,\n        'init_max_step': 300,\n        'max_step': 1000, # use for both on-policy and off-policy, control the max step within one episode.\n        'max_episode': max_episode,\n        'max_sample_time': 20,\n        'till_all_done': True, # use for on-policy leanring\n        'start_continuous_done': False,\n        # train mode, .exe or unity-client && train or inference\n        'train': True,\n        'unity_mode': False,\n        'unity_file': unity_file[0].replace('C:',f'{base}'),\n        'port': 5006,\n        # trick\n        'use_trick': True,\n        # excel\n        'excel_record': False,\n        'excel_record_frequency': 10,\n        # mongodb\n        'mongo_record': False,\n        'mongo_record_frequency': 10,\n        'mongo_record_all': False,\n        # shuffle batch or not\n        'random_batch': True,\n        'batchsize': 100,\n        'epoch': 1,\n        # checkpoint\n        'save_frequency': 20,\n        # set the agents' number and control mode\n        'dynamic_allocation': True,\n        'reset_config': reset_config[1],\n        # some sets about using replay_buffer\n        'use_replay_buffer': True, # on-policy or off-policy\n        'use_priority' : False,\n        'buffer_size' : 10000,\n        'buffer_batch_size': 100,\n        'max_learn_time' : 20\n    },\n\n    'record config': {\n        'basic_dir': r'C:/RLData/'.replace('C:',f'{base}'),\n        'log_basic_dir': r'C:/RLData/logs/'.replace('C:',f'{base}'),\n        'excel_basic_dir': r'C:/RLData/excels/'.replace('C:',f'{base}'),\n        'checkpoint_basic_dir': r'C:/RLData/models/'.replace('C:',f'{base}'),\n        'config_basic_dir': r'C:/RLData/config/'.replace('C:',f'{base}'),\n        'project_name': env_list[0],\n        'remark': r'sac_onefloor',\n        'run_id': r'3',\n        'logger2file' : False\n    },\n\n    'config_file': r\"\",\n    'ps': r\"time penalty=-0.01\",\n\n    'clean_list': [\n        r'Boat\\sac_off_no_border0',\n        r'Boat\\sac_off_no_border1',\n        r'Boat\\sac_off_no_border2',\n        r'RollerBall\\sac_onefloor0',\n        r'RollerBall\\sac_onefloor1',\n        r'RollerBall\\sac_onefloor2',\n    ]\n}\n","repo_name":"StepNeverStop/RLwithUnity","sub_path":"config_file.py","file_name":"config_file.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"77"}
+{"seq_id":"20186162850","text":"import luigi\nimport pandas as pd\nimport joblib\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\nimport ast\n\nclass CleanDataTask(luigi.Task):\n    tweet_file = luigi.Parameter()\n    output_file = luigi.Parameter(default='clean_data.csv')\n\n\n    def run(self):\n        df = pd.read_csv(\"airline_tweets.csv\", encoding=\"iso-8859-1\", usecols=['airline_sentiment', 'tweet_coord'])\n        df = df[df['tweet_coord'].notna()]\n        df.drop(df[df.tweet_coord == '[0.0, 0.0]'].index, inplace=True)\n        df.to_csv(self.output_file, index=False, encoding=\"iso-8859-1\")\n\n    def output(self):\n        return luigi.LocalTarget(self.output_file)\n\n\nclass TrainingDataTask(luigi.Task):\n    tweet_file = luigi.Parameter()\n    cities_file = luigi.Parameter(default='cities.csv')\n    output_file = luigi.Parameter(default='features.csv')\n\n    def requires(self):\n        return CleanDataTask(self.tweet_file)\n\n    def run(self):\n        cities_df = pd.read_csv(self.cities_file, encoding=\"iso-8859-1\")\n        df = pd.read_csv(self.input().path, encoding=\"iso-8859-1\")\n        df[\"tweet_coord\"] = df[\"tweet_coord\"].str.findall(r\"[-+]?\\d*\\.\\d+|\\d+\")\n\n        def closest_city_finder(x, minimum=float(\"inf\")):\n            min_city_name = None\n            x_lat, x_long = float(x[0]), float(x[1])\n            for lat, longtitude, name in zip(cities_df['latitude'], cities_df['longitude'], cities_df['name']):\n                dist = (x_lat - lat) ** 2 + (x_long - longtitude) ** 2\n                if dist < minimum:\n                    min_city_name = name\n                    minimum = dist\n            return min_city_name\n\n        # find the closest city name using Euclidean distance\n        df[\"closest_city_name\"] = [closest_city_finder(x) for x in df['tweet_coord']]\n\n        # convert city names to one hot vectors\n        name_onehot = pd.get_dummies(cities_df['name'], columns=['name'])\n\n        # create a mapping between closest city name and one hot vector\n        dict_ = {}\n        for k, v in zip(cities_df['name'], name_onehot.values.tolist()):\n            if k not in dict_:\n                dict_[k] = v\n\n        # add column X that is the one hot vector for each string.\n        df[\"X\"] = df[\"closest_city_name\"].map(lambda x: list(dict_[x]))\n\n        # convert sentiment to one hot values as given in the problem statement.\n        dict_ = {'negative': 0, 'neutral': 1, 'positive': 2}\n        df['y'] = df[\"airline_sentiment\"].map(lambda x: dict_[x])\n\n        output_df = pd.DataFrame(df[[\"X\", \"y\", \"closest_city_name\"]], columns=[\"X\", \"y\", \"closest_city_name\"])\n        output_df.to_csv(self.output_file, encoding=\"ISO-8859-1\", index=False)\n\n    def output(self):\n        return luigi.LocalTarget(self.output_file)\n\n\n\nclass TrainModelTask(luigi.Task):\n\n    tweet_file = luigi.Parameter()\n    output_file = luigi.Parameter(default='model.pkl')\n\n    def requires(self):\n        return TrainingDataTask(self.tweet_file)\n\n    def run(self):\n        features = pd.read_csv(self.input().path, encoding=\"ISO-8859-1\")\n        # convert string of lists to lists.\n        X = [ast.literal_eval(x) for x in features[\"X\"].values.tolist()]\n        y = features['y'].values.tolist()\n        X = np.asarray(X).astype(float)\n\n        # basic classification model, since the problem statements clearly states to focus only on the pipeline.\n        clf = LogisticRegression(random_state=7, multi_class='multinomial').fit(X, y)\n        joblib.dump(clf, 'model.pkl')\n\n    def output(self):\n        return luigi.LocalTarget(self.output_file)\n\n\nclass ScoreTask(luigi.Task):\n    tweet_file = luigi.Parameter()\n    output_file = luigi.Parameter(default='scores.csv')\n\n    def requires(self):\n        return {\"model\": TrainModelTask(self.tweet_file), \"features\": TrainingDataTask(self.tweet_file)}\n\n    def run(self):\n        features = pd.read_csv(self.input()[\"features\"].path, encoding=\"ISO-8859-1\")\n        X = [ast.literal_eval(x) for x in features[\"X\"].values.tolist()]\n        y = features['y'].values.tolist()\n        X = np.asarray(X).astype(float)\n\n        # load model and make predictions\n        clf_load = joblib.load(self.input()[\"model\"].path)\n        y_pred_prob = clf_load.predict_proba(X)\n\n        score = pd.DataFrame(y_pred_prob, columns=[\"negative\", \"neutral\", \"positive\"])\n        score[\"PREDICTED_LABEL\"] = [np.argmax(x) for x in score[[\"negative\", \"neutral\", \"positive\"]].values.tolist()]\n        score[\"TRUE_LABEL\"] = y\n        score[\"city\"] = features[\"closest_city_name\"]\n\n        score = score.drop_duplicates()\n        score = score.sort_values(\"positive\", ascending=False)\n\n        score.to_csv(self.output_file, encoding=\"ISO-8859-1\", index=False)\n\n    def output(self):\n        return luigi.LocalTarget(self.output_file)\n\nif __name__ == \"__main__\":\n    luigi.run()\n","repo_name":"karthmnz/ML_pipeline","sub_path":"pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":4795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34150334760","text":"class Solution(object):\n    def lengthOfLastWord(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"\n        if s == '': return 0\n        s = s.split(' ')\n        for ele in s[::-1]:\n            if ele != '':\n                return len(ele)\n        return 0\n","repo_name":"Iansdfg/Leetcode2ndTime","sub_path":"58. Length of Last Word.py","file_name":"58. Length of Last Word.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31187135742","text":"# Data Transfer Objects. Not Models\n\n\nclass DishQuantities:\n\n    def __init__(self, dish_id, dish_name, dish_qty, portion_count):\n        self.dish_id = dish_id\n        self.dish_name = dish_name\n        self.dish_qty = dish_qty\n        self.portion_count = portion_count\n\n\nclass IngredientShoppingList:\n\n    def __init__(self, ingredient_name, total_ingredient_weight, total_cost_price):\n        self.ingredient_name = ingredient_name\n        self.total_ingredient_weight = total_ingredient_weight\n        self.total_cost_price = total_cost_price\n","repo_name":"chaturv/fnfweb","sub_path":"operations/dto.py","file_name":"dto.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11604142867","text":"import asyncio\n\nfrom sqlalchemy.future import select\nfrom sqlalchemy.orm import selectinload\nfrom database.app.db import DB\nfrom models import Employee, TimeTracking\n\ndb_instance = DB()\n\n# Добавление нового сотрудника (Employee)\nnew_employee = Employee(\n    full_name=\"John Doe\",\n    department=\"HR\",\n    role=\"Manager\",\n    img_path=\"img/john_doe.jpg\"\n)\n\n# Добавление новой записи о времени (TimeTracking)\nnew_time_tracking = TimeTracking(\n    employee=new_employee,  # Связь с Employee\n    date=\"2023-08-25\",\n    clock_in=\"09:00:00\",\n    clock_out=\"17:00:00\",\n    absence_reason=\"Sick\",\n    total_time=\"08:00:00\"\n)\n\n\nasync def create_objects():\n    async with db_instance.get_async_session() as session:\n        # Добавление объектов в сессию\n        session.add(new_employee)\n        session.add(new_time_tracking)\n        await session.commit()\n\n\nif __name__ == \"__main__\":\n    asyncio.run(create_objects())\n","repo_name":"egorioch/local_time_tracking","sub_path":"database/app/create_objects.py","file_name":"create_objects.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18298710903","text":"from flask import Flask, request, jsonify, send_from_directory, render_template\nfrom flask_cors import CORS\nimport os\nimport subprocess\nimport shlex\n\napp = Flask(__name__)\nCORS(app)\nUPLOAD_FOLDER = 'uploads'\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n# Ensure the upload directory exists\nif not os.path.exists(UPLOAD_FOLDER):\n    os.makedirs(UPLOAD_FOLDER)\n\n# Function to get file type\ndef get_file_type(file_path):\n    magic_mime = magic.Magic()\n    return magic_mime.from_file(file_path)\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/upload', methods=['POST'])\ndef upload_file():\n    if 'file' not in request.files:\n        return jsonify({'error': 'No file part'})\n\n    uploaded_file = request.files['file']\n    if uploaded_file.filename == '':\n        return jsonify({'error': 'No selected file'})\n\n    file_path = os.path.join(app.config['UPLOAD_FOLDER'], uploaded_file.filename)\n    uploaded_file.save(file_path)\n    \n    return jsonify({'message': 'File uploaded successfully'})\n\n@app.route('/files/<filename>', methods=['GET'])\ndef get_file_details(filename):\n    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n    if not os.path.exists(file_path):\n        return jsonify({'error': 'File not found'})\n\n    # Run the 'file' command on the file to get its details\n    try:\n        command = f\"file '{file_path}'\"\n        result = subprocess.check_output(shlex.split(command), universal_newlines=True)\n    except subprocess.CalledProcessError as e:\n        return jsonify({'error': 'Failed to determine file details'})\n\n    # Parse the 'file' command output and extract relevant details\n    file_details = {\n        'filename': filename,\n        'file_details': result.strip()\n    }\n\n    return jsonify(file_details)\n# def get_file_details(filename):\n#     file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n#     if not os.path.exists(file_path):\n#         return jsonify({'error': 'File not found'})\n    \n#     file_type = get_file_type(file_path)\n#     return jsonify({'file_type': file_type})\n\n@app.route('/files/<filename>', methods=['PUT'])\ndef update_file(filename):\n    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n    if not os.path.exists(file_path):\n        return jsonify({'error': 'File not found'})\n\n    # You can implement the update logic here, such as replacing the file contents\n\n    return jsonify({'message': 'File updated successfully'})\n\n@app.route('/files/<filename>', methods=['DELETE'])\ndef delete_file(filename):\n    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n    if not os.path.exists(file_path):\n        return jsonify({'error': 'File not found'})\n\n    os.remove(file_path)\n    return jsonify({'message': 'File deleted successfully'})\n\n@app.route('/download/<filename>', methods=['GET'])\ndef download_file(filename):\n    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n    if not os.path.exists(file_path):\n        return jsonify({'error': 'File not found'})\n\n    return send_from_directory(app.config['UPLOAD_FOLDER'], filename, as_attachment=True)\n\n@app.route('/files', methods=['GET'])\ndef get_all_files():\n    files = [filename for filename in os.listdir(app.config['UPLOAD_FOLDER']) if os.path.isfile(os.path.join(app.config['UPLOAD_FOLDER'], filename))]\n    return jsonify({'files': files})\n\nif __name__ == '__main__':\n    app.run(host='127.0.0.1', port=5000)\n","repo_name":"munna0912/RapidFort_Project","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3043513768","text":"from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n    dependencies = [\n        ('product', '0006_product_query_count'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='AIAttachment',\n            fields=[\n                (\n                    'id',\n                    models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True),\n                ),\n                ('attachment', models.FileField(upload_to='ai/%Y/%m/%d', verbose_name='File')),\n            ],\n            options={\n                'verbose_name': \"AIPics's attachment\",\n                'verbose_name_plural': \"AIPics's attachments\",\n            },\n        ),\n        migrations.CreateModel(\n            name='AIPics',\n            fields=[\n                (\n                    'id',\n                    models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True),\n                ),\n                ('client', models.CharField(max_length=40, verbose_name='Zg\\u0142aszaj\\u0105cy')),\n                ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='Utworzone')),\n                ('original_width', models.IntegerField()),\n                ('original_height', models.IntegerField()),\n                ('width', models.IntegerField()),\n                ('height', models.IntegerField()),\n                ('device_name', models.CharField(max_length=100)),\n                ('flash_used', models.NullBooleanField()),\n                ('was_portrait', models.NullBooleanField()),\n                ('product', models.ForeignKey(to='product.Product', on_delete=models.CASCADE)),\n            ],\n            options={\n                'verbose_name': 'AIPics',\n                'verbose_name_plural': 'AIPics',\n            },\n        ),\n        migrations.AddField(\n            model_name='aiattachment',\n            name='ai_pics',\n            field=models.ForeignKey(to='ai_pics.AIPics', on_delete=models.CASCADE),\n        ),\n    ]\n","repo_name":"KlubJagiellonski/pola-backend","sub_path":"pola/ai_pics/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"77"}
+{"seq_id":"43871954475","text":"def path_finder(wire):\r\n    '''\r\n    This function finds the wires' path from the given commands\r\n    and returns it in (x, y) format\r\n    '''\r\n    x, y = 0, 0\r\n    path = [(0, 0)]\r\n    for j in wire:\r\n        direction = j[0]\r\n        distance = int(j[1:])\r\n        if direction == 'U':\r\n            y += distance\r\n        elif direction == 'D':\r\n            y -= distance\r\n        elif direction == 'R':\r\n            x += distance\r\n        elif direction == 'L':\r\n            x -= distance\r\n        else:\r\n            print(\"Wrong direction!\")\r\n        path.append((x, y))\r\n    \r\n    return path\r\n\r\ndef closest_intersection(path_1, path_2):\r\n    '''\r\n    This function finds the closest intersection point and it's manhattan distance \r\n    '''\r\n    inter = 0\r\n    \r\n    for i in range(len(path_1)-1):\r\n        # here i'm comparing the current point with the point after it to see if the x value is staying the same\r\n        # if it's staying the same this means that the line is moving in the y direction\r\n        if path_1[i][0] == path_1[i+1][0]:\r\n            res1 = 1\r\n        # this code sees if the y value is staying the same\r\n        # if the y value is staying the same this means that the line is moving in the x direction\r\n        else:\r\n            res1 = 0\r\n        for j in range(len(path_2)-1):\r\n            # here I'm doing the same as the previous one but this time on the second wire\r\n            if path_2[j][0] == path_2[j+1][0]:\r\n                res2 = 1\r\n            else:\r\n                res2 = 0\r\n            # To see whether the two lines intersect they shouldn't be parrallel so I'm ignoring the lines with the same axis staying the same\r\n            if res1 == res2:\r\n                continue\r\n            else:\r\n                if (path_1[i] and path_2[j]) != (0, 0):\r\n                    if res1:\r\n                        # this compares the fixed value from the first line with the moving value from the second line \r\n                        if min(path_2[j][0], path_2[j+1][0]) <= path_1[i][0] <= max(path_2[j][0], path_2[j+1][0]) and min(path_1[i][1], path_1[i+1][1]) <= path_2[j][1] <= max(path_1[i][1], path_1[i+1][1]):\r\n                            # this calculates the distance from the nearest intersection to the start point\r\n                            if inter == 0 or sum((abs(path_1[i][0]), abs(path_2[j][1]))) < inter:\r\n                                inter = sum((abs(path_1[i][0]), abs(path_2[j][1])))\r\n                    # this is the same as above but this works if the first lines' y value is fixed   \r\n                    else:\r\n                        if min(path_2[j][1], path_2[j+1][1]) <= path_1[i][1] <= max(path_2[j][1], path_2[j+1][1]) and min(path_1[i][0], path_1[i+1][0]) <= path_2[j][0] <= max(path_1[i][0], path_1[i+1][0]):\r\n                            if inter == 0 or sum((abs(path_2[j][0]), abs(path_1[i][1]))) < inter:\r\n                                inter = sum((abs(path_2[j][0]), abs(path_1[i][1])))\r\n  \r\n                                                            \r\n    return inter \r\n\r\n\r\nwires_file = open('Crossed Wires.txt', 'r')\r\nwire_1 = wires_file.readline().split(',')\r\nwire_2 = wires_file.readline().split(',')\r\n\r\nprint(closest_intersection(path_finder(wire_1), path_finder(wire_2)))\r\n\r\nwires_file.close()\r\n\r\n","repo_name":"diyeddin/AoC-Day-3-Crossed-Wires","sub_path":"Crossed Wires AoC/Crossed Wires (AoC).py","file_name":"Crossed Wires (AoC).py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23681546206","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('inventory', '0002_auto_20150626_1805'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='manufacturer',\n            name='rep',\n            field=models.CharField(null=True, max_length=128, blank=True),\n        ),\n        migrations.AlterField(\n            model_name='vendor',\n            name='lookup_url',\n            field=models.CharField(null=True, help_text='url pattern to look up catalog number', max_length=128, blank=True),\n        ),\n    ]\n","repo_name":"melizalab/django-lab-inventory","sub_path":"inventory/migrations/0003_auto_20150626_1807.py","file_name":"0003_auto_20150626_1807.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"77"}
+{"seq_id":"23344220091","text":"import requests\nimport mysql.connector\n\nfrom flask import Flask, request, make_response, jsonify\n\napp = Flask(__name__)\n\nAPI_URL = 'http://127.0.0.1:5000'\n\ncon = mysql.connector.connect(\n    host='localhost',\n    port=3309,\n    database='db_ApiExterna',\n    user='root',\n    password='my-secret-pw')\n\n\n@app.route('/todos', methods=['GET'])\ndef get_todos():\n    consulta_sql = \"select * from tb_Todos\"\n    cursor = con.cursor()\n    cursor.execute(consulta_sql)\n    todosDoBancoDeDados = cursor.fetchall()\n\n    todos = []\n    for todo in todosDoBancoDeDados:\n        dicTodo = {\"userId\": todo[0],\n                   \"id\": todo[1],\n                   \"title\": todo[2],\n                   \"completed\": todo[3]\n                   }\n        todos.append(dicTodo)\n\n    if (con.is_connected()):\n        con.close()\n        cursor.close()\n        print(\"Conexão ao MySQL encerrada\")\n\n\n    return make_response(jsonify(todos), 200)\n\n\n@app.route('/todos', methods=['POST'])\ndef create_todo():\n    cursor = con.cursor()\n\n    todo = request.get_json()\n\n    userId = todo['userId']\n    id = todo['id']\n    title = todo['title']\n    completed = todo['completed']\n\n    cursor.execute('INSERT INTO tb_Todos (userId, id, title, completed) values (%s, %s, %s, %s)',\n                   (userId, id, title, completed))\n\n    con.commit()\n\n    response = requests.post(f'{API_URL}/todos', json=todo)\n\n    if (con.is_connected()):\n        con.close()\n        cursor.close()\n\n    return make_response(jsonify(response.text), response.status_code)\n\n\n@app.route('/todos/<int:id>', methods=['DELETE'])\ndef delete_todo(id):\n    cursor = con.cursor()\n    cursor.execute('DELETE FROM tb_Todos WHERE id = %s', id)\n    con.commit()\n\n    response = requests.delete(f'{API_URL}/todos/{id}')\n\n    if (con.is_connected()):\n        con.close()\n        cursor.close()\n        print(\"Conexão ao MySQL encerrada\")\n\n    return jsonify({'mensagem:' f'Cliente {id} excluido com sucesso'}), response.status_code\n\n\nif __name__ == '__main__':\n    app.run(debug=True, port=5001)\n\n\n","repo_name":"MGLMendes/api-ac4","sub_path":"aula2.py","file_name":"aula2.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72618485689","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport time\nimport pandas as pd\nimport numpy as np\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york city': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\ncities = CITY_DATA.keys()\n\nmonths =['january','february','march','april','may','june','all']\n\ndays = ['sunday','monday','tuesday','wednesday','thursday','friday','saturday','all']\n\ndef get_filters():\n    print('Hello! Let\\'s explore some US bikeshare data!')\n    \n    # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n    while True:\n        city = input('Which city you want to analyze?\\n1. Chicago \\n2. New York City\\n3. Washington')\n        city = city.lower()\n    \n        if city in cities:\n            print('\\nSo you chose {}!\\n'.format(city.title()))\n            break\n        else:\n            print('\\nInvalid input. Please check that you are writing one of the three cities.\\n')\n\n\n    # TO DO: get user input for month (all, january, february, ... , june)\n    while True:\n        \n        month=input('Do you want to specify a month? Type one from the first six months, If not type \"all\".\\n1. January\\n2. February\\n3. March\\n4. April\\n5. May\\n6. June\\n7. All')\n        month=month.lower()\n        \n        if month in months:\n            print('\\nSo you chose {}!\\n'.format(month.title())) \n            break\n        else:\n             print('\\nInvalid input. Please check that you are writing the right answer.\\n')\n        \n\n    # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n    while True:\n        day = input('Do you want to specify a day? Type the day name, If not type \"all\"')\n        day = day.lower()\n        \n        if day in days:\n            print('\\nSo you chose {}!'.format(day.title()))\n            break\n        else:\n            print('\\nInvalid input. Please check that you are writing the right answer.\\n')\n\n\n    print('-'*40)\n    return city, month, day\n\ndef load_data(city, month, day):                  \n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    df = pd.read_csv(CITY_DATA[city])\n    \n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n    \n    df['month'] = df['Start Time'].dt.month\n    \n    df['day_of_week'] = df['Start Time'].dt.day_name()\n     \n    if month != 'all':\n        \n        month = months.index(month) + 1\n        df = df[df['month'] == month]\n\n    if day != 'all':\n        \n        df = df[df['day_of_week'] == day.title()]\n             \n\n    return df\n\n\ndef time_stats(df):\n    \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n\n    print('\\nCalculating The Most Frequent Times of Travel...\\n')\n    start_time = time.time()\n\n    # TO DO: display the most common month\n    print('\\nThe most common month is {}'.format(df['month'].mode()[0]))\n               \n    # TO DO: display the most common day of week\n    print('\\nThe most common day is {}'.format(df['day_of_week'].mode()[0]))\n                  \n\n    # TO DO: display the most common start hour\n    df['hour'] = df['Start Time'].dt.hour\n    print('\\nThe most common start hour is {}'.format(df['hour'].mode()[0]))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # TO DO: display most commonly used start station\n    print('\\n The most commonly used start station is {}'.format(df['Start Station'].mode()[0]))\n\n\n    # TO DO: display most commonly used end station\n    print('\\n The most commonly end start station is {}'.format(df['End Station'].mode()[0]))              \n\n\n    # TO DO: display most frequent combination of start station and end station trip\n    df['comb. of start and end stations'] = df['Start Station'] + \" to \" + df['End Station']\n    print('\\n The most frequent combination of start station and end station trip is: {}'.format(df['comb. of start and end stations'].mode()[0]))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef trip_duration_stats(df):\n    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n    print('\\nCalculating Trip Duration...\\n')\n    start_time = time.time()\n\n    # TO DO: display total travel time\n    print('Total travel time is {}\\n'.format(np.sum(df['Trip Duration'])))              \n\n    # TO DO: display mean travel time\n    print('Mean travel time is {}\\n'.format(np.mean(df['Trip Duration'])))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef user_stats(df,city):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    # TO DO: Display counts of user types\n    print(df['User Type'].value_counts())\n\n   \n    # TO DO: Display counts of gender\n    if city != 'washington':\n        \n        print(df['Gender'].value_counts())\n        \n    # TO DO: Display earliest, most recent, and most common year of birth\n    \n        print('\\nThe earliest year of birth is {}\\n'.format(df['Birth Year'].min()))\n        print('\\nThe most recent year of birth is {}\\n'.format(df['Birth Year'].max()))              \n        print('\\nThe most common year of birth is {}\\n'.format(df['Birth Year'].mode()[0]))\n   \n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n    \n    \ndef display_data(df):\n    view_data = input('\\nWould you like to view 5 rows of individual trip data? Enter yes or no\\n')\n    start_loc = 0\n    while view_data.lower() =='yes':\n        print(df.iloc[start_loc:start_loc+5])\n        start_loc += 5\n        view_data = input(\"Do you wish to continue?: \").lower()   \n\ndef main():\n    while True:\n        city, month, day = get_filters()\n        df = load_data(city, month, day)\n\n        time_stats(df)\n        station_stats(df)\n        trip_duration_stats(df)\n        user_stats(df,city)\n        display_data(df)\n\n        restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n        if restart.lower() != 'yes':\n            break\n\n            \nif __name__ == \"__main__\":\n\tmain()          \n\n\n# In[ ]:\n\n\n\n\n","repo_name":"HishamM1/Explore-US-Bikeshare-Data","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"3822927817","text":"from Ninja import Ninja\nimport Pet\n\ncat = Pet.Pets(\"catto\", \"cat\", \"generic food\", \"100%\", \"full\")\nblue_ninja = Ninja(\"blue\",\"ninja\",\"generic treats\", \"generic food\", cat)\n\nblue_ninja.feed()\nblue_ninja.walk()\nblue_ninja.shower()\nblue_ninja.pet.noise()\n\ngatete = Pet.Cat(\"Michiatto\", \"generic food\", \"100%\", \"full\")\nred_ninja = Ninja(\"red\",\"ninja\",\"generic treats\", \"generic food\", gatete)\n\nred_ninja.feed()\nred_ninja.walk()\nred_ninja.shower()\nred_ninja.pet.noise()","repo_name":"Mbarakaj4/202306_python","sub_path":"Practice/CodingDojo-DojoPets/DojoPets.py","file_name":"DojoPets.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73360685367","text":"from debsaws.common import base_operating_premise\n\nregion, instanceid, basedir, session = base_operating_premise()\n\nasg_client = session.client(\"autoscaling\")\n\ndef get_asg_detail(autoscalingroup):\n    response = asg_client.describe_auto_scaling_groups(AutoScalingGroupNames=[autoscalingroup])\n    return response\n\ndef get_asg_instances(autoscalingroup):\n    # filters = [ {'Name': 'key','Values': [ autoscalingroup ]} ]\n    instanceids =[]\n    response = asg_client.describe_auto_scaling_groups(\n        AutoScalingGroupNames=[ autoscalingroup ])\n    for group in response[ \"AutoScalingGroups\" ]:\n        for instance in group[\"Instances\"]:\n            instanceinfo = {'InstanceId': instance['InstanceId'],\n                            'LifecycleState' :instance[\"LifecycleState\"],\n                            'HealthStatus' :instance[\"HealthStatus\"] }\n            print(instanceinfo)\n            instanceids.append(instanceinfo)\n    return instanceids\n","repo_name":"debswade/debsaws","sub_path":"debsaws/asg.py","file_name":"asg.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18092917828","text":"import pytest\n\nfrom cfme import test_requirements\nfrom cfme.common.candu_views import UtilizationZoomView\nfrom cfme.infrastructure.provider.rhevm import RHEVMProvider\nfrom cfme.infrastructure.provider.virtualcenter import VMwareProvider\nfrom cfme.markers.env_markers.provider import ONE_PER_TYPE\nfrom cfme.tests.candu import compare_data\nfrom cfme.utils.appliance.implementations.ui import navigate_to\nfrom cfme.utils.log import logger\nfrom cfme.utils.wait import wait_for\n\n\npytestmark = [\n    pytest.mark.tier(3),\n    test_requirements.c_and_u,\n    pytest.mark.usefixtures(\"setup_provider\"),\n    pytest.mark.provider(\n        [VMwareProvider, RHEVMProvider],\n        selector=ONE_PER_TYPE,\n        required_fields=[[\"cap_and_util\", \"cluster\"]]\n    ),\n]\n\n\nGRAPHS = [\n    \"cluster_cpu\",\n    \"cluster_cpu_state\",\n    \"cluster_memory\",\n    \"cluster_disk\",\n    \"cluster_network\",\n    \"cluster_host\",\n    \"cluster_vm\",\n]\n\nINTERVAL = [\"Hourly\", \"Daily\"]\n\n\n@pytest.fixture(scope=\"function\")\ndef cluster(provider):\n    collection = provider.appliance.collections.clusters\n    cluster_name = provider.data[\"cap_and_util\"][\"cluster\"]\n    return collection.instantiate(name=cluster_name, provider=provider)\n\n\n@pytest.fixture(scope=\"function\")\ndef host(appliance, provider):\n    collection = appliance.collections.hosts\n    for test_host in provider.data[\"hosts\"]:\n        if not test_host.get(\"test_fleece\", False):\n            continue\n        return collection.instantiate(name=test_host.name, provider=provider)\n\n\n@pytest.mark.uncollectif(lambda provider, interval:\n                         provider.one_of(RHEVMProvider) and interval == \"Daily\",\n                         reason=\"RHEVM not supporting historical data collection, \"\n                                \"Need to wait for one day at least\")\n@pytest.mark.parametrize(\"interval\", INTERVAL)\n@pytest.mark.parametrize(\"graph_type\", GRAPHS)\ndef test_cluster_graph_screen(provider, cluster, host, graph_type, interval, enable_candu):\n    \"\"\"Test Cluster graphs for Hourly and Daily Interval\n\n    prerequisites:\n        * C&U enabled appliance\n\n    Steps:\n        * Navigate to Cluster\n        * Check graph displayed or not\n        * Select interval Hourly/Daily\n        * Zoom graph to get Table\n        * Compare table and graph data\n\n    Polarion:\n        assignee: gtalreja\n        caseimportance: medium\n        casecomponent: CandU\n        initialEstimate: 1/4h\n    \"\"\"\n    host.capture_historical_data()\n    cluster.wait_candu_data_available(timeout=1200)\n\n    view = navigate_to(cluster, \"Utilization\")\n    view.options.interval.fill(interval)\n\n    # Check garph displayed or not\n    try:\n        graph = getattr(view, graph_type)\n    except AttributeError as e:\n        logger.error(e)\n    assert graph.is_displayed\n\n    def refresh():\n        provider.browser.refresh()\n        view.options.interval.fill(interval)\n\n    # wait, some time graph take time to load\n    wait_for(lambda: len(graph.all_legends) > 0, delay=5, timeout=200, fail_func=refresh)\n\n    # zoom in button not available with normal graph except Host and VM.\n    # We have to use vm or host average graph for zoom in operation.\n    graph_zoom = [\"cluster_host\", \"cluster_vm\"]\n    avg_graph = graph_type if graph_type in graph_zoom else f\"{graph_type}_vm_host_avg\"\n    try:\n        avg_graph = getattr(view, avg_graph)\n    except AttributeError as e:\n        logger.error(e)\n    avg_graph.zoom_in()\n    view = view.browser.create_view(UtilizationZoomView)\n\n    # wait, some time graph take time to load\n    wait_for(lambda: len(view.chart.all_legends) > 0, delay=5, timeout=300, fail_func=refresh)\n    assert view.chart.is_displayed\n    view.flush_widget_cache()\n    legends = view.chart.all_legends\n    graph_data = view.chart.all_data\n    # Clear cache of table widget before read else it will mismatch headers.\n    view.table.clear_cache()\n    table_data = view.table.read()\n    compare_data(table_data=table_data, graph_data=graph_data, legends=legends)\n","repo_name":"ManageIQ/integration_tests","sub_path":"cfme/tests/candu/test_cluster_graph.py","file_name":"test_cluster_graph.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"77"}
+{"seq_id":"35187346018","text":"\"\"\"\nName: Katherine St. John\nEmail: katherine.stjohn@hunter.cuny.edu\nResources:  Used CSci 127 textbook for files and pandas documentation for keyword args\n\"\"\"\n\nimport pandas as pd\n\n\n\ndef make_df(file_name):\n    \"\"\"\n    Opens file_name and reads into a DataFrame, keeping only the columns:\n    Summons Number,Plate ID,Registration State,Plate Type,Issue Date,Violation Code,Violation Time,\n    Violation In Front Of Or Opposite,House Number,Street Name,Vehicle Color\n    Return the resulting DataFrame.\n    \"\"\"\n\n    df = pd.read_csv(file_name,low_memory=False)\n    df = df[ ['Summons Number','Plate ID','Registration State','Plate Type',\\\n            'Issue Date','Violation Code','Violation Time',\\\n            'Violation In Front Of Or Opposite','House Number',\\\n            'Street Name','Vehicle Color'] ]\n    return df\n\n\ndef filter_reg(df, keep = [\"COM\", \"PAS\"]):\n    \"\"\"\n    This function takes two inputs:\n    df: a DataFrame that including the Plate Type column.\n    keep: a list of values for the Plate Type column. The default value is [\"COM\", \"PAS\"].\n    The function returns the DataFrame with only rows that have Registration with a value from the list keep. All rows where the Registration column contains a different value are dropped.\n    \"\"\"\n\n    return(df[ df['Plate Type'].isin(keep) ] )\n\ndef add_indicator(reg_state):\n    \"\"\"\n    This function takes one input:\n    reg_state: a string containing the state of registation.\n    The function should return 1 when reg_state is in [\"NY\",\"NJ\",\"CT\"] and 0 otherwise.\n    \"\"\"\n\n    if reg_state in [\"NY\",\"NJ\",\"CT\"]:\n        return 1\n    else:\n        return 0\n\ndef find_tickets(df, plate_id):\n    \"\"\"\n    This function takes two inputs:\n    df: a DataFrame that including the Plate ID column.\n    plate_id: a string containing a license plate (combination of letters, numbers and spaces).\n    returns, as a list, the Violation Code for all tickets issued to that plate_id. If that plate_id has no tickets in the DataFrame, then an empty list is returned.\n    \"\"\"\n\n    df_tick = df[ df['Plate ID'] == plate_id ]\n    return( list( df_tick['Violation Code'] ) )\n\ndef make_dict(file_name, skip_rows = 1):\n    \"\"\"\n    This function takes two inputs:\n    file_name: a string containing the name of a file.\n    skip_rows: the number of rows to skip at the beginning of file. The default value is 1.\n    Make a dictionary from a text file named file_name, where each line, after those that are skipped, makes a dictionary entry. The key for each entry is the string upto the first comma (',') and the value is the string between the first and second commas. All characters after the second comma on a line are ignored.\n    \"\"\"\n\n    dict = {}\n\n    with open(file_name) as fd:\n        #Read in skip_row lines before processing the rest of the file:\n        for i in range(skip_rows):\n            fd.readline()\n        #Split up the remaining lines and add to dictionary:\n        for line in fd:\n            words = line.split(\",\")\n            dict[words[0]] = words[1]\n    return dict\n","repo_name":"cherorafi/Data-Science-Spr22-Solutions","sub_path":"39542 Solution Master/Program Solutions/Program 2.py","file_name":"Program 2.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19810281124","text":"# -- coding: utf-8 --\n\"\"\"\n题目：找到数组中任一重复的数字\n在一个长度为n的数组中，所有的数字都是0~n-1的范围内。\n数组中某些数字是重复的，但不知道有几个数字是重复的，也不知道每个数字都重复了几次。\n请找出任一重复的数字。\n\neg. [2, 3, 1, 0, 2, 5, 3]\nreturn 2 or 3.\n\n思路：\n数组长度为n，每个数都在0~n-1范围内，如果每个数都不重复，那么数组排序之后，每个数字与其下标相等，[0, 1, 2, 3]\n我们逆向思维，对数组进行从前向后排序，对于索引i，比较当前值l[i]是否与其相等，如果相等，则继续对比下一个索引；\n如果不相等，则比较l[l[i]]跟l[i]是否相等，如果不相等，则交换l[l[i]]跟l[i]的位置（这一步操作使得l[l[i]] = l[i]）\n如果相等，恭喜你，已经找到了答案。\n\n\"\"\"\nfrom typing import List\n\n\ndef find_repeat_num(fuc_input):\n    if not fuc_input:\n        return -1\n    i = 0\n    while i < len(fuc_input):\n        print(fuc_input)\n        if i == fuc_input[i]:\n            i += 1\n        else:\n            if fuc_input[i] == fuc_input[fuc_input[i]]:\n                return fuc_input[i]\n            else:\n                # exchange fuc_input[i] fuc_input[fuc_input[i]]\n                tmp = fuc_input[i]\n                fuc_input[i] = fuc_input[fuc_input[i]]\n                fuc_input[tmp] = tmp\n                i += 1\n    return -1\n\n\n\"\"\"\n折半查找\n\"\"\"\n\n\ndef bisearch(input_list, value):\n    if not input_list or not value:\n        return -1\n    # 保证列表是有序的\n    input_list.sort()\n    start = 0\n    end = len(input_list) - 1\n    while start <= end:\n        mid = (start + end) // 2\n        # 查中间\n        if input_list[mid] == value:\n            return mid\n        if input_list[mid] > value:\n            # 查左边\n            end = mid - 1\n        else:\n            # 查右边\n            start = mid + 1\n    return -1\n\n\n\"\"\"\n不修改列表找出重复的数字\n\n在一个长度为n的数组里，所有的数都是1~n-1之间，至少有一个数字重复，找出任意一个重复的数组，但不能修改原来的数组。\n例如： [2, 3, 5, 4, 3, 2, 6, 7] \n返回： 2 或者 3\n\n思路：\n如果1~n 之间没有重复的数，那么1~n之间的数一共有n个，如果超过n个，那么一定存在重复的数\nstart = 1 (最小数)\nend = len (最大数）\nmid = (start + end) // 2 （中间数）\n\n类似于折半查找，我们每次先查中间数mid的计数，如果count(mid) > 1，则mid重复；\n否则数start~mid-1之间的计数，如果count(start~ mid-1) > mid-start，则start~mid-1之间存在重复；\n否则mid+1~end之间存在重复\n\"\"\"\n\n\ndef find_repeat_num2(input_list):\n    if not input_list:\n        return -1\n    start = 1\n    end = len(input_list)\n    while start <= end:\n        mid = (start + end) // 2\n        count_ = count(input_list, mid, mid)\n        if count_ > 1:\n            return mid\n        count_ = count(input_list, start, mid - 1)\n        if count_ > mid - start:\n            end = mid - 1\n        else:\n            start = mid + 1\n    return -1\n\n\ndef count(input_list, start, end):\n    count_ = 0\n    if end < start or not input_list:\n        return count_\n    for i in input_list:\n        if start <= i <= end:\n            count_ += 1\n    return count_\n\n\n\"\"\"\n二维有序数组里查找\n\n在一个二维数组里，每一行从左往右递增，每一列从从上到下递增。\n输入一个二维数组，判断是否存在某个数。\n例如：\n[[1, 2, 8, 9],\n [2, 4, 9, 12],\n [4, 7, 10, 13],\n [6, 8, 11, 15]]\n输入7，存在返回true；输入5，不存在返回false\n\"\"\"\n\n\ndef find_matrix_value(matrix, value):\n    if not matrix or not value:\n        return False\n    rows = len(matrix)\n    columns = len(matrix[0])\n\n    row = 0\n    col = columns - 1\n    while row <= rows - 1 and col >= 0:\n        if matrix[row][col] == value:\n            return True\n        if matrix[row][col] > value:\n            col = col - 1\n        else:\n            row = row + 1\n    return False\n\n\ndef find_matrix_value2(matrix, value):\n    if not matrix or not value:\n        return False\n    rows = len(matrix)\n    columns = len(matrix[0])\n\n    row = rows - 1\n    col = 0\n    while row >= 0 and col <= columns - 1:\n        if matrix[row][col] == value:\n            return True\n        if matrix[row][col] > value:\n            row = row - 1\n        else:\n            col = col + 1\n    return False\n\n\ndef replace_blank(input_str):\n    \"\"\"\n    把输入字符串中的空格都替换成\"%20\"，要求时间复杂度为O（n)\n    思路：从后往前替换，先计算出替换后的字符串长度，然后逐个将字符移动到替换后的位置\n    :param input_str: We are happy.\n    :return: We%20are%20happy.\n    \"\"\"\n    if not input_str:\n        return input_str\n    blank_count = 0\n    for s in input_str:\n        if s == \" \":\n            blank_count += 1\n    if blank_count == 0:\n        return input_str\n    # python中字符串是不可变类型，不能在原先字符串上直接修改字符\n    # 这里把字符串改为字符列表\n    input_str = list(input_str)\n\n    p1 = len(input_str) - 1\n    p2 = p1 + blank_count * 2\n    input_str = input_str + [\" \"] * blank_count * 2\n    while p2 > p1 >= 0:\n        if input_str[p1] == \" \":\n            input_str[p2] = \"0\"\n            p2 -= 1\n            input_str[p2] = \"2\"\n            p2 -= 1\n            input_str[p2] = \"%\"\n            p2 -= 1\n        else:\n            input_str[p2] = input_str[p1]\n            p2 -= 1\n        p1 -= 1\n    return \"\".join(input_str)\n\n\ndef merge_sorted_list(a1, a2):\n    \"\"\"\n    合并两个有序的数组a1，a2，\n    思路：从后往前合并\n    :param a1:\n    :param a2:\n    :return:\n    \"\"\"\n    if not a1:\n        return a2\n    if not a2:\n        return a1\n\n    result = [0] * (len(a1) + len(a2))\n    p1 = len(a1) - 1\n    p2 = len(a2) - 1\n    p = len(result) - 1\n    # 从后往前，比较a1、a2的后半部分\n    while p1 >= 0 and p2 >= 0:\n        if a1[p1] >= a2[p2]:\n            result[p] = a1[p1]\n            p1 -= 1\n        else:\n            result[p] = a2[p2]\n            p2 -= 1\n        p -= 1\n    # 取剩余的部分\n    while p1 >= 0:\n        result[p] = a1[p1]\n        p -= 1\n        p1 -= 1\n    while p2 >= 0:\n        result[p] = a2[p2]\n        p -= 1\n        p2 -= 1\n\n    return result\n\n\nclass BinaryTreeNode:\n    def __init__(self, root, lchild, rchild):\n        self.root = root\n        self.lchild = lchild\n        self.rchild = rchild\n\n\ndef preorder(tree: BinaryTreeNode,\n             preorderlist=[]):\n    if not tree or not tree.root:\n        return\n    preorderlist.append(tree.root)\n    if tree.lchild:\n        preorder(tree.lchild, preorderlist)\n    if tree.rchild:\n        preorder(tree.rchild, preorderlist)\n\n\ndef midorder(tree: BinaryTreeNode,\n             midorderlist=[]):\n    if not tree or not tree.root:\n        return\n    if tree.lchild:\n        midorder(tree.lchild, midorderlist)\n    midorderlist.append(tree.root)\n    if tree.rchild:\n        midorder(tree.rchild, midorderlist)\n\n\ndef bfsorder(tree: BinaryTreeNode,\n             bfsorderlist=[]):\n    \"\"\"\n    树的宽度优先遍历\n    :param tree:\n    :param bfsorderlist:\n    :return:\n    \"\"\"\n    queues = []\n    bfsorderlist.append(tree.root)\n    if tree.lchild:\n        queues.append(tree.lchild)\n    if tree.rchild:\n        queues.append(tree.rchild)\n\n    while len(queues) > 0:\n        for node in queues:\n            bfsorderlist.append(node.root)\n            if node.lchild:\n                queues.append(node.lchild)\n            if node.rchild:\n                queues.append(node.rchild)\n\n\ndef build_binary_tree(preorderlist,\n                      midorderlist):\n    if not preorderlist or not midorderlist:\n        return None\n\n    root_index = midorderlist.index(preorderlist[0])\n    left_len = root_index\n    lchild = None\n    if left_len > 0:\n        # 左子树\n        lchild = build_binary_tree(preorderlist[1: root_index + 1],\n                                   midorderlist[0: root_index])\n\n    rchild = None\n    right_len = len(preorderlist) - root_index - 1\n    if right_len > 0:\n        # 右子树\n        rchild = build_binary_tree(preorderlist[root_index + 1:],\n                                   midorderlist[root_index + 1:])\n\n    return BinaryTreeNode(preorderlist[0],\n                          lchild,\n                          rchild)\n\n\nclass BinaryTreeNode2(BinaryTreeNode):\n    def __init__(self, root, lchild, rchild, parent):\n        super(BinaryTreeNode2, self).__init__(root, lchild, rchild)\n        self.parent = parent\n\n\ndef find_next_point(node: BinaryTreeNode2):\n    if node is None:\n        return None\n    # 如果节点有右子树，则下一个节点是右子树的最左节点\n    if node.rchild is not None:\n        next_node = node.rchild\n        while next_node.lchild is not None:\n            next_node = next_node.lchild\n        return next_node\n    # 如果节点没有右子树，则从该节点向上遍历，如果某个节点是其父节点的左子节点，则其父节点是下一个节点\n    current_node = node\n    parent_node = node.parent\n    next_node = None\n    while parent_node is not None and current_node == parent_node.lchild:\n        current_node = parent_node\n        parent_node = current_node.patent\n    return next_node\n\n\nclass QueueWithTwoStacks:\n    \"\"\"\n    两个栈实现队列功能，先进先出\n    \"\"\"\n\n    def __init__(self):\n        self.stack1 = []\n        self.stack2 = []\n\n    def is_empty(self):\n        if self.stack1 or self.stack2:\n            return False\n        return True\n\n    def __len__(self):\n        return len(self.stack1) + len(self.stack2)\n\n    def append_tail(self, value):\n        self.stack1.append(value)\n\n    def delete_head(self):\n        if len(self):\n            return None\n\n        if not self.stack2:\n            while self.stack1:\n                value = self.stack1.pop()\n                self.stack2.append(value)\n        return self.stack2.pop()\n\n\nclass Queue:\n    def __init__(self):\n        self.__queue = []\n\n    def __len__(self):\n        return len(self.__queue)\n\n    def is_empty(self):\n        return len(self) == 0\n\n    def enqueue(self, value):\n        self.__queue.append(value)\n\n    def dequeue(self):\n        if self.__queue:\n            return self.__queue.pop(0)\n        return None\n\n\nclass StackWithTwoQueue:\n    def __init__(self):\n        self.queue1 = Queue()\n        self.queue2 = Queue()\n\n    def __len__(self):\n        return len(self.queue1) + len(self.queue2)\n\n    def is_empty(self):\n        return len(self) == 0\n\n    def push(self, value):\n        self.queue1.enqueue(value)\n\n    def pop(self):\n        if self.is_empty():\n            return None\n\n        if len(self.queue1) > 0:\n            while len(self.queue1) > 1:\n                value = self.queue1.dequeue()\n                self.queue2.enqueue(value)\n            return self.queue1.dequeue()\n        else:\n            while len(self.queue2) > 1:\n                value = self.queue2.dequeue()\n                self.queue1.enqueue(value)\n            return self.queue2.dequeue()\n\n    def push(self, value):\n        self.queue1.enqueue(value)\n\n\ndef fibonacci(n):\n    \"\"\"\n    斐波那契数列\n    :param n:\n    :return:\n    \"\"\"\n    if n == 0:\n        return 0\n    if n == 1:\n        return 1\n    return fibonacci(n - 1) + fibonacci(n - 2)\n\n\ndef fibonacci2(n):\n    if n == 0:\n        return 0\n    if n == 1:\n        return 1\n    last_one = 1\n    last_two = 0\n    for i in range(2, n + 1):\n        tmp = last_two + last_one\n        last_two = last_one\n        last_one = tmp\n    return last_one\n\n\ndef swap(a, b):\n    tmp = a\n    a = b\n    b = tmp\n\n\ndef quick_sort(arr, start, end):\n    if start > end:\n        return\n    pivot = arr[start]\n    i = start\n    j = end\n    while i != j:\n        while arr[j] >= pivot and j > i:\n            j = j - 1\n        while arr[i] <= pivot and j > i:\n            i = i + 1\n        # 交换 start、end 元素\n        arr[i], arr[j] = arr[j], arr[i]\n\n    # 交换 start、pivot元素\n    arr[start], arr[i] = arr[i], arr[start]\n    # 递归\n    quick_sort(arr, start, i - 1)\n    quick_sort(arr, i + 1, end)\n\n\ndef quick_sort2(arr, begin, end):\n    if end < begin:\n        return\n    pivot = arr[begin]\n    i = begin\n    j = end\n    while i != j:\n        while arr[j] >= pivot and j > i:\n            j -= 1\n\n        while arr[i] <= pivot and j > i:\n            i += 1\n        # 交换i、j\n        arr[i], arr[j] = arr[j], arr[i]\n    # 交换 begin、 i\n    arr[i], arr[begin] = arr[begin], arr[i]\n    # 递归\n    quick_sort2(arr, begin, i - 1)\n    quick_sort2(arr, i + 1, end)\n\n\ndef min_revolved(arr):\n    \"\"\"\n    查找旋转数组的最小值\n    比如： [3, 4, 5, 6, 7, 8, 0, 1, 2] -> 0\n    二分查找\n    :param arr:\n    :return:\n    \"\"\"\n    if not arr:\n        return None\n    i = 0\n    j = len(arr) - 1\n    if arr[j] > arr[i]:\n        return arr[i]\n    while arr[i] >= arr[j]:\n        if i + 1 == j:\n            return arr[j]\n        mid = (i + j) // 2\n        if arr[mid] == arr[i] == arr[j]:\n            # 老老实实顺序查询\n            min_value = arr[0]\n            for m in range(i, j + 1):\n                if arr[m] < min_value:\n                    min_value = arr[m]\n            return min_value\n        elif arr[mid] >= arr[i]:\n            i = mid\n        elif arr[mid] <= arr[j]:\n            j = mid\n\n\ndef has_path(matrix, path):\n    \"\"\"\n    矩阵中的路径\n    例如, [[a, b, c, d],\n          [e, f, g, h],\n          [h, i ,j ,k]]\n    输入bfgj,返回true\n    输入bfgi，返回false\n    :param matrix:\n    :return:\n    \"\"\"\n    if not matrix:\n        return False\n    rows = len(matrix)\n    cols = len(matrix[0])\n    visited = [False] * rows * cols\n    path_index = 0\n    for r in range(rows):\n        for c in range(cols):\n            if has_path_core(matrix, r, c, rows, cols, path, path_index, visited):\n                return True\n    return False\n\n\ndef has_path_core(matrix, r, c, rows, cols, path, path_index, visited):\n    if path_index == len(path):\n        return True\n    flag = False\n    try:\n        if 0 <= r < rows and 0 <= c < cols and path[path_index] == matrix[r][c] and not visited[r * cols + c]:\n            path_index += 1\n            visited[r * cols + c] = True\n            # 递归判断 (r, c-1)、(r, c+1)、(r-1,c)、(r+1, c)\n            flag = has_path_core(matrix, r, c - 1, rows, cols, path, path_index, visited) \\\n                   or has_path_core(matrix, r, c + 1, rows, cols, path, path_index, visited) \\\n                   or has_path_core(matrix, r - 1, c, rows, cols, path, path_index, visited) \\\n                   or has_path_core(matrix, r + 1, c, rows, cols, path, path_index, visited)\n            if not flag:\n                path_index -= 1\n                visited[r * cols + c] = False\n    except:\n        print(r, c, path_index, r * cols + c)\n    return flag\n\n\ndef robot_moving_count(threshold: int,\n                       rows: int,\n                       cols: int):\n    \"\"\"\n    机器人的运动范围\n    :param threshold:\n    :param rows:\n    :param cols:\n    :return:\n    \"\"\"\n    visited = [False] * rows * cols\n    count = robot_moving_count_core(0, 0, rows, cols, threshold, visited)\n    return count\n\n\ndef robot_moving_count_core(r,\n                            c,\n                            rows,\n                            cols,\n                            threshold,\n                            visited,\n                            ):\n    count = 0\n    if check_row_col(r, c, rows, cols, threshold, visited):\n        print(f\"r={r}, c={c}, count={count}\")\n        visited[r * cols + c] = True\n        count += 1\n        count += robot_moving_count_core(r, c+1, rows, cols, threshold, visited)  # 上\n        count += robot_moving_count_core(r, c-1, rows, cols, threshold, visited)  # 下\n        count += robot_moving_count_core(r-1, c, rows, cols, threshold, visited)  # 左\n        count += robot_moving_count_core(r+1, c, rows, cols, threshold, visited)  # 右\n    return count\n\n\ndef check_row_col(r,\n                  c,\n                  rows,\n                  cols,\n                  threshold,\n                  visited):\n    if 0 <= r < rows \\\n            and 0 <= c < cols \\\n            and sum_num(r) + sum_num(c) <= threshold \\\n            and not visited[r * cols + c]:\n        return True\n    return False\n\n\ndef sum_num(n):\n    result = 0\n    while n >= 1:\n        result += n % 10\n        n = n // 10\n    return result\n\n\ndef max_product_after_cut(length: int):\n    \"\"\"\n    剪绳子\n    动态规划法\n    长度为n的绳子，切断后的最大乘积设为f(n)\n    那么则有 f(n) = max(i*(n-i), i*f(n-i))\n    :param length:\n    :return:\n    \"\"\"\n    if length < 2:\n        return 0\n    if length == 2:\n        return 1\n    if length == 3:\n        return 2\n\n    products = [0] * (length + 1)\n    products[0] = 0\n    products[1] = 0\n    products[2] = 1\n    products[3] = 2\n\n    for i in range(4, length + 1):\n        max_product = 0\n        for j in range(1, i // 2 + 1):\n            max_product = max(max_product, j * products[i - j], j * (i - j))\n            products[i] = max_product\n    return products[length]\n\n\ndef number_of1(n: int):\n    \"\"\"\n    输入一个整数，输出其二进制数的1的个数\n    比如，8 ，二进制为1000，有一个1\n    :param n:\n    :return:\n    \"\"\"\n    if n < 0:\n        n = n & 0xFFFFFFFF\n    num_1 = 0\n    while n > 0:\n        n &= n-1\n        num_1 += 1\n    return num_1\n\n\nif __name__ == '__main__':\n    # print(find_repeat_num([2, 3, 1, 0, 2, 5, 3]))\n    # print(find_repeat_num([0, 1, 2, 3, 4, 5]))\n    # print(find_repeat_num([0, 0, 0, 0, 0, 0]))\n\n    # print(bisearch([0, 1, 2, 3, 4, 5], 8))\n\n    # print(find_repeat_num2([2, 3, 5, 4, 3, 2, 6, 7]))\n    # print(find_repeat_num2([0, 1, 2, 3, 4, 5]))\n    # print(find_repeat_num2([1, 1, 1, 1, 1, 1]))\n\n    # print(find_matrix_value2([[1, 2, 8, 9],\n    #                          [2, 4, 9, 12],\n    #                          [4, 7, 10, 13],\n    #                          [6, 8, 11, 15]], 7))\n    # print(find_matrix_value2([[1, 2, 8, 9],\n    #                          [2, 4, 9, 12],\n    #                          [4, 7, 10, 13],\n    #                          [6, 8, 11, 15]], 5))\n\n    # print(replace_blank(\"We are   happy.\"))\n    # print(merge_sorted_list([1, 2, 3, 4],\n    #                         [3, 4, 5, 6]))\n\n    # tree = build_binary_tree([1, 2, 4, 7, 3, 5, 6, 8],\n    #                          [4, 7, 2, 1, 5, 3, 8, 6])\n    # preorderlist = []\n    # preorder(tree, preorderlist)\n    # print(preorderlist)\n    #\n    # midorderlist = []\n    # midorder(tree, midorderlist)\n    # print(midorderlist)\n\n    # queue = QueueWithTwoStacks()\n    # queue.append_tail(0)\n    # queue.append_tail(1)\n    # queue.append_tail(2)\n    # while len(queue) > 0:\n    #     print(queue.delete_head())\n\n    # stack = StackWithTwoQueue()\n    # for i in [1, 2, 3, 4, 5]:\n    #     stack.push(i)\n    # while len(stack) > 0:\n    #     print(stack.pop())\n\n    # import time\n    # start_time = time.time()\n    # fn = fibonacci(3)\n    # print(f\"cost time: {time.time() - start_time}, value: {fn}\")\n    # start_time = time.time()\n    # fn2 = fibonacci2(3)\n    # print(f\"cost time: {time.time() - start_time}, value: {fn2}\")\n\n    # arr = [6, 1, 2, 7, 9, 3, 4, 5, 10, 8]\n    # quick_sort2(arr, 0, len(arr) -1)\n    # print(arr)\n    # print(min_revolved([3, 4, 5, 6, 7, 8, 0, 1, 2]))\n    # print(min_revolved([3, 4, 5, 6, 7, 8]))\n    # print(min_revolved([0, 1, 1, 1, 0, 1]))\n\n    # print(has_path([['a', 'b', 'c', 'd'],\n    #                 ['e', 'f', 'g', 'h'],\n    #                 ['i', 'j', 'k', 'l']],\n    #                'bfgk'))\n    # print(has_path([['a', 'b', 'c', 'd'],\n    #                 ['e', 'f', 'g', 'h'],\n    #                 ['i', 'j', 'k', 'l']],\n    #                'bfgl'))\n    # print(robot_moving_count(2, 3, 3))\n\n    # print(max_product_after_cut(8))\n\n    print(number_of1(8))","repo_name":"zy614582280/CodePratice","sub_path":"cp2/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":19989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22521023597","text":"#!/usr/bin/env python\nfrom collections import defaultdict\n\nclass Solution:\n    def buddyStrings(self, s: str, goal: str) -> bool:\n\n        if len(s) != len(goal): return False\n\n        i, n = 0, len(s)    # 扫描两字符串\n        pos = []    # 对应字符不同时将下标入pos\n        while i < n:\n            if s[i] != goal[i]:\n                if len(pos) == 2: return False    # 若有超过两处不同，直接返回False\n                pos.append(i)\n            i += 1\n\n        if len(pos) == 0:    # 两者完全相等时，看是否有字母重复出现。只要有就True\n            counter = defaultdict(int)\n            for ch in s:\n                counter[ch] += 1\n                if counter[ch] == 2: return True\n            return False\n        elif len(pos) == 1:    # 有且仅有一处不同，直接False\n            return False\n        else:\n            return s[pos[0]] == goal[pos[1]] and s[pos[1]] == goal[pos[0]]    # 最普遍情况","repo_name":"ftakanashi/JobProjects","sub_path":"LeetCode/859.亲密字符串/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"73238108730","text":"# -*- coding: utf-8 -*-\r\n\r\nimport datetime\r\nimport re\r\nimport time\r\n\r\nZERO_TIME_DELTA = datetime.timedelta(0)\r\nLOCAL_TIME_DELTA = datetime.timedelta(hours=8)  # 本地时区偏差\r\n\r\n\r\nclass UTC(datetime.tzinfo):\r\n    def utcoffset(self, dt):\r\n        return ZERO_TIME_DELTA\r\n\r\n    def dst(self, dt):\r\n        return ZERO_TIME_DELTA\r\n\r\n\r\nclass LocalTimeZone(datetime.tzinfo):\r\n    def utcoffset(self, dt):\r\n        return LOCAL_TIME_DELTA\r\n\r\n    def dst(self, dt):\r\n        return ZERO_TIME_DELTA\r\n\r\n    def tzname(self, dt):\r\n        return '+08:00'\r\n\r\n\r\n# singleton\r\nUTC = UTC()\r\nLocalTimeZone = LocalTimeZone()\r\n\r\ndate_re = re.compile(\r\n    r'(?P<year>\\d{4})-(?P<month>\\d{1,2})-(?P<day>\\d{1,2})$'\r\n)\r\n\r\ndatetime_re = re.compile(\r\n    r'(?P<year>\\d{4})-(?P<month>\\d{1,2})-(?P<day>\\d{1,2})'\r\n    r'[T ](?P<hour>\\d{1,2}):(?P<minute>\\d{1,2})'\r\n    r'(?::(?P<second>\\d{1,2})(?:\\.(?P<microsecond>\\d{1,6})\\d{0,6})?)?'\r\n    r'(?P<tzinfo>Z|[+-]\\d{2}(?::?\\d{2})?)?$'\r\n)\r\n\r\n\r\ndef parse_date(value):\r\n    \"\"\"Parses a string(ISO_8601) and return a datetime.date.\r\n\r\n    Raises ValueError if the input is well formatted but not a valid date.\r\n    Returns None if the input isn't well formatted.\r\n    \"\"\"\r\n    match = date_re.match(value)\r\n    if match:\r\n        kw = {k: int(v) for k, v in match.groupdict().items()}\r\n        return datetime.date(**kw)\r\n\r\n\r\ndef parse_datetime(value):\r\n    \"\"\"Parses a string(ISO_8601) and return a datetime.datetime base UTC,\r\n    or parse datetime.datetime base other timezone and return a datetime.datetime base UTC timezone\r\n    \"\"\"\r\n    if isinstance(value, datetime.datetime):\r\n        if not value.tzinfo:\r\n            value = value.replace(tzinfo=LocalTimeZone)\r\n        return value.astimezone(UTC)\r\n\r\n    match = datetime_re.match(value)\r\n    if match:\r\n        kw = match.groupdict()\r\n        if kw['microsecond']:\r\n            kw['microsecond'] = kw['microsecond'].ljust(6, '0')\r\n        tzinfo = kw.pop('tzinfo')\r\n        tz = UTC\r\n        offset = 0\r\n        if tzinfo == 'Z':\r\n            offset = 0\r\n        elif tzinfo is not None:\r\n            offset_mins = int(tzinfo[-2:]) if len(tzinfo) > 3 else 0\r\n            offset = 60 * int(tzinfo[1:3]) + offset_mins\r\n            if tzinfo[0] == '-':\r\n                offset = -offset\r\n        else:\r\n            tz = LocalTimeZone\r\n        kw = {k: int(v) for k, v in kw.items() if v is not None}\r\n        kw['tzinfo'] = tz\r\n        dt = datetime.datetime(**kw)\r\n        dt += datetime.timedelta(minutes=offset)\r\n        return dt.astimezone(UTC)\r\n\r\n\r\ndef convert_zone(dt: datetime.datetime, tz_to=UTC, tz_default=LocalTimeZone):\r\n    \"\"\"\r\n    @param dt:\r\n    @param tz_to: 转换后的目标时区\r\n    @param tz_default: dt无时区信息时的默认时区\r\n    \"\"\"\r\n    if not dt.tzinfo:\r\n        dt = dt.replace(tzinfo=tz_default)\r\n    return dt.astimezone(tz_to)\r\n\r\n\r\ndef get_utc_time(dt: datetime.datetime = None, tz_default=LocalTimeZone):\r\n    \"\"\"\r\n    @param dt 为None时，返回当前时间\r\n    @param tz_default dt无时区信息时的默认时区\r\n    \"\"\"\r\n    if dt is None:\r\n        dt = datetime.datetime.now()\r\n    return convert_zone(dt, tz_default=tz_default)\r\n\r\n\r\ndef get_time_str(dt: datetime.datetime = None, tz_default=LocalTimeZone):\r\n    \"\"\"\r\n    @param dt 为None时，返回当前时间\r\n    @param tz_default dt无时区信息时的默认时区\r\n    \"\"\"\r\n    if not dt:\r\n        dt = datetime.datetime.now()\r\n    dt = convert_zone(dt, tz_default=tz_default)\r\n    time_str = dt.isoformat().split('+')[0]\r\n    return time_str + 'Z'\r\n\r\n\r\ndef get_date_str(dt: datetime.date = None):\r\n    \"\"\"\r\n    @param dt 为None时，返回当前日期\r\n    \"\"\"\r\n    if not dt:\r\n        dt = datetime.date.today()\r\n    return dt.isoformat()\r\n\r\n\r\ndef get_cur_timestamp():\r\n    \"\"\" 获取当前时间戳\r\n    \"\"\"\r\n    ts = int(time.time())\r\n    return ts\r\n\r\n\r\ndef get_cur_datetime_m():\r\n    \"\"\" 获取当前日期时间字符串，包含 年 + 月 + 日 + 时 + 分 + 秒 + 微妙\r\n    \"\"\"\r\n    today = datetime.datetime.today()\r\n    str_m = today.strftime('%Y%m%d%H%M%S%f')\r\n    return str_m\r\n\r\n\r\ndef get_datetime():\r\n    \"\"\" 获取日期时间字符串，包含 年 + 月 + 日 + 时 + 分 + 秒\r\n    \"\"\"\r\n    today = datetime.datetime.today()\r\n    str_dt = today.strftime('%Y%m%d%H%M%S')\r\n    return str_dt\r\n\r\n\r\ndef get_date(fmt='%Y%m%d', delta_day=0):\r\n    \"\"\" 获取日期字符串，包含 年 + 月 + 日\r\n    @param fmt 返回的日期格式\r\n    \"\"\"\r\n    day = datetime.datetime.today()\r\n    if delta_day:\r\n        day += datetime.timedelta(days=delta_day)\r\n    str_d = day.strftime(fmt)\r\n    return str_d\r\n\r\n\r\ndef date_str_to_dt(date_str=None, fmt='%Y%m%d', delta_day=0):\r\n    \"\"\" 日期字符串转换到datetime对象\r\n    @param date_str 日期字符串\r\n    @param fmt 日期字符串格式\r\n    @param delta_day 相对天数，<0减相对天数，>0加相对天数\r\n    \"\"\"\r\n    if not date_str:\r\n        dt = datetime.datetime.today()\r\n    else:\r\n        dt = datetime.datetime.strptime(date_str, fmt)\r\n    if delta_day:\r\n        dt += datetime.timedelta(days=delta_day)\r\n    return dt\r\n\r\n\r\ndef dt_to_date_str(dt=None, fmt='%Y%m%d', delta_day=0):\r\n    \"\"\" datetime对象转换到日期字符串\r\n    @param dt datetime对象\r\n    @param fmt 返��的日期字符串格式\r\n    @param delta_day 相对天数，<0减相对天数，>0加相对天数\r\n    \"\"\"\r\n    if not dt:\r\n        dt = datetime.datetime.today()\r\n    if delta_day:\r\n        dt += datetime.timedelta(days=delta_day)\r\n    str_d = dt.strftime(fmt)\r\n    return str_d\r\n\r\n\r\ndef ts_to_datetime_str(ts):\r\n    \"\"\" 将时间戳转换为日期时间格式，年-月-日 时:分:秒\r\n    @param ts 时间戳\r\n    \"\"\"\r\n    if not ts:\r\n        return '00-00-00 00:00:00'\r\n    dt = datetime.datetime.fromtimestamp(int(ts))\r\n    return dt.strftime('%Y-%m-%d %H:%M:%S')\r\n\r\n\r\ndef datetime_str_to_ts(dt_str, fmt='%Y-%m-%d %H:%M:%S'):\r\n    \"\"\" 将日期时间格式字符串转换成时间戳\r\n    @param dt_str 日期时间字符串\r\n    @param fmt 日期时间字符串格式\r\n    \"\"\"\r\n    ts = int(time.mktime(datetime.datetime.strptime(dt_str, fmt).timetuple()))\r\n    return ts\r\n\r\n\r\ndef current_timestamp(_int=True):\r\n    res = time.time()\r\n    if _int:\r\n        res = int(res)\r\n    return res\r\n","repo_name":"Karmenzind/fp-server","sub_path":"src/utils/time_ext.py","file_name":"time_ext.py","file_ext":"py","file_size_in_byte":6254,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"77"}
+{"seq_id":"25361983780","text":"import torch\nimport torch.nn as nn #Pytorch nn classes help us train Neural networks in a more concise way. We end up with shorter, understandable and flexible code.\nimport torch.nn.functional as F #Functional permits us to work with built-in activation and loss function\n\nclass MLP(nn.Module):\n    #nn.Module is the base class for all neural network modules\n    def __init__(self,dim_input,dim_hidden,dim_output):\n        '''s\n        Inputs:\n            dim_input: Number of inputs (dimension of the input layer)\n            dim_hidden: Dimension of the hiddel layer(s)\n            dim_output: Number of outputs\n            \n        '''\n        super(MLP,self).__init__()\n        #creating fully connected layers\n        self.fc1=nn.Linear(dim_input,dim_hidden)\n        self.fc2=nn.Linear(dim_hidden,dim_output)\n        self.dropout=nn.Dropout(0.3)\n        self.sigmoid=nn.Sigmoid()\n        \n    def forward(self,x):\n        '''\n        Feedforward behavior of the nn.\n        Input:\n            x: batch of input features\n        Returns:\n            Activated sigmoid function\n        '''\n        \n        out=F.relu(self.fc1(x))\n        out=self.dropout(out)\n        out=self.fc2(out)\n        \n        return self.sigmoid(out)\n        ","repo_name":"josecruzado21/classifying-moon-data-pytorch","sub_path":"source_pytorch/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3340062059","text":"#program to guess a number\r\nimport random\r\ndef nameask():\r\n   name = input(\"what is your name? :\")\r\n   print(\"Hello \" + name + \" in this game you will guess a number between 1 and 5\\n\")\r\n   return name\r\n\r\ndef numchoice():\r\n   a = random.randint(1, 5)\r\n   c = int(input(\"please choose a number between 1 and 5.\\n\"))\r\n   if c == a:\r\n      print(\"correct!\")\r\n   else:\r\n      print(\"incorrect! Answer was: \" + str(a))\r\n   return 0\r\n\r\ndef keepplaying():\r\n   num=1\r\n   while(num == 1):\r\n      numchoice()\r\n      int(input(\"would you like to play again(1 for yes, any key for no)\"))\r\n\r\nnameask()\r\nkeepplaying()\r\n","repo_name":"koza0021/koza0021_assignment4","sub_path":"koza0021-assignement4.py","file_name":"koza0021-assignement4.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40178164884","text":"from __future__ import print_function, division\nimport numpy as np\nimport matplotlib as mpl\nfrom collections import defaultdict\n#mpl.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport sys\n\n# This script attempts to construct a colour palette for the states, locations etc used by WNV nextstrain.\n# It is called by Snakemake, just before the final step (export)\n# ARG1: METADATA ARG2: OUTPUT\n\nwith open(sys.argv[1], 'r') as f:\n  raw = f.read().splitlines() \n  header = raw[0].split(\"\\t\")\n  metadata = []\n  for line in raw[1:]:\n    metadata.append({header[i]:data for i, data in enumerate(line.split(\"\\t\"))})\n\nfh = open(sys.argv[2], \"w\")\n\n#########################################################################################\n# COUNTRIES\ncountries    = [\"United-States\", \"Mexico\", \"Israel\", \"British-Virgin-Islands\", \"Canada\", \"Colombia\", \"Brazil\", \"Argentina\", \"US-Virgin-Islands\",]\ncountry_cols = [\"#969696\", \"#7E00A8\", \"#0054A8\", \"#387A47\", \"#7BDE00\", \"#E5C800\", \"#FF7F00\", \"#A80000\", \"#4C2F00\"]\nfh.write(\"## COUNTRIES ##\\n\")\nfor pair in zip(countries, country_cols):\n  fh.write(\"{}\\t{}\\t{}\\n\".format(\"country\", pair[0], pair[1]))\n\n#########################################################################################\n# LINEAGES / STRAINS / CLADES (they're not monophyletic)\nwnv_strain       = [\"NY99\",    \"SW03\",    \"WN02\", \"pre-NY\"]\nwnv_strain_cols  = [\"#CBB742\", \"#7EB876\", \"#4988C5\", \"#A80000\"]\nfh.write(\"## WNV STRAINS / LINEAGES ##\\n\")\nfor pair in zip(wnv_strain, wnv_strain_cols):\n\tfh.write(\"{}\\t{}\\t{}\\n\".format(\"lineage\", pair[0], pair[1]))\n\n#########################################################################################\n# STATES\n# different colour scales where used to colour states based on five main US geographic regions\nstates = {\n\t\"west\": [\"AK\", \"WA\", \"ID\", \"MT\", \"OR\", \"NV\", \"WY\", \"CA\", \"UT\", \"CO\", \"HI\"],\n\t\"southwest\": [\"AZ\", \"NM\", \"OK\", \"TX\"],\n\t\"midwest\": [\"ND\", \"MN\", \"IL\", \"WI\", \"MI\", \"SD\", \"IA\", \"IN\", \"OH\", \"NE\", \"MO\", \"KS\"],\n\t\"southeast\": [\"KY\", \"WV\", \"VA\", \"AR\", \"TN\", \"NC\", \"SC\", \"LA\", \"MS\", \"AL\", \"GA\", \"FL\"],\n\t\"northeast\": [\"ME\", \"VT\", \"NH\", \"NY\", \"MA\", \"RI\", \"PA\", \"NJ\", \"CT\", \"MD\", \"DC\", \"DE\"],\n\t\"caribbean\": [\"US-VI\", \"VGB\"],\n\t\"canada\": [\"CAN/QC\"],\n\t\"mexico\": [\"MEX/BCN\", \"MEX/SON\", \"MEX/CHH\", \"MEX/TAM\"],\n\t\"southamerica\": [\"COL/ANT\", \"BRA/ES\", \"ARG/B\"],\n\t\"israel\": [\"ISR/D\"]\n}\n# prune out the states that _aren't_ in the metadata (before we create the colour scale)\n# states_present = set([x[\"state\"] for x in metadata])\n# for key, values in states.items():\n#   states[key] = list(filter(lambda x: x in states_present, values))\n  # generate color maps\nstates_cols = {\n\t\"west\": [\"#590000\", \"#690909\", \"#7A1515\", \"#8A2424\", \"#9B3636\", \"#AB4B4B\", \"#BC6363\", \"#CD7E7E\", \"#DD9C9C\", \"#EEBDBD\", \"#FFE1E1\"],\n\t\"southwest\": [\"#AD4700\", \"#C36A11\", \"#D98D22\", \"#EFB034\"],\n\t\"midwest\": [\"#001C00\", \"#022B02\", \"#063906\", \"#0C480C\", \"#145814\", \"#1D671D\", \"#297529\", \"#358535\", \"#449344\", \"#54A354\", \"#67B267\", \"#7BC17B\"],\n\t\"southeast\": [\"#001833\", \"#032142\", \"#092C51\", \"#113860\", \"#1A466F\", \"#25547E\", \"#32638D\", \"#41729C\", \"#5183AB\", \"#6495BA\", \"#78A7C9\", \"#8EBAD9\"],\n\t\"northeast\": [\"#1C001C\", \"#2E0330\", \"#3F0B45\", \"#501559\", \"#60236E\", \"#713483\", \"#824997\", \"#9460AC\", \"#A77BC1\", \"#BD9AD5\", \"#D5BBEA\", \"#F0E1FF\"],\n\t\"caribbean\": [\"#4C2F00\", \"#7F6233\"],\n\t\"canada\": [\"#7FD000\"],\n\t\"mexico\": [\"#4E5900\", \"#7D882E\", \"#ACB75C\", \"#DBE78A\"],\n\t\"southamerica\": [\"#00594E\", \"#00A08C\", \"#00E7CA\"],\n\t\"israel\": [\"#E533E5\"]\n}\n\nfh.write(\"## STATES ##\\n\")\nfor category, names in states.items():\n  for pair in zip(names, states_cols[category]):\n    fh.write(\"{}\\t{}\\t{}\\n\".format(\"state\", pair[0], pair[1]))\n\n#########################################################################################\n# HOSTS\n# use the tab20c scale - https://matplotlib.org/examples/color/colormaps_reference.html\n# tab20     = [mpl.colors.rgb2hex(mpl.cm.tab20c(i)) for i in range(0,20)]\nhost      = [\"Bird-crow\", \"Bird-other\", \"Bird-unknown\", \"Mosquito-Aedes\", \"Mosquito-Culex\", \"Mosquito-Culiseta\", \"Mosquito-other\", \"Mosquito-unknown\", \"Human\", \"Horse\", \"Squirrel\", \"Unknown\"]\nhost_cols = [\"#000000\", \"#41ab5d\", \"#addd8e\",\"#969696\", \"#8c96c6\", \"#8c6bb1\",\"#88419d\", \"#810f7c\", \"#B20023\",\"#D86239\", \"#FEC34F\", \"#00E5E5\"]\nfh.write(\"## HOSTS ##\\n\")\nfor pair in zip(host, host_cols):\n  fh.write(\"{}\\t{}\\t{}\\n\".format(\"host\", pair[0], pair[1]))\n\n#########################################################################################\n# DIVISIONS\ndivisions = set(x[\"division\"] for x in metadata)\ndivisions.discard(\"Unknown\")\ndivisions.discard(\"division\")\ndivisions = list(divisions)\ndivisions.sort(key=str.lower)\ndivisions_cols = [mpl.colors.rgb2hex(mpl.cm.viridis(i)) for i in np.linspace(0,1,len(divisions))]\nfh.write(\"## DIVISIONS ##\\n\")\nfor pair in zip(divisions, divisions_cols):\n  fh.write(\"{}\\t{}\\t{}\\n\".format(\"division\", pair[0], pair[1]))\n\n#########################################################################################\n# END\nfh.close()","repo_name":"colejensen/WNV-4kproject","sub_path":"scripts/make_colors.py","file_name":"make_colors.py","file_ext":"py","file_size_in_byte":4955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25968648762","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2017/9/1\n@author: MG\n\"\"\"\n\n\ndef get_trader_api_and_login():\n    import logging\n    from pyctp_api import MyTraderApi\n    from ctp import ApiStruct\n    import time\n\n    logger = logging.getLogger(__name__)\n    trader_api = MyTraderApi()\n    trader_api.RegisterFront()\n    trader_api.SubscribePrivateTopic(ApiStruct.TERT_QUICK)\n    trader_api.SubscribePublicTopic(ApiStruct.TERT_QUICK)\n    trader_api.Init()\n    # 检查交易端登录情况\n    logger.info(\"等待 登录确认\")\n    for _ in range(5):\n        if trader_api.has_login:\n            # logger.info(\"交易端登录成功\")\n            break\n        else:\n            time.sleep(1)\n    else:\n        logging.warning('交易端登录超时，交易所时间同步可能不准，导致分钟线存在误差')\n        raise ConnectionRefusedError(\"登录错误，无法执行后续测试\")\n\n    return trader_api\n","repo_name":"mmmaaaggg/QABAT","sub_path":"test_case/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"19506919069","text":"\"\"\"\n    This file describes a Tasked Dual Neural net.\n    A tasked neural net has multiple parts:\n    2 models with the same layout (hence the \"dual\"):\n        - A common part, shared between tasks\n        - A task-specific part at the end\n    One of the 2 models is fixed and is synced with the live copy of the network using the sync method.\n    The live copy can be fitted using the model.fit method.\n\"\"\"\nimport keras as ks\n\n\nclass TaskedDualNeuralNet(object):\n    def __init__(self, input_shape, shared_layers, task_specific_layers, compile_function, tasks):\n        \"\"\"\n        Initializes a tasked Dual Neural Net\n        :param input_shape: The shape of the input (not including batch dimension)\n        :param shared_layers: The layers shared by all tasks (Using keras functional API)\n        :param task_specific_layers: The layers specific to a task (Using keras functional API)\n        :param compile_function: A function that takes a model and compiles it\n        :param tasks: An array of tasks\n        \"\"\"\n        self.input_shape = input_shape\n        self.tasks = tasks\n        self.compile_function = compile_function\n        self.task_specific_layers = task_specific_layers\n        self.shared_layers = shared_layers\n        self.live_models = dict()\n        self.fixed_models = dict()\n\n        self.init_models(shared_layers, task_specific_layers)\n\n    def init_models(self, shared_layers, task_specific_layers):\n        inp = ks.Input(self.input_shape)\n        shared_live_out = shared_layers(inp)\n        shared_fixed_out = shared_layers(inp)\n\n        for task in self.tasks:\n            self.live_models[task] = ks.Model(input=inp, output=task_specific_layers(shared_live_out))\n            self.fixed_models[task] = ks.Model(input=inp, output=task_specific_layers(shared_fixed_out))\n            self.compile_function(self.live_models[task])\n\n    def predict(self, x, task, live=True):\n        if live:\n            return self.live_models[task].predict(x)\n        else:\n            return self.fixed_models[task].predict(x)\n\n    def fit(self, x, y, task, epochs=1, verbose=False, batch_size=32):\n        self.live_models[task].fit(x, y, epochs=epochs, verbose=verbose, batch_size=batch_size)\n\n    def sync(self):\n        # Synchronizes the fixed model with the live model\n        for task in self.tasks:\n            self.fixed_models[task].set_weights(self.live_models[task].get_weights())\n","repo_name":"Gerryflap/RL_project_common","sub_path":"sacx/tasked_dual_neural_net.py","file_name":"tasked_dual_neural_net.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"10546633862","text":"import math\n\n\nclass DecisionTreeRegressor:\n\n    def __init__(self, max_depth=None, min_samples_split=2, min_samples_leaf=1):\n        self.max_depth = max_depth\n        self.min_samples_split = min_samples_split\n        self.min_samples_leaf = min_samples_leaf\n\n    def fit(self, X, y):\n        self._features = X\n        self._target = y\n\n        self._tree = self._build_tree()\n\n    def predict(self, X):\n        predictions = []\n        for x in X:\n            predictions.append(self._predict(x))\n\n        return predictions\n\n    def _build_tree(self):\n        root = DecisionTreeNode(self._features, self._target)\n\n        self._build_tree_recursively(root)\n\n        return root\n\n    def _build_tree_recursively(self, node):\n        if len(node.features) <= self.min_samples_leaf:\n            node.prediction = node.target.mean()\n            return\n\n        best_feature, best_split_value = self._find_best_split(node)\n\n        node.left = DecisionTreeNode(node.features[node.features[best_feature] <= best_split_value], node.target[node.features[best_feature] <= best_split_value])\n        node.right = DecisionTreeNode(node.features[node.features[best_feature] > best_split_value], node.target[node.features[best_feature] > best_split_value])\n\n        self._build_tree_recursively(node.left)\n        self._build_tree_recursively(node.right)\n\n    def _find_best_split(self, node):\n        best_gain = -float(\"inf\")\n        best_feature = None\n        best_split_value = None\n\n        for feature in node.features:\n            for split_value in np.unique(node.target):\n                gain = self._information_gain(node.target, feature, split_value)\n\n                if gain > best_gain:\n                    best_gain = gain\n                    best_feature = feature\n                    best_split_value = split_value\n\n        return best_feature, best_split_value\n\n    def _information_gain(self, target, feature, split_value):\n        target_1d = target.ravel()\n\n        entropy_before = self._entropy(target_1d)\n\n        target_left = target_1d[target_1d[feature] <= split_value]\n        target_right = target_1d[target_1d[feature] > split_value]\n\n        entropy_after_left = self._entropy(target_left)\n        entropy_after_right = self._entropy(target_right)\n\n        gain = entropy_before - (len(target_left) / len(target) * entropy_after_left + len(target_right) / len(\n            target) * entropy_after_right)\n\n        return gain\n\n    def _entropy(self, target):\n        counts = np.unique(target, return_counts=True)[1]\n\n        entropy = 0\n        for count in counts:\n            p = count / len(target)\n\n            if p > 0:\n                entropy += -p * math.log2(p)\n\n        return entropy\n\n\nclass DecisionTreeNode:\n\n    def __init__(self, features, target):\n        self.features = features\n        self.target = target\n        self.prediction = None\n        self.left = None\n        self.right = None\n\nimport numpy as np\n\n# Generate some data\nX = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])\ny = np.array([3, 4, 5, 6])\n\n# Create a DecisionTreeRegressor\nregressor = DecisionTreeRegressor()\n\n# Fit the regressor to the data\nregressor.fit(X, y)\n\n# Predict some values\n# Predict some values\npredictions = regressor.predict(np.array([[9, 10], [11, 12]]))\n\n# Print the predictions\nprint(predictions)\n\n","repo_name":"AbdalrhmanBasel/Machine_Learning_A-Z","sub_path":"005 Decision Trees/models/Homework/DecisionTreeClassifier.py","file_name":"DecisionTreeClassifier.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1029096558","text":"from .utils import get, grep, ls, basename\n\n\nclass Memory:\n\n\t@staticmethod\n\tdef get_usage():\n\t\tmeminfo = get(\"/proc/meminfo\")\n\t\ttotal = int(grep(meminfo, \"MemTotal:\"))\n\t\tavailable = int(grep(meminfo, \"MemAvailable:\"))\n\t\tcached = int(grep(meminfo, \"Cached:\"))\n\t\tswap_total = int(grep(meminfo, \"SwapTotal:\"))\n\t\tswap_available = int(grep(meminfo, \"SwapFree:\"))\n\n\t\tprocs = ls(\"/proc\")\n\t\tprocesses = []\n\t\tfor proc in procs:\n\t\t\tpid = basename(proc)\n\t\t\tif pid.isnumeric():\n\t\t\t\tprocesses.append(pid)\n\n\t\treturn {\n\t\t\t\"total\": round(1024 * total / 1000),\n\t\t\t\"available\": round(1024 * available / 1000),\n\t\t\t\"cached\": round(1024 * cached / 1000),\n\t\t\t\"swap_total\": round(1024 * swap_total / 1000),\n\t\t\t\"swap_available\": round(1024 * swap_available / 1000),\n\t\t\t\"processes\": len(processes)\n\t\t}\n","repo_name":"dani3l0/Status","sub_path":"lib/machine/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"77"}
+{"seq_id":"658019129","text":"import os\n\nfrom ubyssey.secrets import Secrets\n\nfrom dispatch.default_settings import *\n\nBASE_URL = 'https://www.ubyssey.ca/'\nCANONICAL_DOMAIN = 'www.ubyssey.ca'\n\nSECRET_KEY = Secrets.get('SECRET_KEY')\n\nALLOWED_HOSTS = [\n    'ubyssey.ca',\n    'www.ubyssey.ca',\n    'ubyssey-prd.appspot.com',\n]\n\nINSTALLED_APPS += [\n    'ubyssey',\n    'ubyssey.events',\n]\n\nROOT_URLCONF = 'ubyssey.urls'\n\nUSE_TZ = True\n\nTIME_ZONE = 'America/Vancouver'\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.mysql',\n        'HOST': Secrets.get('SQL_HOST'),\n        'NAME': Secrets.get('SQL_DATABASE'),\n        'USER': Secrets.get('SQL_USER'),\n        'PASSWORD': Secrets.get('SQL_PASSWORD'),\n        'PORT': 3306,\n    }\n}\n\nTEMPLATES += [\n    {\n        'NAME': 'ubyssey',\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [\n            os.path.join(os.path.dirname(__file__), 'templates'),\n        ],\n    }\n]\n\nSESSION_ENGINE = 'gae_backends.sessions.cached_db'\nCACHES = {\n    'default': {\n        'BACKEND': 'gae_backends.memcache.MemcacheCache',\n    }\n}\n\nMIDDLEWARE_CLASSES += [\n    'canonical_domain.middleware.CanonicalDomainMiddleware',\n]\n\n# GCS File Storage\nDEFAULT_FILE_STORAGE = 'django_google_storage.storage.GoogleStorage'\n\nGS_ACCESS_KEY_ID = Secrets.get('GS_ACCESS_KEY_ID')\nGS_SECRET_ACCESS_KEY = Secrets.get('GS_SECRET_ACCESS_KEY')\nGS_STORAGE_BUCKET_NAME = 'ubyssey'\nGS_LOCATION = 'media'\n\nSTATICFILES_DIRS += (\n    os.path.join(os.path.dirname(__file__), 'static/dist'),\n)\n\nSTATIC_URL = 'https://ubyssey.storage.googleapis.com/static/'\nMEDIA_URL = 'https://ubyssey.storage.googleapis.com/media/'\n\n# Facebook\nFACEBOOK_CLIENT_ID = Secrets.get('FACEBOOK_CLIENT_ID')\nFACEBOOK_CLIENT_SECRET = Secrets.get('FACEBOOK_CLIENT_SECRET')\n\nEMAIL_HOST = Secrets.get('EMAIL_HOST')\nEMAIL_PORT = 465\nEMAIL_HOST_USER = Secrets.get('EMAIL_HOST_USER')\nEMAIL_HOST_PASSWORD = Secrets.get('EMAIL_HOST_PASSWORD')\nEMAIL_USE_SSL = True\n\nUBYSSEY_ADVERTISING_EMAIL = Secrets.get('UBYSSEY_ADVERTISING_EMAIL')\n\n# Use in-memory file handler on Google App Engine\nFILE_UPLOAD_HANDLERS = ['django.core.files.uploadhandler.MemoryFileUploadHandler',]\nFILE_UPLOAD_MAX_MEMORY_SIZE = 25621440\n","repo_name":"vt27/ubyssey.ca","sub_path":"_settings/settings-prd.py","file_name":"settings-prd.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27820119230","text":"from keras.preprocessing import image\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg16 import preprocess_input\nimport numpy as np\nfrom os import walk\nimport os, sys, getopt\nimport pickle as pickle\nimport scipy\nfrom keras.models import Model\n\n# Read VGG16 model\nmodel = VGG16(weights='imagenet', include_top=False)\n# redefine model to output right after the first hidden layer\nixs = [2, 5, 9, 13, 17,18]\n'''17 is used'''\nmodel = Model(inputs=model.inputs, outputs=model.layers[ixs[4]].output)\nmodel.summary()\n\ndef calc_vgg(path_images):\n\n    input_path =path_images\n    output_path=path_images+'../vgg_features'\n\n    vgg16_feature_list=[]\n\n    for (dirpath, dirnames,filenames) in walk(input_path):\n        for i, fname in enumerate(filenames):\n\n            # Load images\n            flnm=os.path.join(input_path + \"/\" + fname)\n            img = image.load_img(flnm, target_size=(224, 224))\n            img_data = image.img_to_array(img)\n            img_data = np.expand_dims(img_data, axis=0)\n            img_data = preprocess_input(img_data)\n\n            vgg16_feature = model.predict(img_data)\n            vgg16_feature_np = np.array(vgg16_feature)\n            vgg16_feature_list.append(vgg16_feature_np.flatten())\n\n        vgg16_feature_list_np = np.array(vgg16_feature_list)\n        print(vgg16_feature_list_np.shape)\n        vgg16_feature_list_means = vgg16_feature_list_np.mean(axis=0)\n\n        pickled_db_path=output_path\n\n        with open(pickled_db_path, 'wb') as fp:\n            pickle.dump(vgg16_feature_list_means, fp)\n        fp.close()\n\n\n    return output_path\n\n","repo_name":"RViMLab/OMIA2021-iOCT-Super-Resolution","sub_path":"metrics/l_feat/cal_vgg.py","file_name":"cal_vgg.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"23089939887","text":"from rest_framework.response import Response\nfrom collections import OrderedDict\nfrom rest_framework import pagination\n\nclass AngularPagination(pagination.LimitOffsetPagination):\n\n    def get_paginated_response(self, data):\n        #print(data)\n        out_dict = {}\n        obj_list = []\n        for i in data:\n            out_dict[i['id']] = i\n        out_list = []\n        for i in data:\n            out_list.append(i['id'])  \n            obj_list.append(i)          \n        return Response(OrderedDict([\n            ('totalCount', self.count),\n            ('next', self.get_next_link()),\n            ('previous', self.get_previous_link()),\n            ('results', out_dict),\n            ('results_list', obj_list),\n            ('ids', out_list),\n            ('status', 0)\n        ]))\n\n","repo_name":"zdimon/djng","sub_path":"backend/backend/angular_paginator.py","file_name":"angular_paginator.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"20447961763","text":"import pandas as pd\nimport numpy as np\nfrom scipy.stats import ks_2samp\nfrom itertools import chain\nimport os\nimport argparse\nimport math\nfrom collections import defaultdict\nimport shutil\nfrom copy import deepcopy\nfrom scipy.stats import norm\nimport math\nfrom sklearn.linear_model import LinearRegression\n\nskewness = {\n    \"email-Enron-full\": 1.220866,\n    \"email-Eu-full\" : 2.521832,\n    \"contact-high-school\" : 0.480156,\n    \"contact-primary-school\": 0.314277,\n    \"NDC-classes-full\" : 8.603801,\n    \"NDC-substances-full\" : 8.702568,\n    \"tags-ask-ubuntu\" : 10.307134,\n    \"tags-math-sx\" : 6.803574,\n    \"threads-ask-ubuntu\" : 52.012624,\n    \"threads-math-sx\" : 57.982697,\n    \"coauth-MAG-History-full\" : 52.414521,\n    \"coauth-MAG-Geology-full\" : 12.639768,\n}\n\ndataset = [\"contact-high-school\", \"contact-primary-school\", \"email-Enron-full\", \"email-Eu-full\", \"tags-math-sx\", \"tags-ask-ubuntu\", \"NDC-classes-full\", \"NDC-substances-full\", \"coauth-MAG-Geology-full\", \"coauth-MAG-History-full\", \"threads-ask-ubuntu\"]\n\n# portionlist = [\"0.10\",\"0.20\",\"0.30\", \"0.4s0\", \"0.50\"]\nportionlist = [0.1, 0.2, 0.3, 0.4, 0.5]\n\ndef _read(fname):\n    deg = -1\n    if os.path.isfile(fname) is False:\n        return -1\n    with open(fname , \"r\") as f:\n        lines = f.readlines()\n        for line in lines:\n            line = line.split(\"\\n\")[0]\n            tmp = line.split(\" : \")\n            if len(tmp) == 2:\n                eval_name, result = tmp\n                if eval_name == \"degree\":\n                    deg = float(result)\n                    break\n    return deg\n\ndef query_degDstat_Time(algoname, data, portion):\n    portion_str = \"%.2f\" % (portion)\n    dir_path = \"../results/\" + algoname + \"/\" + data + \"_\" + portion_str\n    avg_degDstat = 0.0\n    avg_time = 0.0\n    count = 0\n    for i in range(1,4):\n        fname = dir_path + \"/\" + str(i) + \"/result.txt\"\n        degDstat = _read(fname)\n        if degDstat == -1:\n#             print(fname)\n            continue\n        avg_degDstat += degDstat\n        count += 1\n    if count == 0:\n        print(algoname + \"/\" + data + \"_\" + portion_str)\n        return -1, -1\n\n    avg_degDstat /= count\n\n    fname = \"../results_time/\" + algoname + \"/\" + data + \"_\" + portion_str + \"/adjust_time.txt\"\n    with open(fname, \"r\") as f:\n        avg_time = float(f.readline())\n\n    return avg_degDstat, avg_time\n\ndef get_alpha(algoname):\n    alpha_str = algoname.split(\"_\")[-1]\n    return float(alpha_str)\n\ndef train_linear_regressor(except_dataset, grid_result_alpha):\n    X, Y = [], []\n    except_domain = except_dataset.split(\"-\")[0]\n    for portion in portionlist:    \n        for idx, dname in enumerate(dataset):\n            #if dname == except_dataset:\n            if except_domain in dname:\n                continue\n            X.append([portion, math.log2(skewness[dname]) ])\n            Y.append(math.log2(grid_result_alpha[portion][dname] + 1))\n\n    X, Y = np.array(X), np.array(Y)\n    reg = LinearRegression().fit(X, Y)\n    score = reg.score(X, Y)\n    coef, intercept = reg.coef_, reg.intercept_\n    \n    return reg, coef, intercept\n\ndef trained_model(reg, portion, sk):\n    x = np.array( [[portion, math.log2(sk)]] )\n    predicted_logscale= reg.predict(x)\n    predicted = 2 ** predicted_logscale - 1\n    \n    return predicted_logscale, predicted\n\ndef get_fname(alpha):\n    return \"es/global_deg_min_%.4f\" % (alpha)\n    \ndef run_midas_grid(portion, data, search_space):\n    algorithmlist = [\"es/global_deg_min_%.4f\" % a for a in search_space]\n\n    best_performing_alpha = -1\n    best_dstat = 1000\n    total_time = 0\n    trynum = 0\n    for algoname in algorithmlist:\n        degDstat, time = query_degDstat_Time(algoname, data, portion)\n        total_time += time\n        trynum += 1\n        if degDstat < best_dstat:\n            best_dstat = degDstat\n            best_performing_alpha = get_alpha(algoname)\n            \n    # Save Time\n    portion_str = \"%.2f\" % (portion)\n    outputdir = \"../results_time/midas_grid/\" + data + \"_\" + portion_str + \"/\"\n    if os.path.isdir(outputdir) is False:\n        os.makedirs(outputdir)\n    with open(outputdir + \"time.txt\", \"w\") as f:\n        f.write(str(total_time) + \"\\n\")\n\n    return best_performing_alpha, best_dstat, time, trynum\n\ndef run_midas(data, portion, trained_linear_model, search_space):\n    best_performing_alpha, best_dstat, time, trynum = -1, 1000, 0, 0\n    _, _alpha = trained_model(trained_linear_model, portion, skewness[data])\n\n    start_idx = -1\n    while start_idx < (len(search_space) - 1):\n        if search_space[start_idx + 1] < _alpha:\n            start_idx += 1\n        else:\n            break\n\n    if start_idx == -1:\n        search_direction = 1\n        start_idx = 0\n    elif start_idx == len(search_space) - 1:\n        search_direction = -1\n    else:\n        left_alpha = search_space[start_idx]\n        degDstat_left_alpha, spent = query_degDstat_Time(get_fname(left_alpha), data, portion)\n        time += spent\n        trynum += 1\n\n        right_alpha = search_space[start_idx + 1]\n        degDstat_right_alpha, spent = query_degDstat_Time(get_fname(right_alpha), data, portion)\n        time += spent\n        trynum += 1\n\n        if degDstat_left_alpha <= degDstat_right_alpha:\n            best_performing_alpha = left_alpha\n            best_dstat = degDstat_left_alpha\n            search_direction = -1\n            start_idx = start_idx - 1\n        else:\n            best_performing_alpha = right_alpha\n            best_dstat = degDstat_right_alpha\n            search_direction = 1\n            start_idx = start_idx + 2\n\n    idx = start_idx\n    while (idx >= 0) and (idx < len(search_space)):\n        next_alpha = search_space[idx]\n        degDstat, spent = query_degDstat_Time(get_fname(next_alpha), data, portion)\n        time += spent\n        trynum += 1\n        if best_dstat <= degDstat:\n            break\n        best_performing_alpha = next_alpha\n        best_dstat = degDstat\n        idx += search_direction\n\n    return best_performing_alpha, best_dstat, time, trynum\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--data', required=True, type=str)\n    parser.add_argument('--portion', required=True, type=float)\n    args = parser.parse_args()\n\n    search_space = sorted([float(\"%.4f\" % (2 ** i)) for i in np.arange(-3,6.5,0.5)] + [0.0])\n    \n    # MiDaS_Grid\n    result_alpha = defaultdict(dict)\n    result_dstat = defaultdict(dict)\n    result_time = defaultdict(dict)\n    result_try = defaultdict(dict)\n    for portion in portionlist:\n        for idx, data in enumerate(dataset):\n            best_performing_alpha, best_dstat, time, trynum = run_midas_grid(portion, data, search_space)\n            result_alpha[portion][data] = best_performing_alpha\n            result_dstat[portion][data] = best_dstat\n            result_time[portion][data] = time\n            result_try[portion][data] = trynum\n    \n    # print(result_alpha)\n    midas_grid = {\n        \"alpha\": result_alpha,\n        \"degDstat\": result_dstat,\n        \"time\": result_time,\n        \"try\": result_try\n    }\n    # Save Result\n    with open(\"../results/midas_grid/search_result.txt\", \"w\") as f:\n        for portion in portionlist:\n            portion_str = \"%.2f\" % (portion)\n            for data in dataset:\n                best_performing_alpha = result_alpha[portion][data]\n                alpha_name = \"../results/es/global_deg_min_%.4f\" % (best_performing_alpha)\n                f.write(alpha_name + \"/\" + data + \"_\" + portion_str + \"\\n\")\n\n    # MiDaS\n    trained_linear_model, coef, intercept = train_linear_regressor(args.data, midas_grid[\"alpha\"])\n    best_performing_alpha, best_dstat, time, trynum = run_midas(args.data, args.portion, trained_linear_model, search_space)\n\n    outputdir = \"../results_time/midas/\"\n    if os.path.isdir(outputdir) is False:\n        os.makedirs(outputdir)\n    with open(outputdir + \"time.txt\", \"w\") as f: # save time\n        f.write(str(time) + \"\\n\")\n\n    outputdir = \"../results/midas/\"\n    if os.path.isdir(outputdir) is False:\n        os.makedirs(outputdir)\n    outputname = outputdir + \"search_result.txt\"\n    with open(outputname, \"a+\") as f: # save result\n        f.write(\"../results/es/global_deg_min_%.4f\" % (best_performing_alpha) + \"/\" + args.data + \"_%.2f\" % (args.portion) + \"\\n\")\n\n","repo_name":"young917/MiDaS","sub_path":"analyze/midas.py","file_name":"midas.py","file_ext":"py","file_size_in_byte":8271,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"35036961454","text":"from CA_1D import cellular_automata, plot_cellular_automata\n\n#----------------------Assigning values to variables----------------------------------\n\niterations = 4*256\nsize = 2*iterations+1\nrule = 102\n# Survival probability is equal to 1 if the cellular automata is deterministic\nsurvival_prob = 1\n# If a standard type is implemented, size should be bigger or equal to 2*iteration+1 becaus is coded for periodic boundarie and we would avoid collisions\ntype = 'standard'\n# Is the probability of appearing living cells when considering random conditions in the initial array\ngeneration_probability = 0.5\n\n#-----------------------Implementation of the code------------------------------------\n\n# Implememntation of the cellular automata and the measure of fractal dimension\nCA = cellular_automata(iterations, size, rule, survival_prob, type, generation_probability)\nplot_cellular_automata(CA)","repo_name":"teodoromayayo/1D-cellular-automata","sub_path":"example_1.py","file_name":"example_1.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6655567762","text":"'''\n    Contains some functions related to the creation of the line chart.\n'''\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport hover_template\n\nfrom template import THEME\n\n\ndef get_empty_figure():\n    '''\n        Returns the figure to display when there is no data to show.\n\n        The text to display is : 'No data to display. Select a cell\n        in the heatmap for more information.\n\n    '''\n    \n    #create empty figure with specified text\n    empty_scatter = go.Scatter(x=[], y=[])\n    display_text = {\n        'x': 0.5,\n        'y': 0.5,\n        'showarrow': False,\n        'font': {'size': 12} ,\n        'text': 'No data to display. Select a cell in the heatmap for more information.',\n    }\n    \n    empty_layout = {\n        'ticks': '',\n        'showticklabels': False,\n        'showgrid': False,\n        'zeroline': False,\n        'showline': False\n    }\n\n    empty_layout = go.Layout(\n        dragmode=False,\n        showlegend=False,\n        annotations=[display_text],\n        xaxis=empty_layout,\n        yaxis=empty_layout,\n    )\n\n    fig = go.Figure(data=[empty_scatter], layout=empty_layout)\n    return fig\n\n\ndef add_rectangle_shape(fig):\n    '''\n        Adds a rectangle to the figure displayed\n        behind the informational text. The color\n        is the 'pale_color' in the THEME dictionary.\n\n        The rectangle's width takes up the entire\n        paper of the figure. The height goes from\n        0.25% to 0.75% the height of the figure.\n    '''\n    \n    #create the rectangle\n    fig.update_yaxes(range=[0, 1])\n    fig.update_xaxes(range=[0, 1])\n\n    fig.add_shape(\n        type='rect',\n        layer='below',\n        fillcolor=THEME['pale_color'],\n        line_color=THEME['pale_color'],\n        x0=0,\n        x1=1,\n        y0=0.25,\n        y1=0.75\n    )\n\n    return fig\n\n\n\ndef get_figure(line_data, arrond, year):\n    '''\n        Generates the line chart using the given data.\n\n        The ticks must show the zero-padded day and\n        abbreviated month. The y-axis title should be 'Trees'\n        and the title should indicated the displayed\n        neighborhood and year.\n\n        In the case that there is only one data point,\n        the trace should be displayed as a single\n        point instead of a line.\n\n        Args:\n            line_data: The data to display in the\n            line chart\n            arrond: The selected neighborhood\n            year: The selected year\n        Returns:\n            The figure to be displayed\n    '''\n    #get line chart mode\n    line_mode = ''\n    if(len(line_data) == 1):\n     line_mode = 'markers' \n    else:\n     line_mode = 'lines'   \n      \n    line_trace = go.Scatter(\n        mode=line_mode,\n        x=line_data['Date_Plantation'],\n        y=line_data['Counts'],\n    )\n    \n    line_fig = go.Figure(\n        layout_title_text=f'Trees planted in {arrond} in {year}',\n        data=[line_trace]\n    )\n    \n    #draw line chart\n    line_fig.update_traces(hovertemplate=hover_template.get_linechart_hover_template())\n    line_fig.update_xaxes(tickformat='%d %b', tickangle=-45)\n    line_fig.update_yaxes(title_text='Trees')\n    \n    return line_fig","repo_name":"bazfar/Data-visualisation","sub_path":"Tp/tp3/line_chart.py","file_name":"line_chart.py","file_ext":"py","file_size_in_byte":3151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2312280243","text":"import dash\nfrom dash import html, dcc, callback, Input, Output,dash_table\nimport dash_bootstrap_components as dbc\nfrom data import *\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport json\n\ndash.register_page(__name__)\n\ndef analysisLayout():\n\n    heading_result = html.Div([html.H4(['Clustering of Indian States Based on Multidimensional Poverty Index Indicators'],\n                         style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    \n    \n    df1 = pd.read_csv(\"Data/State_Factors_Embeding.csv\")\n    df2 = pd.read_csv(\"Data/Final_Processed.csv\")\n    df = df1.merge(df2,on=\"State\",how=\"inner\")\n    #df[\"FA_Cluster\"] = df[\"FA_Cluster\"].apply(lambda x : str(x))\n    df[\"State Cluster\"] = df[\"State Cluster\"].apply(lambda x : str(x))\n\n    #Normalise MPI Index so that difference in size is visibile\n    df[\"MPI Index Normalised\"] = (df[\"MPI Index\"] - np.min(df[\"MPI Index\"]))/(np.max(df[\"MPI Index\"])-np.min(df[\"MPI Index\"]))\n\n    #df['text'] = df['State'] + '<br>' + 'MPI Index: ' + df['MPI Index'].astype(str)\n\n    \n    \n    # plot clusters\n    fig_cluster = px.scatter(df, x=\"Factor-1\", y=\"Factor-2\",color=\"State Cluster\",\n                             hover_data={'State Cluster':False,'MPI Index Normalised':False,\n                                         \"Factor-1\":False,\"Factor-2\":False,\n                                         \"State\":True , \"MPI Index\":':.4f'},\n                             size=\"MPI Index Normalised\")\n\n    fig_cluster.update_layout(title={'text': \"<b>State Clusters Based on MPI Indicators</b>\",\n                                    'y':1.0,'x':0.51, 'xanchor': 'center','yanchor': 'top'},\n                              margin=dict(l=0, r=0, t=25, b=0),height=400,\n                              xaxis=dict(title=dict(font=dict(size=16, family='Arial'))),\n                              yaxis=dict(title=dict(font=dict(size=16, family='Arial'))))\n    \n    fig_cluster = dcc.Graph(figure=fig_cluster)  \n    fig_cluster_div = html.Div([fig_cluster],id=\"analysis_state_clusters\")\n\n    # plot bar-chart comparisons\n    #contribution_colnames = [i for i in df.columns if \"Contribution\" in i]\n    #colnames = [i for i in df.columns if \"Contribution\" in i]\n    #colnames.append(\"State\")\n    #colnames.append(\"MPI Index\")\n    #colnames.append(\"FA_Cluster\")\n    #df = df[colnames]\n\n    #input_state = \"Kerala\"\n    #df_state = df[df[\"State\"]==input_state]\n    #contribution_state = [df_state[i].values[0] for i in contribution_colnames]\n\n    #state_cluster = \"0\"\n    #df_cluster = df[df[\"FA_Cluster\"]==state_cluster]\n    #df_cluster = df_cluster.mean(axis=0)\n    #df_cluster = df_cluster.T\n    #contribution_cluster_0 = [df_cluster[i] for i in contribution_colnames]\n\n    #state_cluster = \"1\"\n    #df_cluster = df[df[\"FA_Cluster\"]==state_cluster]\n    #df_cluster = df_cluster.mean(axis=0)\n    #df_cluster = df_cluster.T\n    #contribution_cluster_1 = [df_cluster[i] for i in contribution_colnames]\n\n    #state_cluster = \"2\"\n    #df_cluster = df[df[\"FA_Cluster\"]==state_cluster]\n    #df_cluster = df_cluster.mean(axis=0)\n    #df_cluster = df_cluster.T\n    #contribution_cluster_2 = [df_cluster[i] for i in contribution_colnames]\n\n    #df_india = df.copy()\n    #df_india = df_india.mean(axis=0)\n    #df_india = df_india.T\n    #contribution_india = [df_india[i] for i in contribution_colnames]\n\n    #all_count = len(contribution_colnames)\n    #contribution_all = contribution_state + contribution_cluster_0 + contribution_cluster_1 + \\\n    #                   contribution_cluster_2\n    #type_all = [input_state]*all_count+[\"Cluster_0\"]*all_count+[\"Cluster_1\"]*all_count+\\\n    #           [\"Cluster_2\"]*all_count\n\n    # change name to display on UI\n    #contribution_colnames_display = [i.replace(\"Contribution_\",\"\") for i in contribution_colnames]\n    #dff = pd.DataFrame({\"Contribution in MPI\":contribution_all,\"Indicator\":contribution_colnames_display*4,\n    #                   \"Type\":type_all})\n    #dff = dff.sort_values(\"Contribution in MPI\").reset_index().drop(columns=\"index\")\n    \n    \n    #fig_compare = px.bar(dff, y=\"Indicator\", x=\"Contribution in MPI\", \n    #                     color=\"Type\",color_discrete_sequence=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'], \n    #                     barmode=\"group\",orientation='h')\n\n    #fig_compare.update_layout(title={'text': \"<b>Comparison</b>\",'y':1.0,'x':0.51, \n    #                                 'xanchor': 'center','yanchor': 'top'},\n    #                          margin=dict(l=0, r=0, t=25, b=0),height=1000,\n    #                          legend=dict(x=0,y=1.025,orientation=\"h\",bgcolor='rgba(255, 255, 255, 0)'))\n    \n    #fig_compare = dcc.Graph(figure=fig_compare)  \n    #fig_compare_div = html.Div([fig_compare],id=\"analysis_state_compare\")\n\n    #fig_cluster_final = html.Div([fig_cluster_div,fig_compare_div],id=\"analysis_cluster_final\")\n    \n    heading1 = html.Div([html.H4(['Motivation'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    content1 = html.Div([html.P(\"In our analysis of data on poverty in different states, we found that\\\n                                 using the Multidimensional Poverty Index (MPI) alone isn't enough to \\\n                                 understand the full picture. While the MPI can help us identify which \\\n                                 states are doing poorly in terms of poverty, breaking down the data by\\\n                                 individual indicators gives us a more nuanced understanding. For example,\\\n                                 we found that Himachal Pradesh and Arunachal Pradesh both have an MPI of \\\n                                 0.48 and 0.46 respectively, but the contribution of illiteracy to their \\\n                                 overall MPI is very different: just 11% for Himachal Pradesh, compared to \\\n                                 31% for Arunachal Pradesh. Similarly, Jharkhand and Tripura have similar \\\n                                 MPIs (0.6 and 0.59, respectively), but the contribution of attendance \\\n                                 ratios to their overall MPI is 8% and 28%, respectively.This highlights \\\n                                the importance of looking at individual indicators, rather than just relying\\\n                                on overall MPI scores. By identifying which states have similar contributing\\\n                                indicators, we can make more targeted policy recommendations to address \\\n                                poverty in those areas. For instance, our analysis found that Bihar, \\\n                                Odisha,Assam, and Jharkhand are all struggling with the same set of \\\n                                indicators, so similar policy improvements could be implemented in those \\\n                                states.\")],className=\"analysis_content\")  \n\n    \n    df = pd.read_csv(\"Data/Final_Processed.csv\")\n    df = df[['Illiterate population (%)', 'Deprived_Cooking_Fuel (%)','Deprived_Sanitisation (%)', 'Deprived_Drinking_Water (%)',\n         'Deprived_Electricity (%)', 'Deprived_House (%)', 'Deprived_Assets (%)','Infant Mortality Rate (%)', 'Attendance Ratio',\n         'Adults BMI Below Normal']]\n    corr = df.corr()\n    mask = np.tri(*corr.shape, k=0)\n    corr_masked = np.ma.array(corr, mask=mask)\n    fig_corr = go.Figure(data=go.Heatmap(z=(corr.values)*mask,x=corr.columns,y=corr.columns,text=(corr.values)*mask,texttemplate='%{text:.2f}',\n                                colorscale=\"rdbu\"))\n    \n    fig_corr.update_layout(title={'text': \"<b>Correlation Between Contributions By Indicators</b>\",'y':1.0,'x':0.50, \n                              'xanchor': 'center','yanchor': 'top'},\n                           margin=dict(l=0, r=0, t=25, b=0))\n    \n    correlation_graph = dcc.Graph(figure=fig_corr)  \n    correlation_graph_div = html.Div([correlation_graph])\n\n    heading2 = html.Div([html.H4(['Factor Analysis'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    content2 = html.Div([html.P(\"Factor analysis is a statistical method used to identify underlying \\\n                                 factors or hidden drivers that contribute to observed variations in a \\\n                                 set of variables. In our study, we have identified 10 indicators that \\\n                                 make up the Multidimensional Poverty Index (MPI). However, it is possible \\\n                                 that there are fewer, underlying factors that are actually responsible for\\\n                                 the variation in these 10 indicators. By using factor analysis, we can \\\n                                 try to identify the number of these latent factors and understand how \\\n                                 they relate to the observed indicators. This can help us identify the \\\n                                 key factors that we can focus on in order to improve all of the indicators.\\\n                                 The correlation matrix shows that some of the indicators are correlated \\\n                                 with each other, making it an appropriate choice to use factor analysis \\\n                                 to explore the latent factors that may be responsible for the observed \\\n                                 correlations in the data.\")],className=\"analysis_content\")\n\n    heading3 = html.Div([html.H4(['Methodology'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    subheading3 = html.Div([html.H4(['Preliminary Test'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_subheading\")\n    content3 = html.Div([html.P(\"It is important to verify the quality of the data before performing factor \\\n                                 analysis. The Bartlett sphericity test can be used to determine whether \\\n                                 the correlation matrix of the data is significantly different from an \\\n                                 identity matrix. If it is, this suggests that there is sufficient structure\\\n                                 in the data to proceed with factor analysis. In our study, we found that \\\n                                 the correlations in the data indicate the presence of latent factors that\\\n                                 underlie the data.\")],className=\"analysis_content\")\n    subheading4 = html.Div([html.H4(['Identifying Number of Factors'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_subheading\")\n    \n    \n    df = pd.read_csv(\"Data/Num_Factors_Eigen_Values.csv\")\n    y = list(df[\"Eigen Value\"].values)\n    x = list(df[\"Num of Factors\"].values)\n    fig4 = px.line(df,x=\"Num of Factors\", y=\"Eigen Value\",markers=True)\n    fig4.update_layout(title={'text': \"<b>Number of Factors Scree Plot</b>\",'y':1.0,'x':0.51, \n                              'xanchor': 'center','yanchor': 'top'},\n                      margin=dict(l=0, r=0, t=25, b=0),height=300,width=300)\n    fig4.add_hline(y=1, line_width=1, line_dash=\"dash\", line_color=\"red\")\n    graph_figure_4 = dcc.Graph(figure=fig4)  \n    graph_div_4 = html.Div([graph_figure_4],\n                           style={\"display\":\"inline-block\",\"width\":\"350px\",\"border-radius\":\"25px\"})\n    \n    content4 = html.Div([html.P(\"To identify the number of factors that contribute to the covariance in\\\n                                 the data, we used Eigenvalues. These values indicate the amount of \\\n                                 variance explained by each factor. In our analysis, we only included \\\n                                 factors that had Eigenvalues greater than 1, as these factors explain \\\n                                 more variance than a single indicator. As shown in the figure, there \\\n                                 are two factors that have Eigenvalues greater than 1. Therefore, we \\\n                                 selected these two factors for our analysis.\"),graph_div_4],\n                                 className=\"analysis_content\",\n                                 id=\"analysis_num_factors\")\n\n    heading5 = html.Div([html.H4(['Factor Analysis Results'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    content5 = html.Div([html.P(\"The results of our factor analysis model showed that 60% of the variation\\\n                                 in the original dataset could be explained by two factors. These factors,\\\n                                 labeled as Factor-1 and Factor-2, had significant influence on several \\\n                                 variables in the dataset. For example, variables such as Deprived Cooking \\\n                                 Fuel, Deprived House, and Deprived Assets had high loading on Factor-1, \\\n                                 meaning they were largely influenced by this factor. On the other hand, \\\n                                 variables such as Deprived Drinking Water, Deprived Electricity, Infant \\\n                                 Mortality Rate, and Nutrition had high loading on Factor-2, indicating \\\n                                 they were primarily associated with this factor. Other variables, such as\\\n                                 Illiteracy, Deprived Sanitation, and Attendance Ratio, had loading on both\\\n                                 factors, suggesting they were impacted by both.The significance of this\\\n                                 analysis is that if we focus on improving Factor-1, we could expect to \\\n                                 see improvements in indicators like Cooking Fuel, Housing, and Assets. \\\n                                 Similarly, working to improve Factor-2 could lead to positive changes \\\n                                 in indicators like Drinking Water, Electricity, Infant Mortality Rate, \\\n                                and Nutrition.\")],className=\"analysis_content\")\n    \n    \n    df = pd.read_csv(\"Data/Variable_Variance_Explained_By_Factor.csv\")\n    data_table = dash_table.DataTable(df.to_dict('records'), [{\"name\": i, \"id\": i} for i in df.columns],\n                                       fixed_rows={'headers': True},\n                                       style_table={'overflowX': 'auto'},\n                                       style_cell={'height': 'auto','minWidth': '180px', \n                                                    'width': '180px', 'maxWidth': '180px',\n                                                    'whiteSpace': 'normal',\n                                                    'fontSize':15, 'font-family':'sans-serif',\n                                                    \"text-align\":\"center\"},\n                                      style_header={'backgroundColor': 'rgb(30, 30, 30)',\n                                                    'color': 'white',\"font-weight\":\"bolder\"})\n\n    heading6 = html.Div([html.H4(['Clustering With Factors'],style={\"font-weight\":\"bold\",\"margin-left\":\"5px\"})],className=\"analysis_heading\")\n    \n    df = pd.read_csv(\"Data/Num_Cluster_Distortion_Score.csv\")\n    fig6 = px.line(df,x=\"Num of Clusters\", y=\"Distortion Score\",markers=True)\n    fig6.update_layout(title={'text': \"<b>Number of Clusters Scree Plot</b>\",'y':1.0,'x':0.50, \n                              'xanchor': 'center','yanchor': 'top'},\n                      margin=dict(l=0, r=0, t=25, b=0),height=450,width=550)\n    fig6.add_vline(x=3, line_width=1, line_dash=\"dash\", line_color=\"red\")\n    graph_figure_6 = dcc.Graph(figure=fig6)  \n    graph_div_6 = html.Div([graph_figure_6],\n                           style={\"display\":\"inline-block\",\"width\":\"50%\",\"height\":\"350px\",\"border-radius\":\"25px\"})\n\n    df = pd.read_csv(\"Data/Silhouette_Score.csv\")\n\n    # first sort by silhouete score\n    df = df.sort_values(\"Silhouette Score\").reset_index().drop(columns=\"index\")\n    df[\"Labels\"] = df[\"Labels\"].apply(lambda x : str(x))\n    # now grouby by labels so that similary groups score are plotted together.\n    df1 = pd.DataFrame()\n    for i,j in df.groupby(\"Labels\"):\n        df1 = pd.concat([df1,j],axis=0)\n    \n    df1 = df1.reset_index().drop(columns=\"index\")\n    df = df1.copy()\n    fig6_1 = px.bar(df, x='Silhouette Score', color=\"Labels\")\n    fig6_1.update_layout(showlegend=False)\n    fig6_1.update_layout(title={'text': \"<b>Silhouette Score</b>\",'y':1.0,'x':0.52, \n                              'xanchor': 'center','yanchor': 'top'},\n                         margin=dict(l=0, r=0, t=25, b=0),height=450,width=550)\n    fig6_1.add_vline(x=0.48, line_width=1, line_dash=\"dash\", line_color=\"black\")               \n    graph_figure_6_1 = dcc.Graph(figure=fig6_1)  \n    graph_div_6_1 = html.Div([graph_figure_6_1],\n                           style={\"display\":\"inline-block\",\"width\":\"50%\",\"height\":\"350px\",\"border-radius\":\"25px\"})\n\n    \n    \n    content6 = html.Div([html.P(\"This section describes how clustering analysis was performed using KMeans \\\n                                 Clustering. Clustering is a technique for dividing a dataset into groups \\\n                                 (also known as clusters) of similar data points. The motivation of \\\n                                 clustering has been previously explained. Before applying KMeans \\\n                                 Clustering, we identified two latent factors. These latent factors are \\\n                                 underlying features or characteristics that are not directly observed, \\\n                                 but can be inferred from observed data. Identifying latent factors can \\\n                                 help to focus the analysis and improve the interpretability of the results.\\\n                                 Once the latent factors have been identified, we used KMeans Clustering to \\\n                                 group the data points into clusters.KMeans Clustering is a popular \\\n                                 algorithm that divides a dataset into a specified number of clusters by\\\n                                 iteratively reassigning data points to the cluster with the nearest mean,\\\n                                 until the cluster means no longer change. One issue with using KMeans \\\n                                 Clustering is determining the appropriate number of clusters to use. \\\n                                 We addressed this by using a distortion score elbow plot, which is a \\\n                                 graphical tool that plots the distortion score\\\n                                 (a measure of within-cluster variance) against the number of clusters.\\\n                                 The elbow in the plot indicates the optimal number of clusters, \\\n                                 as it is the point at which the distortion score decreases at a \\\n                                 diminishing rate. Based on the elbow plot, we selected 3 clusters for our \\\n                                 analysis. Finally, we performed KMeans Clustering with 3 clusters and \\\n                                 the two identified latent factors, and obtained a silhouette score of \\\n                                 0.48. The resulting clusters and silhouette score provide valuable \\\n                                 insights into the patterns and relationships within the data.\"),\n                                 graph_div_6,graph_div_6_1],\n                                 className=\"analysis_content\")\n\n\n    layout = html.Div([ heading_result,fig_cluster_div,heading1,content1,correlation_graph_div,\n                        heading2,content2,heading3,subheading3,content3,\n                        subheading4,content4,heading5,content5,data_table,heading6,content6])\n\n    return layout\n\nlayout = analysisLayout()\n","repo_name":"PalashJauhari/MPI-Project","sub_path":"pages/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":19798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36868579875","text":"from functools import wraps\nfrom dataclasses import dataclass\nfrom typing import Callable, TypeVar, Sequence, Mapping, Optional, Any\n\nT = TypeVar('T')\n\n\n@dataclass\nclass Call(object):\n    args: Sequence[Any]\n    kwargs: Mapping[str, Any]\n    exception: Optional[Exception] = None\n\n\ndef record_calls(fn: Callable[..., T]) -> Callable[..., T]:\n    \"\"\"\n    Decorator that maintains the call information of a function during execution.\n    \"\"\"\n    fn.calls = []\n\n    @wraps(fn)\n    def wrapper(*args: Sequence[Any], **kwargs: Optional[Mapping[str, Any]]):\n        current_call = Call(args=args, kwargs=kwargs)\n        try:\n            return_value = fn(*args, **kwargs)\n            current_call.return_value = return_value\n        except Exception as exc:\n            current_call.exception = exc\n            current_call.return_value = None\n            raise\n        finally:\n            fn.calls.append(current_call)\n\n        return return_value\n\n    return wrapper\n\n","repo_name":"wranglerr/python-practice","sub_path":"python_practice/record_calls.py","file_name":"record_calls.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2158899198","text":"from setuptools import setup\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetup(\n    name=\"ScEqAn\",\n    version=\"1.0.0\",\n    description=\"Scientific Equation/Expression Analysis Based On Dynamic Program Analysis\",\n    py_modules=[\"ScEqAn/Analysis\"],\n    package_dir={\"\": \"SRCS\"},\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.7\",\n        \"License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)\",\n        \"Operating System :: OS Independent\",\n    ],\n    url=\"https://github.com/Palani-SN/ScEqAn\",\n    author=\"Palani-SN\",\n    author_email=\"psn396@gmail.com\",\n\n    install_requires = [\n        \"blessings ~= 1.7\",\n    ],\n\n    extras_require = {\n        \"dev\": [\n            \"pytest >= 3.7\",\n            \"check-manifest\",\n            \"twine\",\n        ],\n    },\n)\n","repo_name":"Palani-SN/ScEqAn","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32498021207","text":"import numpy as np\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport argparse\nimport pickle\n\nfrom models import CCFlyingSquid, CCMLE\nfrom utils import run_sweep, cross_entropy, get_real_fns\n\nUNLABELED_SWEEP = [(100, 900, 100), (1000, 9000, 1000), (10000, 40000, 10000)]\nLABELED_SWEEP = [(10, 98, 2), (100, 900, 100), (1000, 9000, 1000), (10000, 40000, 10000)]\nAGGS = [\"mean\", \"median\"]\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--dataset\", type=str, choices=[\"imdb\"], default=\"imdb\")\n    parser.add_argument(\"--num_trials\", type=int, default=1000)\n    args = parser.parse_args()\n\n    (L_train, L_test), (Y_train, Y_test) = pickle.load(open(Path(\"data\") / f\"{args.dataset}.pkl\", \"rb\"))\n    sample_fn, loss_fn = get_real_fns(L_train, L_test, Y_train, Y_test)\n    class_balance = np.mean(Y_train)\n\n    save_path = Path(\"results\") / args.dataset\n    save_path.mkdir(exist_ok=True)\n\n    unlabeled_models = {agg: CCFlyingSquid(agg=agg, fast=True) for agg in AGGS}\n    labeled_model = CCMLE()\n\n    unlabeled_losses = defaultdict(list)\n    labeled_losses = []\n    for trial in tqdm(range(args.num_trials)):\n        for agg in AGGS:\n            unlabeled_losses[agg].append(run_sweep(sample_fn, loss_fn, unlabeled_models[agg], class_balance, UNLABELED_SWEEP))\n        labeled_losses.append(run_sweep(sample_fn, loss_fn, labeled_model, class_balance, LABELED_SWEEP))\n\n        for agg in AGGS:\n            np.save(save_path / f\"unlabeled_losses_{agg}.npy\", unlabeled_losses[agg])\n        np.save(save_path / f\"labeled_losses.npy\", labeled_losses)\n","repo_name":"bencw99/comparing-labeled-and-unlabeled-data","sub_path":"scripts/run_real_experiments.py","file_name":"run_real_experiments.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"22175097676","text":"from typing import Callable, Dict, Optional, Sequence, Set, Tuple, Union, TYPE_CHECKING\n\nimport re\nimport numpy as np\n\nfrom cirq import ops, linalg, protocols, value\n\nif TYPE_CHECKING:\n    import cirq\n\n\n@value.value_equality(approximate=True)\nclass QasmUGate(ops.Gate):\n    def __init__(self, theta, phi, lmda) -> None:\n        \"\"\"A QASM gate representing any single qubit unitary with a series of\n        three rotations, Z, Y, and Z.\n\n        The angles are normalized to the range [0, 2) half_turns.\n\n        Args:\n            theta: Half turns to rotate about Y (applied second).\n            phi: Half turns to rotate about Z (applied last).\n            lmda: Half turns to rotate about Z (applied first).\n        \"\"\"\n        self.lmda = lmda % 2\n        self.theta = theta % 2\n        self.phi = phi % 2\n\n    def _num_qubits_(self) -> int:\n        return 1\n\n    @staticmethod\n    def from_matrix(mat: np.ndarray) -> 'QasmUGate':\n        pre_phase, rotation, post_phase = linalg.deconstruct_single_qubit_matrix_into_angles(mat)\n        return QasmUGate(rotation / np.pi, post_phase / np.pi, pre_phase / np.pi)\n\n    def _has_unitary_(self):\n        return True\n\n    def _qasm_(self, qubits: Tuple['cirq.Qid', ...], args: 'cirq.QasmArgs') -> str:\n        args.validate_version('2.0')\n        return args.format(\n            'u3({0:half_turns},{1:half_turns},{2:half_turns}) {3};\\n',\n            self.theta,\n            self.phi,\n            self.lmda,\n            qubits[0],\n        )\n\n    def __repr__(self) -> str:\n        return (\n            f'cirq.circuits.qasm_output.QasmUGate('\n            f'theta={self.theta!r}, '\n            f'phi={self.phi!r}, '\n            f'lmda={self.lmda})'\n        )\n\n    def _decompose_(self, qubits):\n        q = qubits[0]\n        return [\n            ops.rz(self.lmda * np.pi).on(q),\n            ops.ry(self.theta * np.pi).on(q),\n            ops.rz(self.phi * np.pi).on(q),\n        ]\n\n    def _value_equality_values_(self):\n        return self.lmda, self.theta, self.phi\n\n    def _json_dict_(self) -> Dict[str, float]:\n        return {'theta': self.theta, 'phi': self.phi, 'lmda': self.lmda}\n\n    @classmethod\n    def _from_json_dict_(cls, theta: float, phi: float, lmda: float, **kwargs) -> 'QasmUGate':\n        return cls(theta, phi, lmda)\n\n\n@value.value_equality\nclass QasmTwoQubitGate(ops.Gate):\n    def __init__(self, kak: 'cirq.KakDecomposition') -> None:\n        \"\"\"A two qubit gate represented in QASM by the KAK decomposition.\n\n        All angles are in half turns.  Assumes a canonicalized KAK\n        decomposition.\n\n        Args:\n            kak: KAK decomposition of the two-qubit gate.\n        \"\"\"\n        self.kak = kak\n\n    def _num_qubits_(self) -> int:\n        return 2\n\n    def _value_equality_values_(self):\n        return self.kak\n\n    @staticmethod\n    def from_matrix(mat: np.ndarray, atol=1e-8) -> 'QasmTwoQubitGate':\n        \"\"\"Creates a QasmTwoQubitGate from the given matrix.\n\n        Args:\n            mat: The unitary matrix of the two qubit gate.\n            atol: Absolute error tolerance when decomposing.\n\n        Returns:\n            A QasmTwoQubitGate implementing the matrix.\n        \"\"\"\n        kak = linalg.kak_decomposition(mat, atol=atol)\n        return QasmTwoQubitGate(kak)\n\n    def _unitary_(self):\n        return protocols.unitary(self.kak)\n\n    def _decompose_(self, qubits: Sequence['cirq.Qid']) -> 'cirq.OP_TREE':\n        q0, q1 = qubits\n        x, y, z = self.kak.interaction_coefficients\n        a = x * -2 / np.pi + 0.5\n        b = y * -2 / np.pi + 0.5\n        c = z * -2 / np.pi + 0.5\n\n        b0, b1 = self.kak.single_qubit_operations_before\n        yield QasmUGate.from_matrix(b0).on(q0)\n        yield QasmUGate.from_matrix(b1).on(q1)\n\n        yield ops.X(q0) ** 0.5\n        yield ops.CNOT(q0, q1)\n        yield ops.X(q0) ** a\n        yield ops.Y(q1) ** b\n        yield ops.CNOT(q1, q0)\n        yield ops.X(q1) ** -0.5\n        yield ops.Z(q1) ** c\n        yield ops.CNOT(q0, q1)\n\n        a0, a1 = self.kak.single_qubit_operations_after\n        yield QasmUGate.from_matrix(a0).on(q0)\n        yield QasmUGate.from_matrix(a1).on(q1)\n\n    def __repr__(self) -> str:\n        return f'cirq.circuits.qasm_output.QasmTwoQubitGate({self.kak!r})'\n\n\nclass QasmOutput:\n    \"\"\"Representation of a circuit in QASM (quantum assembly) format.\n\n    Please note that the QASM importer is in an experimental state and\n    currently only supports a subset of the full OpenQASM spec.\n    Amongst others, classical control, arbitrary gate definitions,\n    and even some of the gates that don't have a one-to-one representation\n    in Cirq, are not yet supported.\n\n    QASM output can be saved to a file using the save method.\n    \"\"\"\n\n    valid_id_re = re.compile(r'[a-z][a-zA-Z0-9_]*\\Z')\n\n    def __init__(\n        self,\n        operations: 'cirq.OP_TREE',\n        qubits: Tuple['cirq.Qid', ...],\n        header: str = '',\n        precision: int = 10,\n        version: str = '2.0',\n    ) -> None:\n        \"\"\"Representation of a circuit in QASM format.\n\n        Args:\n            operations: Tree of operations to insert.\n            qubits: The qubits used in the operations.\n            header: A multi-line string that is placed in a comment at the top\n                of the QASM.\n            precision: The number of digits after the decimal to show for\n                numbers in the QASM code.\n            version: The QASM version to target. Objects may return different\n                QASM depending on version.\n        \"\"\"\n        self.operations = tuple(ops.flatten_to_ops(operations))\n        self.qubits = qubits\n        self.header = header\n        self.measurements = tuple(\n            op for op in self.operations if isinstance(op.gate, ops.MeasurementGate)\n        )\n        meas_key_id_map, meas_comments = self._generate_measurement_ids()\n        self.meas_comments = meas_comments\n        qubit_id_map = self._generate_qubit_ids()\n        self.args = protocols.QasmArgs(\n            precision=precision,\n            version=version,\n            qubit_id_map=qubit_id_map,\n            meas_key_id_map=meas_key_id_map,\n        )\n\n    def _generate_measurement_ids(self) -> Tuple[Dict[str, str], Dict[str, Optional[str]]]:\n        # Pick an id for the creg that will store each measurement\n        meas_key_id_map: Dict[str, str] = {}\n        meas_comments: Dict[str, Optional[str]] = {}\n        meas_i = 0\n        for meas in self.measurements:\n            key = protocols.measurement_key_name(meas)\n            if key in meas_key_id_map:\n                continue\n            meas_id = f'm_{key}'\n            if self.is_valid_qasm_id(meas_id):\n                meas_comments[key] = None\n            else:\n                meas_id = f'm{meas_i}'\n                meas_i += 1\n                meas_comments[key] = ' '.join(key.split('\\n'))\n            meas_key_id_map[key] = meas_id\n        return meas_key_id_map, meas_comments\n\n    def _generate_qubit_ids(self) -> Dict['cirq.Qid', str]:\n        return {qubit: f'q[{i}]' for i, qubit in enumerate(self.qubits)}\n\n    def is_valid_qasm_id(self, id_str: str) -> bool:\n        \"\"\"Test if id_str is a valid id in QASM grammar.\"\"\"\n        return self.valid_id_re.match(id_str) is not None\n\n    def save(self, path: Union[str, bytes, int]) -> None:\n        \"\"\"Write QASM output to a file specified by path.\"\"\"\n        with open(path, 'w') as f:\n\n            def write(s: str) -> None:\n                f.write(s)\n\n            self._write_qasm(write)\n\n    def __str__(self) -> str:\n        \"\"\"Return QASM output as a string.\"\"\"\n        output = []\n        self._write_qasm(lambda s: output.append(s))\n        return ''.join(output)\n\n    def _write_qasm(self, output_func: Callable[[str], None]) -> None:\n        self.args.validate_version('2.0')\n\n        # Generate nice line spacing\n        line_gap = [0]\n\n        def output_line_gap(n):\n            line_gap[0] = max(line_gap[0], n)\n\n        def output(text):\n            if line_gap[0] > 0:\n                output_func('\\n' * line_gap[0])\n                line_gap[0] = 0\n            output_func(text)\n\n        # Comment header\n        if self.header:\n            for line in self.header.split('\\n'):\n                output(('// ' + line).rstrip() + '\\n')\n            output('\\n')\n\n        # Version\n        output('OPENQASM 2.0;\\n')\n        output('include \"qelib1.inc\";\\n')\n        output_line_gap(2)\n\n        # Function definitions\n        # None yet\n\n        # Register definitions\n        # Qubit registers\n        output(f\"// Qubits: [{', '.join(map(str, self.qubits))}]\\n\")\n        if len(self.qubits) > 0:\n            output(f'qreg q[{len(self.qubits)}];\\n')\n        # Classical registers\n        # Pick an id for the creg that will store each measurement\n        already_output_keys: Set[str] = set()\n        for meas in self.measurements:\n            key = protocols.measurement_key_name(meas)\n            if key in already_output_keys:\n                continue\n            already_output_keys.add(key)\n            meas_id = self.args.meas_key_id_map[key]\n            comment = self.meas_comments[key]\n            if comment is None:\n                output(f'creg {meas_id}[{len(meas.qubits)}];\\n')\n            else:\n                output(f'creg {meas_id}[{len(meas.qubits)}];  // Measurement: {comment}\\n')\n        output_line_gap(2)\n\n        # Operations\n        self._write_operations(self.operations, output, output_line_gap)\n\n    def _write_operations(\n        self,\n        op_tree: 'cirq.OP_TREE',\n        output: Callable[[str], None],\n        output_line_gap: Callable[[int], None],\n    ) -> None:\n        def keep(op: 'cirq.Operation') -> bool:\n            return protocols.qasm(op, args=self.args, default=None) is not None\n\n        def fallback(op):\n            if len(op.qubits) not in [1, 2]:\n                return NotImplemented\n\n            mat = protocols.unitary(op, None)\n            if mat is None:\n                return NotImplemented\n\n            if len(op.qubits) == 1:\n                return QasmUGate.from_matrix(mat).on(*op.qubits)\n            return QasmTwoQubitGate.from_matrix(mat).on(*op.qubits)\n\n        def on_stuck(bad_op):\n            return ValueError(f'Cannot output operation as QASM: {bad_op!r}')\n\n        for main_op in ops.flatten_op_tree(op_tree):\n            decomposed = protocols.decompose(\n                main_op, keep=keep, fallback_decomposer=fallback, on_stuck_raise=on_stuck\n            )\n\n            qasms = [protocols.qasm(op, args=self.args) for op in decomposed]\n\n            should_annotate = decomposed != [main_op] or qasms[0].count('\\n') > 1\n            if should_annotate:\n                output_line_gap(1)\n                if isinstance(main_op, ops.GateOperation):\n                    x = str(main_op.gate).replace('\\n', '\\n //')\n                    output(f'// Gate: {x!s}\\n')\n                else:\n                    x = str(main_op).replace('\\n', '\\n //')\n                    output(f'// Operation: {x!s}\\n')\n\n            for qasm in qasms:\n                output(qasm)\n\n            if should_annotate:\n                output_line_gap(1)\n","repo_name":"quantumlib/Cirq","sub_path":"cirq-core/cirq/circuits/qasm_output.py","file_name":"qasm_output.py","file_ext":"py","file_size_in_byte":11097,"program_lang":"python","lang":"en","doc_type":"code","stars":3974,"dataset":"github-code","pt":"77"}
+{"seq_id":"16304051689","text":"# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport chromedriver_binary\n\noptions = Options()\n# ヘッドレスモード（Linux上で動かすとき必ずこのモードにしておく）\noptions.add_argument('--headless')\noptions.add_argument('--no-sandbox')\noptions.add_experimental_option('excludeSwitches', ['enable-logging'])\ndriver = webdriver.Chrome(options=options)\ndriver.get('https://example.com')\nprint(driver.current_url)\n\ndriver.close()\n","repo_name":"yoshi-kin/test_actions","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42523578577","text":"from unicodedata import name\nfrom django.shortcuts import render\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.views.generic import ListView\nfrom .models import Pokemon\n\ndef index(request):\n    '''\n    home page \n    '''\n    num_pokemon = Pokemon.objects.all().count()\n    type_count = Pokemon.objects.values('type1').annotate(n=models.Count('type1')).order_by('-n')\n\n    context = {\n        'num_pokemon': num_pokemon,\n        'type_count': type_count,\n    }\n    \n    return render(request, 'dex/index.html', context=context)\n\ndef typeListView(request, _type):\n    '''\n    view to display Pokemon having a specific primary type\n    '''\n    pokemon = Pokemon.objects.filter(type1=_type.capitalize())\n    p_type = _type # can't use underscore var's in templates\n    context = {\n        'pokemon': pokemon,\n        'p_type': p_type,\n    }\n\n    def get_absolute_url(self):\n    \n        return reverse(\"type_list\", kwargs={'_type': self._type})\n    \n    return render(request, 'dex/type.html', context=context)\n\ndef pokeDetailView(request, pname):\n    '''\n    view to display detailed info on a particular Pokemon\n    '''\n    annoying_url_names = ['mr-mime', 'nidoran-female', 'nidoran-male', 'farfetchd']\n    if pname in annoying_url_names:\n        p = Pokemon.objects.get(name=pname)\n    else:\n        p = Pokemon.objects.get(name=pname.capitalize())\n\n    num = p.number\n    if num == 1:\n        prev = Pokemon.objects.get(number=151)\n    else:\n        prev = Pokemon.objects.get(number=num-1)\n    if num == 151:\n        next = Pokemon.objects.get(number=1)\n    else:\n        next = Pokemon.objects.get(number=num+1)\n\n    context = {\n        'p': p,\n        'prev': prev,\n        'next': next,\n    }\n\n    def get_absolute_url(self):\n    \n        return reverse(\"poke_detail\", kwargs={'pname': self.pname})\n\n    return render(request, 'dex/detail.html', context=context)\n\ndef searchResultsView(request):\n    '''    \n    view to display search results of pokemon names\n    '''\n    if request.method == \"POST\":\n        \n        query = request.POST['query']\n        poke_list = Pokemon.objects.filter(name__icontains=(query))\n\n        context = {\n            'query': query,\n            'poke_list': poke_list\n        }\n\n        return render(request, 'dex/search_results.html', context=context) \n    else:\n        context = {\n\n        }\n\n        return render(request, 'dex/search_results.html', context=context) \n\ndef allView(request):\n    '''\n    view to display all original 151 Pokemon\n    '''\n    pokemon = Pokemon.objects.all()\n    context = {\n        'pokemon': pokemon\n    }\n    \n    return render(request, 'dex/all.html', context=context)\n","repo_name":"ghbhc/PokeSite","sub_path":"dex/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33674882643","text":"import asyncio\nimport argparse\nfrom rtpTTML import TTMLReceiver\n\n\nclass Receiver:\n    def __init__(self, port: int, encoding: str) -> None:\n        self.receiver = TTMLReceiver(port, self.processDoc, encoding=encoding)\n\n    def processDoc(self, doc: str, timestamp: int) -> None:\n        print(\"{}\\n\".format(doc))\n\n    def stop(self) -> None:\n        self.receiver.async_close()\n\n    async def run(self) -> None:\n        await self.receiver.async_run()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description='Example TTML RTP receiver.')\n    parser.add_argument(\n        '-p',\n        '--port',\n        type=int,\n        help='receiver port',\n        required=True)\n    parser.add_argument(\n        '-e',\n        '--encoding',\n        type=str,\n        default=\"UTF-8\",\n        help='Character encoding of document. One of UTF-8, UTF-16, UTF-16LE, '\n             'and UTF-16BE (default: UTF-8)',\n        required=False)\n    args = parser.parse_args()\n\n    rx = Receiver(args.port, encoding=args.encoding)\n\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(rx.run())\n    try:\n        loop.run_forever()\n    except KeyboardInterrupt:\n        pass\n    rx.stop()\n    loop.close()\n","repo_name":"bbc/rd-apmm-python-lib-rtpTTML","sub_path":"examples/exampleRX.py","file_name":"exampleRX.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"43504971275","text":"import gym\nimport os\nimport numpy as np\nimport cv2\nimport math\nimport copy\nimport random\nimport torch\nimport torchvision\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nimport torch.nn.functional as F\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.mixture import GaussianMixture\nimport Helpers.Helper_Functions as hf\n\nclass GetRepresentationVisualisation():\n    def __init__(self, args, actor_critic):\n        self.args = args\n        self.device = torch.device(self.args.gpu if torch.cuda.is_available() else \"cpu\")\n        torch.cuda.set_device(self.args.gpu)\n\n        self.ppo_net = actor_critic(3, self.args.num_acts, self.args.width).to(self.device)\n        self.ppo_net.eval()\n        self.exp_name = self.args.env_name + \"_PPO_\" + self.args.save_name + str(self.args.num_levels) + \"_\" +\\\n                        self.args.dist_mode + \"_\" + str(int(self.args.max_frames//1e6)) + \"M_\" + str(self.args.exp_indx)\n        self.save_path = os.path.join(self.args.save_dir + \"/Models\", self.exp_name + \".pt\")\n        self.save_data_path = os.path.join(self.args.save_dir + \"/Data\", self.exp_name)\n\n        self.level_number_logger = []\n        self.states_logger = []\n        self.state_representations_logger = []\n        self.actions_logger = []\n        self.rewards_logger = []\n        self.values_logger = []\n        self.train_test_logger = []\n        self.tsne_embedding = []\n        self.terminal_states = []\n        self.number_of_steps = []\n        self.masks_logger = []\n\n        self.frame_indx = 0\n\n        self.load_checkpoint()\n\n        print(self.ppo_net.mean_offset)\n\n    def load_checkpoint(self):\n        print(self.save_path)\n        if os.path.isfile(self.save_path):\n            # load checkpoint\n            check_point = torch.load(self.save_path)\n            self.ppo_net.load_state_dict(check_point['agent_state_dict'])\n            self.frame_indx = check_point['frames']\n            # self.mean_error = check_point['mean_error']\n\n            print(\"Checkpoint loaded, starting from epoch:\", self.frame_indx)\n        else:\n            # Raise Error if it does not exist\n            raise ValueError(\"Checkpoint Does not exist\")\n\n    def reset_data(self):\n        self.level_number_logger = []\n        self.states_logger = []\n        self.state_representations_logger = []\n        self.actions_logger = []\n        self.rewards_logger = []\n        self.values_logger = []\n        self.train_test_logger = []\n        self.tsne_embedding = []\n        self.terminal_states = []\n        self.number_of_steps = []\n        self.masks_logger = []\n\n    def get_data(self, start_test_level, max_levels=10, test_levels=True):\n        print(\"Collecting data\")\n        for i in range(min(max_levels, self.args.num_levels)):\n            env = gym.make(\"procgen:procgen-\" + self.args.env_name + \"-v0\", start_level=i, num_levels=1,\n                           distribution_mode=self.args.dist_mode)\n            self.collect_rollout(env, i)\n            print(\"Level %d\" % i)\n\n        if test_levels:\n            for i in range(min(max_levels, self.args.num_levels)):\n                env = gym.make(\"procgen:procgen-\" + self.args.env_name + \"-v0\",\n                               start_level=start_test_level + i,\n                               num_levels=1,\n                               distribution_mode=self.args.dist_mode)\n                self.collect_rollout(env, i+min(max_levels, self.args.num_levels), 1)\n                print(\"Level %d\" % i)\n\n        self.state_representations_logger = torch.cat(self.state_representations_logger).numpy()\n        self.states_logger = torch.cat(self.states_logger)\n        self.terminal_states = torch.cat(self.terminal_states)\n\n    def collect_rollout(self, env, level_num, train_test=0, random_acts=False):\n        start_state = env.reset()\n        # print(env.unwrapped.env.env.combos)\n        state = hf.state_to_tensor(start_state, self.device)\n        self.ppo_net.eval()\n\n        step = 0\n        done = False\n        with torch.no_grad():\n            while not done:\n\n                dist, value = self.ppo_net(state)  # Forward pass of actor-critic model\n                # action = dist.sample().argmax(1)\n                action = dist.sample()  # Sample action from distribution\n                next_state, reward, done, env_info = env.step(action.item())\n\n                # Work out reward, we are just setting the reward to be either\n                # -1, 1 or 0 for negative, positive or no reward\n                ppo_reward = torch.FloatTensor([((reward > 0).astype(\"float64\") - (reward < 0).astype(\"float64\"))])\n                ppo_reward = ppo_reward.unsqueeze(1)\n\n                self.level_number_logger.append(level_num)\n                self.states_logger.append(state.cpu())\n                self.state_representations_logger.append(self.ppo_net.state.cpu().reshape(1, -1))\n                self.actions_logger.append(action.cpu())\n                self.rewards_logger.append(ppo_reward)\n                self.values_logger.append(value.cpu())\n                self.train_test_logger.append(train_test)\n\n                state = hf.state_to_tensor(next_state, self.device)\n                current_mask = torch.FloatTensor([1 - done]).unsqueeze(1)\n                self.masks_logger.append(current_mask)\n\n                step += 1\n            self.terminal_states.append(state.cpu())\n            self.number_of_steps.append(step)\n\n    def get_tsne(self):\n        print(\"Performing TNSE\")\n        if len(self.state_representations_logger) > 0:\n            if self.state_representations_logger.shape[1] > 2:\n                if self.state_representations_logger.shape[1] > 50:\n                    pca = PCA(n_components=50)\n                    pca.fit(self.state_representations_logger)\n                    reps_50d = pca.transform(self.state_representations_logger)\n                    self.tsne_embedding = TSNE(n_components=2).fit_transform(reps_50d)\n                else:\n                    self.tsne_embedding = TSNE(n_components=2).fit_transform(self.state_representations_logger)\n            else:\n                self.tsne_embedding = self.state_representations_logger\n        else:\n            print(\"No data!\")\n            return None\n\n    def onclick(self, event):\n        print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n              ('double' if event.dblclick else 'single', event.button,\n               event.x, event.y, event.xdata, event.ydata))\n\n        point = np.expand_dims(np.array([event.xdata, event.ydata]), axis = 0)\n        point_indexs = self.find_nearest(point)\n        self.get_state_figure(point_indexs)\n        if self.args.attention:\n            self.get_attention_figure(point_indexs)\n\n    def gaussian_mixture(self, centers=40):\n        gaussian_mixture = GaussianMixture(n_components=centers)\n        cluster_labels = gaussian_mixture.fit_predict(self.state_representations_logger)\n        cluster_gen = 0\n        for i in range(centers):\n            sum_train = np.sum(np.array(self.train_test_logger)[cluster_labels == i])\n            cluster_gen += np.abs((sum_train / np.sum(cluster_labels == i)) - 0.5) * 2\n        print(\"Cluster Generalization Ratio:\", cluster_gen/centers)\n\n        # print(cluster_labels.shape)\n        # plt.scatter(self.tsne_embedding[:, 0], self.tsne_embedding[:, 1], c=cluster_labels, cmap=\"tab20\")\n        # print(cluster_labels)\n        # plt.show()\n\n    def create_plots(self):\n        print(\"Plotting\")\n        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True, sharey=True)\n        fig.subplots_adjust(hspace=0, wspace=0)\n        ax1.scatter(self.tsne_embedding[:, 0], self.tsne_embedding[:, 1], c=self.level_number_logger, cmap=\"tab20\")\n        ax1.set_title(\"Level Distribution\")\n        ax1.set_xticks([])\n        ax1.set_yticks([])\n        ###############################################################################################################\n        ax2.scatter(self.tsne_embedding[:, 0], self.tsne_embedding[:, 1], c=self.actions_logger, cmap=\"tab20\")\n        ax2.set_title(\"Action Distribution\")\n        ax2.set_xticks([])\n        ax2.set_yticks([])\n        ###############################################################################################################\n        scatter = ax3.scatter(self.tsne_embedding[:, 0], self.tsne_embedding[:, 1], c=torch.cat(self.values_logger).numpy())\n        ax3.set_title(\"Value Distribution\", y=-0.1)\n        cbaxes = inset_axes(ax3, width=\"30%\", height=\"3%\", loc=2)\n        fig.colorbar(scatter, cax=cbaxes, ax=ax3, orientation=\"horizontal\", shrink=0.3)\n        ax3.set_xticks([])\n        ax3.set_yticks([])\n        ###############################################################################################################\n        train_levels = np.array(self.train_test_logger) == 0\n        ax4.scatter(self.tsne_embedding[:, 0][train_levels], self.tsne_embedding[:, 1][train_levels],\n                              label=\"Train\")\n\n        test_levels = np.array(self.train_test_logger) == 1\n        ax4.scatter(self.tsne_embedding[:, 0][test_levels], self.tsne_embedding[:, 1][test_levels],\n                              label=\"Test\")\n        ax4.set_title(\"Test-Train Level Distribution\", y=-0.1)\n        ax4.legend(loc='upper left', numpoints=1, ncol=3, fontsize=8)\n        ax4.set_xticks([])\n        ax4.set_yticks([])\n        ###############################################################################################################\n        fig.set_size_inches(18.5, 10.5)\n\n        cid = fig.canvas.mpl_connect('button_press_event', self.onclick)\n        plt.show()\n        fig.savefig(\"Plots/\" + self.exp_name + '_plot.png')\n\n    def find_nearest(self, x_y, n=25):\n        if len(self.tsne_embedding) > 0:\n            closest_point = np.argsort(np.sum(np.abs((self.tsne_embedding - x_y)), 1))[0]\n            closest_representation = self.state_representations_logger[closest_point, :]\n            closest_points = np.argsort(np.sum(np.abs((self.state_representations_logger - closest_representation)), 1))[:n]\n            return closest_points\n        else:\n            print(\"No Data!\")\n            return None\n\n    def get_state_figure(self, state_indices):\n        top_n_states = self.states_logger[state_indices, :3]\n        image_grid = torchvision.utils.make_grid(top_n_states, 5)\n        image_grid = image_grid.numpy().transpose((1, 2, 0))\n        fig = plt.figure(figsize=(10, 10))\n        plt.imshow(image_grid)\n        plt.show()\n        plt.savefig(\"Top_\" + str(len(state_indices)) +\".png\")\n\n    def terminal_state_figure(self):\n        image_grid = torchvision.utils.make_grid(self.terminal_states, 5)\n        image_grid = image_grid.numpy().transpose((1, 2, 0))\n        fig = plt.figure(figsize=(10, 10))\n        plt.imshow(image_grid)\n        plt.show()\n        plt.savefig(\"Terminal_states.png\")\n\n    def plot_values(self):\n        # returns = hf.compute_gae(next_value, rewards, masks, values)\n\n        returns = hf.compute_returns(self.rewards_logger, self.masks_logger, gamma=self.args.ppo_gamma)\n        returns = torch.cat(list(returns)).numpy()\n\n        fig = plt.figure(figsize=(20, 10))\n        plt.plot(torch.cat(self.values_logger).numpy())\n        plt.plot(returns)\n\n        plt.plot(1 - np.array(torch.cat(self.masks_logger)), c=\"k\", linewidth=4)\n\n        plt.legend([\"Value\", \"Basic Returns\"])\n        plt.savefig(\"Plots/\" + self.exp_name + \"_Values.png\")\n        plt.show()\n\n    def plot_rewards(self):\n        fig = plt.figure(figsize=(20, 10))\n\n        plt.plot(self.rewards_logger)\n        plt.plot(self.masks_logger, linestyle=\"-\")\n        plt.savefig(\"Plots/Rewards.png\")\n        plt.show()\n\n    def record_rollout(self):\n        self.reset_data()\n        \n        env = gym.make(\"procgen:procgen-\" + self.args.env_name + \"-v0\", start_level=500,\n                       num_levels=1, distribution_mode=self.args.dist_mode)\n        self.collect_rollout(env, 20, random_acts=False)\n\n        height = 64\n        width = 64\n        if self.args.attention:\n            UpNN = torch.nn.UpsamplingBilinear2d(scale_factor=8)\n            height *= 2\n\n        Video_name = \"Videos/test_video.avi\"\n        video = cv2.VideoWriter(Video_name, cv2.VideoWriter_fourcc(*\"MJPG\"), 15, (width, height))\n        print(len(self.states_logger))\n        with torch.no_grad():\n            for i in range(len(self.states_logger)):\n                img = self.states_logger[i][0, :3].cpu().numpy()\n                if self.args.attention:\n                    atten_mask = UpNN(self.attention_maps_logger[i])[0].cpu().numpy()\n                    masked_img = img * atten_mask\n                    img = np.concatenate((img, masked_img), 1)\n\n                image = (img.transpose((1, 2, 0)) * 255).astype(np.uint8)\n                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n                video.write(image)\n            video.release()\n","repo_name":"LukeDitria/PPO_procgen","sub_path":"Visualise_jupyter.py","file_name":"Visualise_jupyter.py","file_ext":"py","file_size_in_byte":13039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74606588727","text":"# -*- coding: utf-8 -*-\n\n# Run this app with `python app.py` and\n# visit http://127.0.0.1:8050/ in your web browser.\n\nimport dash\nfrom dash.dependencies import Input, Output, State\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport numpy as np\nimport pandas as pd\nimport os\nimport sys\nimport argparse\nimport configparser\n\nimport datajoint as dj\nfrom rg_pipeline.run import set_config\nfrom rg_pipeline import compute_utils as cpt_uils\nfrom rg_visual import plot_utils\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css', 'https://codepen.io/chriddyp/pen/brPBPO.css']\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\ndef get_session_options()->list:\n    \"\"\"\n    Get session options for the session dropdown list\n    \"\"\"\n    from rg_pipeline.ingest.experiment import Session, Subject, Stimulation, SpikeGroup, Spike\n    session = Session()\n    subject = Subject()\n    query = session * subject\n    sessions = query.fetch(as_dict=True)\n    session_options = []\n    for session in sessions:\n        session_options.append({\n            'label':'Subject: {} | Sample Number: {} | Session Date: {}'.format(\n                session['subject_name'], \n                session['sample_number'], \n                session['session_date']\n            ), \n            'value':session['session_id']\n        })\n    return session_options\n\napp.layout = html.Div(children=[\"Loading\"])\ndef get_layout():\n    session_options = get_session_options()\n    layout = html.Div(children=[\n        html.H1(children='Retinal Ganglion Cell Experiment Visualization - Drew Yang'),\n        dcc.Dropdown(\n            id='session-dropdown',\n            options=session_options,\n            placeholder='Select a session'\n        ),\n        dcc.Dropdown(\n            id='stimulation-dropdown',\n            options=[],\n            placeholder='Select a stimulation'\n        ),\n        dcc.Dropdown(\n            id='spike-group-dropdown',\n            options=[],\n            placeholder='Select a spike group'\n        ),\n        dcc.Dropdown(\n            id='spike-dropdown',\n            options=[],\n            placeholder='Select a spike'\n        ),\n        dcc.Input(id='delay-input', type='number', min=1, placeholder='Number of frames that are previously from the selected spike', style={'width':'500px'}),\n        html.P(id='warning', children='', style={'color':'orange'}),\n        html.P(id='error', children='', style={'color':'red'}),\n        dcc.Graph(\n            id='spike-time-graph',\n            figure=go.Figure()\n        ),\n        dcc.Graph(\n            id='sta-graph',\n            figure=go.Figure()\n        )\n    ])\n    return layout\n\n\n\n@app.callback(\n    [\n        Output(\"stimulation-dropdown\", \"options\"),\n        Output(\"stimulation-dropdown\", \"value\")\n    ],\n    [\n        Input(\"session-dropdown\", \"value\")\n    ],\n)\ndef update_stimulation_options(session_value):\n    from rg_pipeline.ingest.experiment import Session, Subject, Stimulation, SpikeGroup, Spike\n    if session_value:\n        stimulation_options = []\n        stimulation = Stimulation()\n        session = Session()\n        query =  stimulation * session & 'session_id = \"{}\"'.format(session_value)\n        stimulations = query.fetch('stimulation_id', 'fps', 'pixel_size', 'stim_height', 'stim_width', 'stimulus_onset', 'x_block_size', 'y_block_size', as_dict=True)\n        for stimulation in stimulations:\n            stimulation_options.append({\n                'label':'FPS: {} | Pixel Size/px: {} | Onset/s: {} | Stimulus Size/blocks {}w * {}h | Block Size/px: {}w * {}h'.format(\n                    stimulation['fps'],\n                    stimulation['pixel_size'],\n                    stimulation['stimulus_onset'],\n                    stimulation['stim_width'],stimulation['stim_height'],\n                    stimulation['x_block_size'],stimulation['y_block_size']\n                ),\n                'value':stimulation['stimulation_id'],\n            })\n\n        return stimulation_options, ''\n    return [], ''\n\n@app.callback(\n    [\n        Output(\"spike-group-dropdown\", \"options\"),\n        Output(\"spike-group-dropdown\", \"value\")\n    ],\n    [\n        Input(\"stimulation-dropdown\", \"value\")\n    ],\n)\ndef update_spike_group_options(stimulation_value):\n    from rg_pipeline.ingest.experiment import Session, Subject, Stimulation, SpikeGroup, Spike\n    if stimulation_value:\n        spike_group_options = []\n        spike_group = SpikeGroup()\n        stimulation = Stimulation()\n        query =  spike_group * stimulation & 'stimulation_id = \"{}\"'.format(stimulation_value)\n        spike_groups = query.fetch(as_dict=True)\n        for group in spike_groups:\n            spike_group_options.append({\n                'label':'Spike Group ID: {}'.format(\n                    group['spike_group_id']\n                ),\n                'value':group['spike_group_id'],\n            })\n\n        return spike_group_options, ''\n    return [], ''\n\n@app.callback(\n    [\n        Output(\"spike-dropdown\", \"options\"),\n        Output(\"spike-dropdown\", \"value\")\n    ],\n    [\n        Input(\"spike-group-dropdown\", \"value\")\n    ],\n)\ndef update_spike_options(spike_group_value):\n    from rg_pipeline.ingest.experiment import Session, Subject, Stimulation, SpikeGroup, Spike\n    if spike_group_value:\n        spike_options = []\n        spike = Spike()\n        spike_group = SpikeGroup()\n        query =  spike * spike_group & 'spike_group_id = \"{}\"'.format(spike_group_value)\n        spikes = query.fetch('spike_id', 'spike_time', 'spike_movie_time', as_dict=True)\n        for spike in spikes:\n            spike_options.append({\n                'label':'Spike Time: {} | Spike Movie Time: {}'.format(\n                    spike['spike_time'],\n                    spike['spike_movie_time']\n                ),\n                'value':spike['spike_id'],\n            })\n\n        return spike_options, ''\n    return [], ''\n\n@app.callback(\n    [\n        Output(\"spike-time-graph\", \"figure\"),\n        Output(\"sta-graph\", \"figure\"),\n        Output(\"error\", \"children\"),\n        Output(\"warning\", \"children\")\n    ],\n    [\n        Input(\"spike-dropdown\", \"value\"),\n        Input(\"delay-input\", \"value\")\n    ],\n    [\n        State(\"spike-group-dropdown\", \"value\"),\n        State(\"stimulation-dropdown\", \"value\"),\n        State(\"session-dropdown\", \"value\"),\n        State(\"delay-input\", \"value\")\n    ]\n)\ndef update_spike_time(spike_value, delay_value, spike_group_value, stimulation_value, session_value, n_delays):\n    from rg_pipeline.ingest.experiment import Session, Subject, Stimulation, SpikeGroup, Spike\n    # from rg_visual import plot_utils\n    if spike_value:\n        stimulation = Stimulation()\n        stimulation_query = stimulation & 'stimulation_id = \"{}\"'.format(stimulation_value)\n        selected_stimulation = stimulation_query.fetch(as_dict=True)[0]\n        spike = Spike()\n        spike_query = spike & 'spike_id = \"{}\"'.format(spike_value)\n        selected_spike = spike_query.fetch(as_dict=True)[0]\n\n        warning_msg = \"\"\n        error_msg = \"\"\n\n        n_frames = cpt_uils.get_frame_idx(selected_spike['spike_movie_time'], selected_stimulation['fps'])\n        movie = np.frombuffer(selected_stimulation['movie'])\n        movie = eval(\"movie.reshape{}\".format(selected_stimulation['movie_shape']))\n        frame_2d = cpt_uils.get_frame_2darray(selected_stimulation, movie, n_frames) \n        if frame_2d is not None:\n            frame_fig = plot_utils.plot_frame(n_frames, frame_2d)\n        else:\n            error_msg = \"Error: Frame is not available, maybe spike shows after the movie ends.\"\n            return go.Figure(), go.Figure(), error_msg, warning_msg\n        \n        if n_delays is not None:\n            if n_delays<2:\n                warning_msg = \"Warning: Quickly input multiple digits from the keyboard needs to be tuned. Correct it by clicking increase/decrease after inputting or input slowly\"\n\n            sta = cpt_uils.get_sta(selected_stimulation, movie, selected_spike['spike_movie_time'], n_delays)\n            if sta is not None:\n                sta_fig = plot_utils.plot_sta(n_frames, n_delays, sta)\n            else:\n                sta_fig = go.Figure()\n                error_msg = \"Error: STA plot is not available since Spike Movie Time is less than 0 or not enough frames to calculate.\"\n        else:\n            sta_fig = go.Figure()\n            error_msg = \"Error: STA plot is require to set a number of frames that are previously from the selected spike.\"\n\n        return frame_fig, sta_fig, error_msg, warning_msg\n    return go.Figure(), go.Figure(), \"\", \"\"\n\ndef main(args):\n    if args.config and os.path.exists(args.config):\n        config = configparser.ConfigParser()\n        config.read(args.config)\n        set_config(config['DEFAULT']['database'], config['DEFAULT']['user'], config['DEFAULT']['pwd'])\n    else:\n        set_config(args.database, args.user, args.pwd)\n    app.layout = get_layout\n    app.run_server(debug=True, host='0.0.0.0')\n\ndef parse_args_for_main(app_home=os.path.realpath(__file__+'/../..')):\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', '--config', default=None, help='DataJoint database config file path')\n    parser.add_argument('-db', '--database', default=None, help='DataJoint database host')\n    parser.add_argument('-u', '--user', default=None, help='DataJoint database user')\n    parser.add_argument('-p', '--pwd', default=None, help='DataJoint database password')\n    args = parser.parse_args()\n    main(args)\n\nif __name__ == '__main__':\n    parse_args_for_main(app_home=os.path.realpath(__file__+'/../..'))","repo_name":"yambottle/datajoint-retinal-ganglion-pipeline","sub_path":"visualization/rg_visual/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8346254329","text":"# QCalendarWidget -- 日历\n\"\"\"\nQCalendarWidget常用api：\n1、setMaximumDate()、setMinimumDate() -- 设置日历显示的最小日期和最大日期\n2、setDateRange() -- 设置日历的日期范围\n3、maximumDate()、minimumDate() -- 返回日历的最小日期和最大日期\n4、selectedDate() -- 返回当前用户选择的日期\n5、setDateEditEnabled() -- 让日历支持输入，当日历控件获得焦点时，按非修饰键可弹出一个小型输入框，通过手动输入的方式选择日期（此项默认为True）\n6、setDateEditAcceptDelay() -- 设置日历输入时的等待时间（单位是毫秒）\n7、setFirstDayOfWeek() -- 设置一周里的第一天；其值为：Qt.DayOfWeek.Monday~Qt.DayOfWeek.Sunday\n8、setSelectionMode() -- 设置选择模式，有两个值：QCalendarWidget.SingleSelection（可选择）、QCalendarWidget.NoSelection（不可选择）\n9、setSelectedDate() -- 设置日历初始日期\n10、monthShown()、yearShown() --  返回当前选择的月份和年份\n11、setGridVisible() -- 设置日历网格是否可见\n12、setNavigationBarVisible() -- 设置顶部导航是否可见\n13、showNextMonth()、showNextYear() -- 分别使日历导航栏中的月份和年份往前进一位\n14、showPreviousMonth()、showPreviousYear() -- 分别使日历导航栏中的月份和年份往后退一位\n\n15、setHorizontalHeaderFormat() -- 设置日历水平标题的格式，有四个值：\n    QCalendarWidget.SingleLetterDayNames：标题显示日期名称的单字母缩写(例如，星期一为M)\n    QCalendarWidget.ShortDayNames：标题显示日期名称的简短缩写(例如，星期一为周一)\n    QCalendarWidget.LongDayNames：标题显示完整的日期名称(例如星期一)\n    QCalendarWidget.NoHorizontalHeader：标头是隐藏的\n\n16、setVerticalHeaderFormat() -- 设置日历垂直标题的格式，有两个值：\n    QCalendarWidget.ISOWeekNumbers：标头显示ISO周编号\n    QCalendarWidget.NoVerticalHeader：标头是隐藏的\n\n\nQCalendarWidget常用信号：\n1、clicked -- 在控件中用鼠标单击选择日期时触发\n2、activated -- 通过按键盘的Enter键选中日期时触发\n3、currentPageChanged -- 在导航栏中切换了年份或者月份时触发\n4、selectionChanged -- 在控件中通过键盘的上下左右按钮切换日期时触发\n\"\"\"\nimport sys\nfrom PySide6.QtWidgets import *\nfrom PySide6.QtGui import *\nfrom PySide6.QtCore import *\n\n\nclass Window(QWidget):\n    def __init__(self):\n        super(Window, self).__init__()\n        self.window_ui()\n\n    def window_ui(self):\n        calendar = QCalendarWidget()\n        box = QHBoxLayout()\n        box.addWidget(calendar)\n        self.setLayout(box)\n\n        calendar.setDateRange(QDate(2001, 1, 1), QDate(2035, 12, 31))\n        # calendar.setDateEditAcceptDelay(10000)\n        # calendar.setDateEditEnabled(False)\n        # calendar.setSelectionMode(QCalendarWidget.NoSelection)\n        calendar.setNavigationBarVisible(True)\n        # calendar.setHorizontalHeaderFormat(QCalendarWidget.SingleLetterDayNames)\n\n        def do_something():\n            print(calendar.selectedDate())\n\n        calendar.clicked.connect(do_something)\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    window = Window()\n    window.show()\n    sys.exit(app.exec())\n","repo_name":"zhanghefan123/pyside6","sub_path":"43-日历控件QCalendarWidget.py","file_name":"43-日历控件QCalendarWidget.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3493031648","text":"def mayor(n1,n2):\r\n    \r\n    if(n1>n2):\r\n        print(\"El numero mayor es: \",n1)\r\n    elif(n1<n2):\r\n        print(\"El numero mayor es: \",n2)\r\n    else:\r\n        print(\"Hay algun error con tus números\")\r\n\r\n      \r\ndef menor(n1,n2):\r\n    \r\n    if(n1>n2):\r\n        print(\"El numero menor es: \",n2)\r\n    elif(n1<n2):\r\n        print(\"El numero menor es: \",n1)\r\n    else:\r\n        print(\"Hay algun error con tus números\")\r\n\r\ndef menu(funcion):\r\n    if(funcion=='1'):\r\n        print(\"-------MAYOR--------\")\r\n        n1=input(\"Ingrese el primer numero a comprar: \")\r\n        n2=input(\"Ingrese el segundo numero a comprar: \")\r\n        mayor(n1,n2)\r\n    elif (funcion=='2'):    \r\n        print(\"-------MENOR--------\")    \r\n        n1=input(\"Ingrese el primer numero a comprar: \")\r\n        n2=input(\"Ingrese el segundo numero a comprar: \")\r\n        menor(n1,n2)\r\n    else:\r\n        print(\"Algo hiciste mal\")\r\n\r\n\r\nprint(\"MENU DE FUNCIONES\")\r\nprint(\"1) Saber mayor\")\r\nprint(\"2) Saber menor\\n\")\r\nfuncion=input(\"Ingrese lo que desea saber:\\n\")\r\nmenu(funcion)\r\n\r\n","repo_name":"Jaimeibarrae/ejerciciosSegundaunidad","sub_path":"JACI_mayor_menor.py","file_name":"JACI_mayor_menor.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37051171648","text":"import re\n\nnice1 = re.compile(\"(..).*\\\\1\")\nnice2 = re.compile(\"(.).\\\\1\")\n\nfh = open('5.txt', 'r')\nnice = 0\nfor line in fh.readlines():\n  line = line.strip()\n  if nice1.search(line) and nice2.search(line):\n    nice += 1\n\nprint(nice)\n\n\n","repo_name":"daniel-kullmann/advent-of-code","sub_path":"5a.py","file_name":"5a.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30295734299","text":"import tcod\n\nfrom game_messages import Message\nfrom components.ai import ConfusedMonster\nfrom utils import disk\nfrom effect import GFX_Effect\n\n\ndef heal(*args, **kwargs):\n    entity = args[0]\n    amount = kwargs.get('amount')\n\n    results = []\n\n    if entity.fighter.current_hp == entity.fighter.max_hp:\n        results.append({\n            'consumed':\n            False,\n            'message':\n            Message('You are already at full health', \"yellow\")\n        })\n    else:\n        entity.fighter.heal(amount)\n        results.append({\n            'consumed':\n            True,\n            'message':\n            Message('Your wounds start to feel better!', \"green\")\n        })\n\n    return results\n\n\ndef cast_chaos_bolt(*args, **kwargs):\n    caster = args[0]\n    entities = kwargs.get(\"entities\")\n    game_map = kwargs.get(\"game_map\")\n    damage = kwargs.get(\"damage\")\n    maximum_range = kwargs.get(\"maximum_range\")\n\n    results = []\n\n    target = None\n    closest_distance = maximum_range + 1\n\n    for entity in entities:\n        if entity.fighter and entity != caster and game_map.fov[entity.\n                                                                x, entity.y]:\n            distance = caster.distance_to(entity)\n\n            if distance < closest_distance:\n                target = entity\n                closest_distance = distance\n\n    if target:\n        results.append({\n            \"consumed\":\n            True,\n            \"target\":\n            target,\n            \"message\":\n            Message(\n                f\"A bolt of entropic energies strikes the {target.name} for {damage} damage!\",\n                \"crimson\")\n        })\n        results.extend(target.fighter.take_damage(damage))\n        game_map.effects.append(\n            GFX_Effect(target.x, target.y, gfx_effect_tile=0x1009))\n    else:\n        results.append({\n            \"consumed\":\n            False,\n            \"target\":\n            None,\n            \"message\":\n            Message(\"There are no enemies in range.\", \"red\")\n        })\n\n    return results\n\n\ndef cast_fireball(*args, **kwargs):\n\n    entities = kwargs.get(\"entities\")\n    game_map = kwargs.get(\"game_map\")\n    damage = kwargs.get(\"damage\")\n    radius = kwargs.get(\"radius\")\n    target_x = kwargs.get(\"target_x\")\n    target_y = kwargs.get(\"target_y\")\n\n    results = []\n\n    if not game_map.fov[target_x, target_y]:\n        results.append({\n            \"consumed\":\n            False,\n            \"message\":\n            Message(\"You cannot target a tile outside your line of sight.\",\n                    \"yellow\")\n        })\n        return results\n\n    results.append({\n        \"consumed\":\n        True,\n        \"message\":\n        Message(\n            f\"The fireball explodes in roaring flames, burning everything within {radius} tiles!\"\n        )\n    })\n\n    target_area = disk(target_x, target_y, radius)\n\n    for entity in entities:\n        if entity.fighter and (entity.x, entity.y) in target_area:\n            results.append({\n                \"message\":\n                Message(\n                    f\"The {entity.name} is engulfed in flames and is burned for {damage} hit points!\",\n                    \"orange\")\n            })\n            results.extend(entity.fighter.take_damage(damage))\n\n    for map_coordinates in target_area:\n        game_map.effects.append(\n            GFX_Effect(map_coordinates[0],\n                       map_coordinates[1],\n                       gfx_effect_tile=0x1008))\n\n    return results\n\n\ndef cast_confuse(*args, **kwargs):\n    entities = kwargs.get(\"entities\")\n    game_map = kwargs.get(\"game_map\")\n    target_x = kwargs.get(\"target_x\")\n    target_y = kwargs.get(\"target_y\")\n\n    results = []\n\n    if not game_map.fov[target_x, target_y]:\n        results.append({\n            \"consumed\":\n            False,\n            \"message\":\n            Message(\"You cannot target a tile outside your line of sight.\",\n                    \"yellow\")\n        })\n        return results\n\n    for entity in entities:\n        if entity.x == target_x and entity.y == target_y and entity.ai:\n            confused_ai = ConfusedMonster(entity.ai, 10)\n\n            confused_ai.owner = entity\n            entity.ai = confused_ai\n\n            results.append({\n                \"consumed\":\n                True,\n                \"message\":\n                Message(\n                    f\"The eyes of the {entity.name} look vacant as it starts to stumble around!\",\n                    \"light green\")\n            })\n\n            break\n    else:\n        results.append({\n            \"consumed\":\n            False,\n            \"message\":\n            Message(\"There is no targetable enemy at that location.\", \"yellow\")\n        })\n\n    return results","repo_name":"AapoPitkanen/BLT-RL","sub_path":"item_functions.py","file_name":"item_functions.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30830777989","text":"\"\"\"\n@author: Bogdan Moskalik, \nDataScience, \nNiestacjonarne, \nGrupa 2\n\"\"\"\n\nfrom matplotlib import pyplot as plt\n\n\ndef quadratic_data(a, b, c):\n    xaxis = []\n    yaxis = []\n    for x in range(0, 100):\n        xaxis.append(x)\n        yaxis.append(a*(x**2) + b*x + c)\n        \n    return xaxis, yaxis\n\nx, y = quadratic_data(2, 5, 1)\nplt.plot(x, y)","repo_name":"moskalikbogdan/CDV","sub_path":"Python/Warsztaty/24.11/2019.11.24.z1.py","file_name":"2019.11.24.z1.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36792003644","text":"# Instillinger for spillet\n# Størrelsen på vinduet til spillet\nDISPLAY_RESOLUTION = (800, 600)\nPADDLE_SIZE = (20, 80)\n# Farger er i RGB \n# Her kan en evnt. teste RGB farger: https://www.w3schools.com/colors/colors_rgb.asp\nPADDLE_COLOR = (0, 255, 0)\n\n# Speed indikerer hvor mange pixler noe beveger seg.\nPADDLE_SPEED = 6\nBALL_INITIAL_SPEED = 5","repo_name":"hogt1/vg2_pygame","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18761280541","text":"\"\"\"\nCreated on ..., .. December 2021\n\n@author Eloi Schlegel\n\"\"\"\n\nimport time\nfrom datetime import datetime\nimport mercury\nimport threading\n\nimport os\n\nfrom mercury_functions import init_reader\n\npath_code = os.path.dirname(__file__)\npath_general = os.path.abspath(os.path.join(path_code, os.pardir))\nPath_data = os.path.join(path_general, \"data\")\nPath_save = os.path.join(Path_data, \"Live_eval\")\n\nstart = datetime.now()\n\nreader = init_reader()\n\nprint(\"\\n\\n ************** MAKE SURE BOTH PORT ARE CONNECTED ************** \")\ninput()\n\nclass states:\n    page = \"Menu\"   # Menu, Read, Settings\n    target = None\n    read = False    # True, False\n    save = False    # True, False\n    thread = True   # True, False\n\n\ndef settings(time, S_g):\n    print(\"\\n\\n\\n##### Settings #####\\n\")\n\n    print(\"Change the refresh time ? y/n\\n\")\n    if input() == \"y\":\n        print(\" * Refresh time [ms]:\")\n        try:\n            n_time = int(input()) #refresh time in ms\n        except:\n            print(\" *** Not a number! ***\\n\")\n    else:\n        n_time = time\n\n    print(\"Change the target ? y/n\\n\")\n    if input() == \"y\":\n\n        n_target = None\n\n        while n_target == None:\n            temp = reader.read()\n            try:\n                print(\"Is it the tag to track ?\\n\", temp[0],\"y/n\")\n            except:\n                print(\"No tag dedected, try again!\")\n                next\n\n            x = input()\n            if x == \"y\":\n                n_target = str(temp[0])[2:-1].encode() # encode the EPC to get a binary format\n            elif x == \"xx\":\n                print(\"No chosen target\\n\")\n                break\n    else:\n        n_target = S_g.target\n\n    print(\"Change the reading power ?\")\n    if input() == \"y\":\n        print(\" * Current Reading power: \", reader.get_read_powers())\n        \n        try:\n            print(\" * Choose an antenna ?\")\n            a = int(input())\n            print(\" * Choose a power between the following range ?\", reader.get_power_range())\n            b = int(input())\n            reader.set_read_powers([(a, b)])\n        except:\n            print(\" *** Settings: error - no changement occur ***\")\n\n    return n_time, n_target\n\n\ndef save_Data(d_ant1, d_ant2):\n    for element in d_ant1:\n        data[0].append(element)\n    for element in d_ant2:\n        data[1].append(element)\n    return data\n\n\ndef export_data(data, folder_name = None):\n\n    if data == None:\n        print(\"No data to export\")\n        return\n\n    # Create folder if needed\n    if folder_name == None:\n        folder = input(\"Name the folder:\" )\n    else:\n        folder = folder_name\n\n    path_exp = os.path.join(Path_save, \"test\")\n    path = os.path.join(Path_save, folder)\n    try:\n        os.mkdir(path)\n    except:\n        print(\"*** Error: Folder already exists or Folder not created\")\n\n    # Save the data\n    try:\n        textfile = open(path+\"/ant1.txt\", \"w\")\n        for element in data[0]:\n            textfile.write(str(element[0])+ \",\" + str(element[1]) + \",\" + str(element[2]) + \",\" + str(element[3]) + \"\\n\")\n        textfile.close()\n\n        textfile = open(path+\"/ant2.txt\", \"w\")\n        for element in data[1]:\n            textfile.write(str(element[0])+ \",\" + str(element[1]) + \",\" + str(element[2]) + \",\" + str(element[3]) + \"\\n\")\n        textfile.close()\n\n        print(\"\\n--- Data saved ---\")\n    except:\n        print(\"\\nExport_data(): *** Error occured: Data are not saved ***\")\n\n\ndef EPCs_displayer():\n    #States\n    global S_g\n    \n    # Variables\n    global t_refresh\n    global data\n\n    epcs_1 = None\n    epcs_2 = None\n\n    while S_g.thread:\n\n        if S_g.read:\n            reader.set_read_plan([1], \"GEN2\", epc_target = S_g.target)\n            epcs_1 = list( map( lambda t: [t.epc, t.rssi, t.antenna, str(datetime.fromtimestamp(t.timestamp) - start)[5::] ], reader.read(timeout = int(t_refresh/2)) ) )\n\n            reader.set_read_plan([2], \"GEN2\", epc_target = S_g.target) \n            epcs_2 = list( map( lambda t: [t.epc, t.rssi, t.antenna, str(datetime.fromtimestamp(t.timestamp) - start)[5::] ], reader.read(timeout = int(t_refresh/2)) ) )\n\n            if S_g.save:\n                data = save_Data(epcs_1, epcs_2)\n                print(\"saving\\n\")\n\n        displayer(S_g, epcs_1, epcs_2)\n\n\ndef displayer(S_g, epcs_1, epcs_2):\n    global t_refresh\n\n    if S_g.page == \"Menu\":\n        print(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n### Menu: ###\\n\\n r: reading\\n p: parameters\\n q: quit\")\n        while S_g.page == \"Menu\":\n            time.sleep(1)\n\n    elif S_g.page == \"Read\":\n        # Display mode\n        print(\"\\n\\n\\n\\n\\n\", \"Mode: \", \"Normal\" if S_g.target == None else \"Target\")\n\n        # Display reading and saving\n        print(\"\\n\\n\", \"Reading\" if S_g.read else \"       \", \" & \" if S_g.read and S_g.save else \" \", \"Saving\" if S_g.save else \"      \", \"\\n\")\n\n        # Display what is read\n        if S_g.target == None:\n            print(\"\\n Antenna 1:\\n\", epcs_1, \"\\n\\n Antenna 2: \\n\", epcs_2, \"\\n\")\n        else:\n            print(\"Target = \", S_g.target, \"\\n\")\n            print(\"Antenna 1: \", 1 if epcs_1 else 0, \"  / Antenna 2: \", 1 if epcs_2 else 0 )\n            print(\"\\n Antenna 1:\\n\", epcs_1, \"\\n\\n Antenna 2: \\n\", epcs_2, \"\\n\")\n\n        while (S_g.read == False) and S_g.page == \"Read\" and S_g.thread:\n            time.sleep(1)\n\n\ndef program_measure():\n    global t_refresh\n    global S_g\n\n    t_scan_test = [10, 30, 50, 100, 300, 500, 1000]\n    for t in t_scan_test:\n        t_refresh = t\n\n        #reset data\n        data = [[],[]]\n\n        #read mode\n        S_g.page = \"Read\"\n        S_g.read = True\n\n        #save mode\n        S_g.save = True\n        start = datetime.now()\n\n        # wait\n        time.sleep(3)\n\n        #stop\n        S_g.save = False\n        S_g.read = False\n\n        # export\n        name_fold = \"Ant2_\"+str(t_refresh) +\"_2\"\n        export_data(data, name_fold)\n\n    print( \"\\n\\n\\n\\n\\n\\n ************* FINIIIIIII ******** \\n\\n\\n\\n\\n\\n \")\n\n\nprint(\"--- Initialization Done ---\")\n############################################################################################################################\n\nS_g = states()\n#page, mode, read , save , thread\n\ndata = [[],[]]\nt_refresh, S_g.target = 100, None #settings()\n\nthr = threading.Thread(target=EPCs_displayer)\nthr.start()\n\nx = None\n##### Exit part #####\nwhile x != \"q\":\n\n    x = input()\n\n    ##### Menu #####\n    if x == \"m\":\n        S_g.page = \"Menu\"\n        S_g.read = False\n\n    ##### Read #####\n    elif x == \"r\":\n        # start reading data or stop reading\n        S_g.page = \"Read\"\n        S_g.read = False if S_g.read else True\n\n    elif x == \"s\" :\n        # start saving data or stop saving\n        S_g.save = False if S_g.save else True\n\n        start = datetime.now()\n\n    elif x == \"e\":\n        # Export the saved data\n        export_data(data)\n    \n    elif x == \"d\":\n        # Delete the saved data\n        data = [[],[]]\n        print(\"data deleted\")\n\n    elif x == \"show\":\n        # Show the saved data\n        print(\"\\n\\n Last read tag:\\n\")\n        try:\n            print(\"Antenna 1:\\n\", data[0][-1])\n        except:\n            print(\"antenna 1:\\n no data read\")\n\n        try:\n            print(\"\\n\\nAntenna 2:\\n\", data[1][-1])\n        except:\n            print(\"antenna 2:\\n no data read\")\n    \n    elif x == \"p\":\n        S_g.page = \"Settings\"\n        S_g.read = False\n        print(\"\\n\\n\\n\\n\\n\\n\")\n        t_refresh, S_g.target = settings(t_refresh, S_g)\n        S_g.page = \"Menu\"\n\n    if x == \"GO\":\n        program_measure()\n        \n\n\nS_g.page = None\nS_g.thread = False\ntime.sleep(1)\nprint(\"\\ntschus\")\n\n\n","repo_name":"Helvboy/Master_Project","sub_path":"Live_eval.py","file_name":"Live_eval.py","file_ext":"py","file_size_in_byte":7545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41852149196","text":"import struct\nfrom recordclass import recordclass\n\nfrom pyxenoverse import BaseRecord, read_name, write_name\n\nBCSBone = recordclass('BCSBone', [\n    'u_00',\n    'u_04',\n    'u_08',\n    'u_0c',\n    'u_0e',\n    'u_10',\n    'u_12',\n    'u_14',\n    'u_16',\n    'u_18',\n    'u_1a',\n    'u_1c',\n    'u_1e',\n    'u_20',\n    'u_22',\n    'u_24',\n    'u_26',\n    'u_28',\n    'u_2a',\n    'u_2c',\n    'u_2e',\n    'name_offset'\n])\nBCS_BONE_SIZE = 52\nBCS_BONE_BYTE_ORDER = 'IIIHHHHHHHHHHHHHHHHHHI'\n\n\nclass Bone(BaseRecord):\n    def __init__(self):\n        super().__init__()\n        self.name = ''\n        self.data = BCSBone(*([0] * len(BCSBone.__fields__)))\n\n    def read(self, f, endian):\n        address = f.tell()\n        self.data = BCSBone(*struct.unpack(endian + BCS_BONE_BYTE_ORDER, f.read(BCS_BONE_SIZE)))\n        if self.name_offset:\n            self.name = read_name(f, address + self.name_offset)\n            # print(self.name)\n        # print(self.data)\n\n    def write(self, f, names, endian):\n        address = f.tell()\n        self.name_offset = 0\n        f.write(struct.pack(endian + BCS_BONE_BYTE_ORDER, *self.data))\n\n        # Add name\n        if self.name:\n            names.append((address, 0x30, self.name))\n\n    def paste(self, other):\n        if type(self) != type(other):\n            return False\n        self.data = BCSBone(*other.data)\n        self.name = other.name\n        return True\n","repo_name":"KyonkoYuuki/pyxenoverse","sub_path":"pyxenoverse/bcs/bone.py","file_name":"bone.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"35187348708","text":"\"\"\"\nName: Katherine St. John\nEmail: katherine.stjohn@hunter.cuny.edu\nResources:  Used OpenDataNYC for data formats and pandas documentation for file args\n\"\"\"\nimport pandas as pd\n\ndef make_insp_df(file_name):\n    '''\n    Takes the name of a CSV file of restaurant inspection data from\n    data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j/data\n    and returns a cleaned df.\n    '''\n\n    df = pd.read_csv(file_name)\n    df = df[ ['CAMIS', 'DBA', 'BORO', 'BUILDING', 'STREET', 'ZIPCODE', 'SCORE', 'GRADE', 'NTA'] ]\n    df = df[ df['SCORE'].notnull() ]\n    return(df)\n\ndef predict_grade(num_violations):\n    '''\n    Takes the number of violations points and returns the letter grade.\n    '''\n\n    if num_violations <= 13:\n        return 'A'\n    elif num_violations <= 27:\n        return 'B'\n    else:\n        return 'C'\n\ndef grade2num(grade):\n    '''\n    Takes the letter grade of A, B, C and returns its value on a 4.0 scale.\n    Returns \"\" for all other values.\n    '''\n    if grade == \"\":\n        return None\n    elif grade == 'A':\n        return 4.0\n    elif grade == 'B':\n        return 3.0\n    elif grade == 'C':\n        return 2.0\n    else:\n        return None\n\ndef compute_ave_grade(df, col):\n    '''\n    This function takes a df and groups on NTA and takes the average\n    of the numeric values in col (i.e. mean(numeric_only=True)).\n    Converts the groupby object into a df and returns the df.\n    '''\n    grouped = df.groupby('NTA')[col].mean(numeric_only=True)\n    return(grouped.to_frame())\n\ndef make_nta_df(file_name):\n    '''\n    Takes the name of CSV file for Neighborhood Tabulation Areas and returns a\n    DataFrame with the two columns:  NTACode and NTAName\n    '''\n\n    df = pd.read_csv(file_name)\n    return(df[ ['NTACode','NTAName'] ])\n\ndef neighborhood_grades(ave_df,nta_df):\n    '''\n    This function takes a df containing NTACode and the nta_df containing\n    NTA and returns the joins on these two columns, dropping both before returning.\n    '''\n\n    df = ave_df.merge(nta_df,left_on='NTA',right_on='NTACode')\n    #return(df.drop(axis=1,columns = ['NTA','NTACode']))\n    return(df)\n","repo_name":"cherorafi/Data-Science-Spr22-Solutions","sub_path":"39542 Solution Master/Program Solutions/Program 3.py","file_name":"Program 3.py","file_ext":"py","file_size_in_byte":2145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29454313109","text":"import random\npalabras_secretas=[\"paralelepipedo\",\"oftalmologo\",\"caricatura\"]\npalabra=random.choice(palabras_secretas)\npalabra=\"lepidoptero\"\na=0\nnumero=len(palabra)\nocultas=random.randint(1,numero)\ndef ocultar_letras(a,b):\n    letras=[]\n    for letra in a:\n        letras.append(letra)    \n    while b>0:\n        elegida=random.randint(0,len(a)-1)\n        elegida2=letras[elegida]\n        if elegida2==\"_\":\n            pass\n        else:\n            elegida=int(elegida)\n            p=\"_\"\n            letras[elegida]=p\n            b=b-1\n        a=\"\".join(str(e) for e in letras)\n    return a\ndef revisar_letra(palabra_normal,palabra_oculta,INPUT):\n    letras_normal=[]\n    letras_oculta=[]\n    contador=0\n    if len(INPUT)>1:\n        if INPUT==palabra1:\n            palabra2=INPUT\n    elif len(INPUT)==1:\n        for letra in palabra_normal:\n            letras_normal.append(letra)\n        for letra in palabra_oculta:\n            letras_oculta.append(letra)\n        for letra in letras_normal:\n            if letra==INPUT:\n                letras_oculta[contador]=INPUT\n                contador=contador+1\n            elif letra!=INPUT:\n                contador=contador+1    \n        palabra_final=\"\".join(str(e) for e in letras_oculta)\n    return palabra_final\nif __name__ == \"__main__\":\n    palabra_secreta=ocultar_letras(palabra,ocultas)\n    while a<1:\n        print(\"La palabra es: \",palabra_secreta)\n        INPUT=input(\"Ingrese una letra o la palabra que usted crea que es: \")\n        palabra_secreta=revisar_letra(INPUT,palabra,palabra_secreta)\n        \n\n\n\n\n\n\n\n\n\n        \n        \n    \n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema4_ej1/tema4_ej1_5c341d10c5596a0fd920fda9f33bcb06.py","file_name":"tema4_ej1_5c341d10c5596a0fd920fda9f33bcb06.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11606725206","text":"import numpy as np\nimport  manimlib.constants as V\nfrom manimlib.animation.animation import Animation, AGroup\nfrom manimlib.animation.creation import ShowSubmobjectsOneByOne,Write,Show\nfrom manimlib.animation.composition import AnimationGroup,OneByOne,Succession\nfrom manimlib.animation.fading import FadeIn\nfrom manimlib.animation.growing import GrowFromCenter,DiminishToSide,DiminishToEdge\nfrom manimlib.animation.transform import Restore,ApplyMethod\nfrom manimlib.basic.basic_animation import FadeInThenIndicate, FadeoutSuccession, ShowPassingFlashAndIndicateThenFadeOut,Freeze,GrowTitle\n\nfrom manimlib.basic.basic_compound import ShowSubmobjectsOneByOneAndFadeInThenIndicateThenFadeOut\nfrom manimlib.basic.basic_mobject import ImageMobjectGroup, MobjectOrChars,OrderedGroup,AnimatedGroup\nfrom manimlib.basic.basic_geometry import GeomPosition\nfrom manimlib.constants import DOWN, GREEN, PoweredBy, Project, UP, WHITE, YELLOW\nfrom manimlib.mobject.geometry import Circle, RegularPolygon, Square, Triangle\nfrom manimlib.mobject.mobject import Group, Mobject\nfrom manimlib.mobject.shape_matchers import Underline\nfrom manimlib.mobject.svg.tex_mobject import TextMobject\nfrom manimlib.mobject.types.vectorized_mobject import VGroup\nfrom manimlib.utils.rate_functions import linear\n\n\nclass StartScreens01(AnimationGroup):\n    '''\n    mobjs_1, mobjs_1_scale, mobjs_1_interval, mobjs_1_rate_func, title_1, title_1_color, title_1_scale, title_1_position, title_1_shadow, title_1_indicate_scale_factor, title_1_extra = screen01 + [\"\", 2, 0.5, linear, \"\", \"#0808B8\", 2, [DOWN], [2, slice(0, 3, 2)], 1.2, \"\"][len(screen01):]\n\n    mobjs_2, mobjs_2_scale, mobjs_2_interval, mobjs_2_rate_func, title_2, title_2_color, title_2_scale, title_2_position, title_2_shadow, title_2_indicate_scale_factor, title_2_extra = screen02 + [\"\", 2, 0.5, linear, \"\", \"#0808B8\", 1.2, [DOWN], [2, slice(1, 4, 2)], \"\", [slice(1, 2), WHITE, [-2.5, 0, 0]]][len(screen02):]\n\n    [title, subtitle, filename, reference, warning, mobjes_3_run_time] = screen03 + [[], [], [], [], [], 3][len(screen03):]\n\n    [title, title_color, title_scale, title_position] = title + [\"\", WHITE, 1, [UP]][len(title):]\n\n    [subtitle, subtitle_color, subtitle_scale, subtitle_position] = subtitle + [\"\", WHITE, 1, [0, 0, 0]][len(subtitle):]\n\n    [filename, filename_color, filename_scale, filename_position] = filename + [\"\", WHITE, 1, [0, -2.9, 0]][len(filename):]\n\n    [reference, reference_color, reference_scale, reference_position] = reference + [\"\", YELLOW, 1, [0, -3.3, 0]][len(reference):]\n\n    [warning, warning_color, warning_scale, warning_position] = warning + [\"\", YELLOW, 1, [0, -3.7, 0]\n            ][len(warning):]\n    '''\n\n    def __init__(self, screen01=[], screen02=[], screen03=[], lag_ratio=1, **kwargs):\n        mobjs_1, mobjs_1_scale, mobjs_1_interval, mobjs_1_rate_func, \\\n            title_1, title_1_color, title_1_scale, title_1_position, title_1_shadow, \\\n            title_1_indicate_scale_factor, title_1_extra = screen01 + \\\n            [\"\", 2, 0.5, linear, \"\", \"#0808B8\", 2, [DOWN], [2, slice(0, 3, 2)],\n             1.2, \"\"][len(screen01):]\n        mobjs_2, mobjs_2_scale, mobjs_2_interval, mobjs_2_rate_func, \\\n            title_2, title_2_color, title_2_scale, title_2_position, title_2_shadow, \\\n            title_2_indicate_scale_factor, title_2_extra = screen02 + \\\n            [\"\", 2, 0.5, linear, \"\", \"#0808B8\", 1.2, [DOWN], [2, slice(1, 4, 2)],\n             \"\", [slice(1, 2), WHITE, [-2.5, 0, 0]]][len(screen02):]\n        [title, subtitle, filename, reference, warning, mobjes_3_run_time] =\\\n            screen03 + [[], [], [], [], [], 3][len(screen03):]\n        [title, title_color, title_scale, title_position] = title + \\\n            [\"\", WHITE, 1, [UP]][len(title):]\n        [subtitle, subtitle_color, subtitle_scale, subtitle_position] = subtitle + \\\n            [\"\", WHITE, 1, [0, 0, 0]][len(subtitle):]\n        [filename, filename_color, filename_scale, filename_position] = filename + \\\n            [\"\", WHITE, 1, [0, -2.9, 0]][len(filename):]\n        [reference, reference_color, reference_scale, reference_position] = reference + \\\n            [\"\", YELLOW, 1, [0, -3.3, 0]][len(reference):]\n        [warning, warning_color, warning_scale, warning_position] = warning + \\\n            [\"\", YELLOW, 1, [0, -3.7, 0]\n             ][len(warning):]\n\n        startscreens = AGroup()\n        if mobjs_1 != None:\n            if mobjs_1 == \"\":\n                try:\n                    mobjs_1 = ImageMobjectGroup(np.char.mod('%01d', range(\n                        0, 10)), \"sidewayoutput\\\\sidewayoutput2020yt\")\n                except:\n                    mobjs_1 = ImageMobjectGroup(np.char.mod(\n                        '%01d', range(9, -1, -1)), \"001\\\\\")\n            if title_1 == \"\":\n                title_1 = PoweredBy\n            title_1 = MobjectOrChars(title_1)\n            title_1.set_color(title_1_color).scale(title_1_scale).align_on_border(\n                *title_1_position).add_shadow_mobjects(title_1_shadow[0], title_1[title_1_shadow[1]])\n            if title_1_extra != \"\":\n                title_1[title_1_extra[0]].set_color(\n                    title_1_extra[1]).shift(title_1_extra[2])\n            if title_1_indicate_scale_factor == \"\":\n                title_width = mobjs_1.get_width()\n                title_1_indicate_scale_factor = (title_width-0.5)/title_width\n            startscreens.add(ShowSubmobjectsOneByOneAndFadeInThenIndicateThenFadeOut(\n                mobjs_1.scale(mobjs_1_scale),\n                title_1,\n                indicate_scale_factor=title_1_indicate_scale_factor,\n                show_rate_func=mobjs_1_rate_func,\n                run_time=mobjs_1_interval*(len(mobjs_1)),\n                **kwargs))\n        if mobjs_2 != None:\n            if mobjs_2 == \"\":\n                strs = TextMobject(r\"\\textspA{%s}\" % Project)\n                mobjs_2 = Group(\n                    Circle(fill_opacity=0.75),\n                    RegularPolygon(fill_opacity=0.75),\n                    Triangle(color=GREEN, fill_opacity=0.75),\n                    Square(fill_opacity=0.75),\n                    strs.set_color(\"#FFFFFF\"),\n                    strs.copy().set_color(\"#F8F8F8\").scale(1.3),\n                    strs.copy().set_color(\"#F8F8B8\").scale(1.6),\n                    strs.copy().set_color(\"#B8B8B8\").scale(1.6),\n                    strs.copy().set_color(\"#8888B8\").scale(1.6),\n                    strs.copy().set_color(\"#6868B8\").scale(1.6),\n                    strs.copy().set_color(\"#4848B8\").scale(1.6),\n                    strs.copy().set_color(\"#2828B8\").scale(1.6),\n                    strs.copy().set_color(\"#0808B8\").scale(1.6),\n                )\n            if title_2 == \"\":\n                title_2 = (r\"{\\tiny{\\emph{Powered by}:}}\\\\ \", *PoweredBy)\n            title_2 = MobjectOrChars(title_2)\n            title_2.set_color(title_2_color).scale(title_2_scale).align_on_border(\n                *title_2_position).add_shadow_mobjects(title_2_shadow[0], title_2[title_2_shadow[1]])\n            if title_2_extra != \"\":\n                title_2[title_2_extra[0]].set_color(\n                    title_2_extra[1]).shift(title_2_extra[2])\n            if title_2_indicate_scale_factor == \"\":\n                title_width = mobjs_2.get_width()\n                title_2_indicate_scale_factor = (title_width-0.5)/title_width\n            startscreens.add(ShowSubmobjectsOneByOneAndFadeInThenIndicateThenFadeOut(\n                mobjs_2.scale(mobjs_2_scale),\n                title_2,\n                indicate_scale_factor=title_2_indicate_scale_factor,\n                show_rate_func=mobjs_2_rate_func,\n                run_time=mobjs_2_interval*(len(mobjs_2)),\n                **kwargs))\n        if title != None or subtitle != None:\n            mobjs_3 = [Group(), \"\", \"\"]\n            if title != None:\n                txt_title = TextMobject(title).scale(title_scale)\n                if txt_title.get_width() > 14:\n                    txt_title.stretch_to_fit_width(14)\n                mobjs_3[1] = txt_title.set_color(\n                    title_color).to_edge(*title_position)\n                mobjs_3[0].add(mobjs_3[1])\n            if subtitle != None:\n                mobjs_3[0].add(TextMobject(subtitle).set_color(subtitle_color).scale(\n                    subtitle_scale).shift(subtitle_position))\n            if filename != None and filename != \"\":\n                if reference == None or reference == \"\":\n                    filename_position = reference_position\n                mobjs_3[0].add(TextMobject(filename).set_color(\n                    filename_color).scale(filename_scale).shift(filename_position))\n            if reference != None and reference != \"\":\n                txt_reference = TextMobject(reference).scale(reference_scale)\n                if txt_reference.get_width() > 14:\n                    txt_reference.stretch_to_fit_width(14)\n                mobjs_3[0].add(txt_reference.set_color(\n                    reference_color).shift(reference_position))\n            if warning != None and warning != \"\":\n                txt_warning = TextMobject(warning).scale(\n                    warning_scale)  # height=0.3\n                if txt_warning.get_width() > 14:\n                    txt_warning.stretch_to_fit_width(14)\n                mobjs_3[2] = txt_warning.set_color(\n                    warning_color).shift(warning_position)\n            animations = AGroup()\n            if len(mobjs_3[0]) > 0:\n                animations.add(\n                    FadeIn(mobjs_3[0], run_time=0.5, scale_factor=1, color=None))\n            if len(mobjs_3[1]) > 0:\n                animations.add(GrowFromCenter(Underline(mobjs_3[1])))\n            if len(mobjs_3[2]) > 0:\n                animations.add(FadeInThenIndicate(\n                    mobjs_3[2], run_time=0.5, scale_factor=1.2, color=None))\n            startscreens.add(FadeoutSuccession(AnimationGroup(\n                *animations, run_time=mobjes_3_run_time), run_time=0.05))\n        super().__init__(AnimationGroup(*startscreens, lag_ratio=1), **kwargs)\n\nclass EndScreen01(AnimationGroup):\n    def __init__(self, chars=r\"\\textrm{\\textbf{THANKS\\ FOR\\ WATCHING}}\", **kwargs):\n        super().__init__(\n            ShowPassingFlashAndIndicateThenFadeOut(\n                MobjectOrChars(chars), **kwargs),\n        )\n\nclass PlayMobject(AnimationGroup):\n    def __init__(self, mobjs=\"\", mobj=Circle(), scale=1,shift=[0,0,0],offset=None,run_time=1, clear=True, **kwargs):\n        if offset is None:\n            offset=shift\n        if mobjs != None:\n            if mobjs == \"\":\n                try:\n                    mobjs = ImageMobjectGroup(np.char.mod('%01d', range(\n                        0, 10)), \"sidewayoutput\\\\sidewayoutput2020yt\")\n                except:\n                    mobjs = ImageMobjectGroup(np.char.mod(\n                        '%01d', range(9, -1, -1)), \"001\\\\\")\n        #if mobj!=None:\n        t=mobj.update().get_critical_point([-1,1,0])-mobjs.get_critical_point([-1,1,0])\n        [each.add_updater(lambda mob,mob0=mobj:mob.shift(mob0.get_critical_point([-1,1,0])-mob.get_critical_point([-1,1,0])).shift(offset)) for each in mobjs]\n        if clear:\n            mobjs.add(Mobject())\n        super().__init__(\n            ShowSubmobjectsOneByOne(mobjs.update().scale(scale),run_time=run_time,**kwargs),\n        )\n\nclass SlideShow01(AnimationGroup):\n    def __init__(self, paras,anims,sound,\n        i_start=1, i_count=None,i_first=None,\n        position=GeomPosition([0, 3.3, 0]),width=[6.75],height=7, \n        mobjs=None, scene=None, animate=True,\n        **kwargs):\n        if i_count is None:\n            i_count=len(paras)\n        if i_first is None:\n            i_first=i_start\n        if scene is None:\n            scene=V.scene\n        if mobjs is None:\n            mobjs=AnimatedGroup().scale(0.2).post_to(scene)\n        animations=AGroup()\n        if 1==1 and paras[0] is not None:\n            animations.add(AnimationGroup(Animation(Mobject()),sound=sound[0],xaction=\"sound\"))\n            animations.add(AnimationGroup(GrowTitle(paras[0]),  Freeze(4),anims[0],foreground=True,xaction=\"post\"))\n        i_end = min((i_start+i_count), len(paras),  len(anims))\n        print(i_end,i_start+i_count,len(paras),len(paras))\n        group = VGroup(position)\n        OrderedGroup(group, [\"/\"]*(i_start-1) +\n                        paras[i_start:i_end],  width=width).post_to(scene)\n        block = VGroup()\n        print(\"i\",anims[0].run_time)\n        \n        anim=Animation(Mobject())\n        for i in range(i_start, i_end):\n            print(\"i\",i,anims[i].run_time)\n            group[i].add_to_group(block)\n            recycle = VGroup()\n            if animate:\n                anim=anims[i]\n            while block.get_height() > height:\n                if len(block) == 1:\n                    group[i].stretch_to_fit_height(height-0.05)\n                else:\n                    block[0].add_to_group(recycle).remove_from_group(block)\n            if i >= i_first:\n                animations.add(AnimationGroup(Animation(Mobject()),sound=sound[i], time_offset=0.5,xaction=\"sound\"))\n                animations.add(AnimationGroup(anim,PlayMobject(mobjs.copy().next_to(group[i]), group[i]), DiminishToSide(recycle), Write(group[i]), ))\n            else:\n                animations.add(AnimationGroup(animDiminishToSide(recycle), Animation(group[i]), xaction=\"display\"))#anim,\n        super().__init__(*animations)\n\nclass SlideShow02(AnimationGroup):\n    def __init__(self,\n        parbs,anibs,sndbs,\n        j_start=1,j_first=None, j_count=None,\n        i_start=1,i_first=None,i_count=None,\n        j_position=GeomPosition([-6.5, 3.3, 0]),j_width=[13],j_height=7,\n        i_position=GeomPosition([0, 3.3, 0]),i_width=[6.75],i_height=7, \n        mobjs=None, scene=None, **kwargs):\n        if j_count is None:\n            j_count=len(parbs[0])\n        if j_first is None:\n            j_first=j_start\n        if scene is None:\n            scene=V.scene\n        if mobjs is None:\n            mobjs=AnimatedGroup().scale(0.2).post_to(scene)\n        animations=AGroup()\n        if 1==1:\n            animations.add(AnimationGroup(Animation(Mobject()),sound=sndbs[0][0],xaction=\"sound\"))\n            animations.add(AnimationGroup(GrowTitle(parbs[0][0]),  Freeze(4),anibs[0][0],foreground=True,xaction=\"post\"))\n        j_end = min((j_start+j_count), len(parbs[0]),  len(anibs[0]))\n        grpa = VGroup(j_position)\n        OrderedGroup(grpa,  [\"/\"]*(j_start-1)+parbs[0][j_start:],  width=j_width).post_to(scene)#[\"/\"]*(i_start-1)+\n        blka = VGroup().save_state()\n        blk=[grpa[j_first:j_first+i+1] for i in range(j_count+1)]\n        blk[0].save_state()\n        for j in range(j_start, j_end):\n            print(\"j\",j)\n            grpa[j].add_to_group(blka)\n            rcca = VGroup()\n            while blka.get_height() > j_height:\n                if len(blka) == 1:\n                    grpa[j].stretch_to_fit_height(j_height-0.05)\n                else:\n                    blka[0].add_to_group(rcca).remove_from_group(blka)\n            if j >= j_first:\n                animations.add(AnimationGroup(Animation(Mobject()),sound=sndbs[0][j],xaction=\"sound\"))\n                animations.add(AnimationGroup(PlayMobject(mobjs.copy().next_to(grpa[j]), grpa[j]), DiminishToSide(rcca), Write(grpa[j]), anibs[0][j], Freeze(2)))\n            else:\n                animations.add(AnimationGroup(DiminishToSide(rcca), Animation(grpa[j]), anibs[0][j],xaction=\"display\"))\n            if 1 == 1 and j >= j_first:\n                animations.add(AnimationGroup(DiminishToEdge(blk[j-j_first].save_state()), run_time=0.5))\n                animations.add(*SlideShow01(parbs[j],anibs[j],sndbs[j],i_start, i_count,i_first).animations)\n                animations.add(AnimationGroup(exclude_mobjs=\"foreground\", xaction=\"fadeout\"))\n                if j<j_end-1:\n                    animations.add(AnimationGroup(Restore(blk[j-j_first]), run_time=0.5))\n\n        super().__init__(\n            *animations,\n        )\n\n","repo_name":"SidewayOutput/Basic-Manim","sub_path":"manimlib/basic/basic_complex.py","file_name":"basic_complex.py","file_ext":"py","file_size_in_byte":16001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16111334646","text":"\"\"\"\nThe National University conducts an examination of N students in X subjects.\nYour task is to compute the average scores of each student.\n\nThe format for the general mark sheet is:\n\nStudent ID → ___1_____2_____3_____4_____5__\nSubject 1   |  89    90    78    93    80\nSubject 2   |  90    91    85    88    86\nSubject 3   |  91    92    83    89    90.5\n            |______________________________\nAverage        90    91    82    90    85.5\n\"\"\"\nN, X = list(map(int, input().split()))\nfor t in zip(*[list(map(float, input().split())) for _ in range(X)]):\n    print(round(sum(t)/X, 1))\n","repo_name":"tinnan/python_challenge","sub_path":"11_built_ins/070_zipped.py","file_name":"070_zipped.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15979990013","text":"import unicodedata\r\n\r\ndef filter_accents(text):\r\n    \"\"\"Return a sequence of accented characters found in\r\n       the passed in lowercased text string\r\n    \"\"\"\r\n    accented = []\r\n    for line in text.splitlines():\r\n        for word in line.split():\r\n            for char in word:\r\n                if unicodedata.decomposition(char):\r\n                    accented.append(char.lower())   \r\n    return sorted(accented)\r\n\r\n","repo_name":"pyrrhull/bitesofpy","sub_path":"157/accents.py","file_name":"accents.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30082812727","text":"\"\"\"Работа с несколькими файлами\"\"\"\ndef count_words(filename):\n    \"\"\"Подсчет приблизительного количества слов в файле\"\"\"\n    try:\n        with open(filename, encoding='utf=8') as f:\n            contents = f.read()\n    except FileNotFoundError:\n        print(f\"Извините, файл {filename}, не найден.\")\n    else:\n        # Подсчет прибилизительного количества строк в файле.\n        words = contents.split()\n        num_words = len(words)\n        print(f\"В файле {filename}, есть {num_words} слов\")\n\n\nfilenames = ['alice.txt', 'siffhartha.txt', 'moby_dick.txt', 'little_women.txt']\nfor filename in filenames:\n    count_words(filename)\n","repo_name":"MaximZolotukhin/erik_metiz","sub_path":"chapter_10_part_2/10_word_count.py","file_name":"10_word_count.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34706898518","text":"#!/usr/bin/python3\n# Finding the Date Range for the Current Month\n\nfrom datetime import datetime, date, timedelta\nimport calendar\n\ndef get_month_range(start_date=None):\n\tif start_date is None:\n\t\tstart_date = date.today().replace(day=1)\n\n\t_, days_in_month = calendar.monthrange(start_date.year, start_date.month)\n\tend_date = start_date + timedelta(days=days_in_month)\n\treturn (start_date, end_date)\n\ndef date_range(start, stop, step):\n\twhile start < stop:\n\t\tyield start\n\t\tstart += step\n\n\nif __name__ == '__main__':\n\tfor d in date_range(datetime.today(), datetime.today() + timedelta(days=1), timedelta(hours=6)):\n\t\tprint(d)","repo_name":"cloud-sharknado/python","sub_path":"recipes/date-range.py","file_name":"date-range.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33131787847","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorflow_constrained_optimization.python import graph_and_eager_test_case\nfrom tensorflow_constrained_optimization.python.rates import defaults\nfrom tensorflow_constrained_optimization.python.rates import deferred_tensor\nfrom tensorflow_constrained_optimization.python.rates import operations\n# Placeholder for internal import.\n\n\n# @run_all_tests_in_graph_and_eager_modes\nclass OperationsTest(graph_and_eager_test_case.GraphAndEagerTestCase):\n  \"\"\"Tests for `Expression`-manipulation functions.\"\"\"\n\n  def _evaluate_expression(self, expression, extra_update_ops_fn=None):\n    \"\"\"Evaluates and returns both portions of an Expression.\n\n    Args:\n      expression: `Expression` to evaluate.\n      extra_update_ops_fn: function that takes an `EvaluationMemoizer` and the\n        list of `DeferredVariables`, and returns a list of ops to execute before\n        evaluation.\n\n    Returns:\n      A pair (penalty,constraint) containing the values of the penalty and\n      constraint portions of the `Expression`.\n    \"\"\"\n    structure_memoizer = {\n        defaults.DENOMINATOR_LOWER_BOUND_KEY: 0.0,\n        defaults.GLOBAL_STEP_KEY: tf.Variable(0, dtype=tf.int32),\n        defaults.VARIABLE_FN_KEY: tf.Variable\n    }\n\n    penalty_value = expression.penalty_expression.evaluate(structure_memoizer)\n    constraint_value = expression.constraint_expression.evaluate(\n        structure_memoizer)\n\n    # We need to explicitly create the variables before creating the wrapped\n    # session.\n    variables = deferred_tensor.DeferredVariableList(\n        penalty_value.variables + constraint_value.variables).list\n    for variable in variables:\n      variable.create(structure_memoizer)\n\n    def update_ops_fn():\n      if not extra_update_ops_fn:\n        update_ops = []\n      else:\n        update_ops = extra_update_ops_fn(structure_memoizer, variables)\n      for variable in variables:\n        update_ops += variable.update_ops(structure_memoizer)\n      return update_ops\n\n    with self.wrapped_session() as session:\n      session.run_ops(update_ops_fn)\n      return [\n          session.run(penalty_value(structure_memoizer)),\n          session.run(constraint_value(structure_memoizer))\n      ]\n\n  def test_wrap_rate_raises(self):\n    \"\"\"Make sure that wrap_rate() raises if given an `Expression`.\"\"\"\n    penalty_value = 3.1\n    constraint_value = 2.7\n\n    wrapped = operations.wrap_rate(penalty_value, constraint_value)\n    # We should raise a TypeError if either or both of the parameters to\n    # wrap_rate() are Expressions.\n    with self.assertRaises(TypeError):\n      operations.wrap_rate(penalty_value, wrapped)\n    with self.assertRaises(TypeError):\n      operations.wrap_rate(wrapped, constraint_value)\n    with self.assertRaises(TypeError):\n      operations.wrap_rate(wrapped, wrapped)\n\n  def test_wrap_rate_penalty(self):\n    \"\"\"Make sure that wrap_rate() works correctly when given one parameter.\"\"\"\n    penalty_value = 3.1\n    penalty_tensor = tf.constant(penalty_value, dtype=tf.float32)\n\n    wrapped = operations.wrap_rate(penalty_tensor)\n    actual_penalty_value, actual_constraint_value = self._evaluate_expression(\n        wrapped)\n    # Both the penalty and the constraint portions of the Expression were\n    # initialized to penalty_tensor.\n    self.assertNear(penalty_value, actual_penalty_value, err=1e-6)\n    self.assertNear(penalty_value, actual_constraint_value, err=1e-6)\n\n  def test_wrap_rate_penalty_and_constraint(self):\n    \"\"\"Make sure that wrap_rate() works correctly when given two parameters.\"\"\"\n    penalty_value = 3.1\n    constraint_value = 2.7\n    penalty_tensor = tf.constant(penalty_value, dtype=tf.float32)\n    constraint_tensor = tf.constant(constraint_value, dtype=tf.float32)\n\n    wrapped = operations.wrap_rate(penalty_tensor, constraint_tensor)\n    wrapped = operations.wrap_rate(penalty_tensor)\n    actual_penalty_value, actual_constraint_value = self._evaluate_expression(\n        wrapped)\n    # The penalty and constraint portions of the Expression were initialized to\n    # penalty_tensor and constraint_tensor, respectively.\n    self.assertNear(penalty_value, actual_penalty_value, err=1e-6)\n    self.assertNear(penalty_value, actual_constraint_value, err=1e-6)\n\n  def test_upper_bound_raises_on_empty_list(self):\n    \"\"\"Make sure that upper_bound() raises for an empty expressions list.\"\"\"\n    with self.assertRaises(ValueError):\n      operations.upper_bound([])\n\n  def test_upper_bound_raises_on_tensor(self):\n    \"\"\"Make sure that upper_bound() raises when given a non-Expression.\"\"\"\n    value1 = 3.1\n    value2 = 2.7\n    tensor1 = tf.constant(value1, dtype=tf.float32)\n    expression2 = operations.wrap_rate(tf.constant(value2, dtype=tf.float64))\n\n    # List element is a Tensor, instead of an Expression.\n    with self.assertRaises(TypeError):\n      operations.upper_bound([tensor1, expression2])\n\n  def test_upper_bound_raises_on_maximization(self):\n    \"\"\"Make sure that upper_bound() raises if it's maximized.\"\"\"\n    bounded = -operations.upper_bound([operations.wrap_rate(1.0)])\n    # All three of \"penalty_expression\", \"constraint_expression\" and\n    # \"extra_constraints\" should raise.\n    with self.assertRaises(RuntimeError):\n      _ = bounded.penalty_expression\n    with self.assertRaises(RuntimeError):\n      _ = bounded.constraint_expression\n    with self.assertRaises(RuntimeError):\n      _ = bounded.extra_constraints\n\n  def test_upper_bound(self):\n    \"\"\"Make sure that upper_bound() creates the correct Expression.\"\"\"\n    values = [0.8, 3.1, -1.6, 2.7]\n    bound_value = 1.4\n    expressions = [operations.wrap_rate(value) for value in values]\n\n    bounded = operations.upper_bound(expressions)\n\n    # Before evaluating any expressions, we'll assign \"bound_value\" to the slack\n    # variable, so that we can make sure that the same slack variable is being\n    # used for all of the constraints.\n    def update_ops_fn(structure_memoizer, variables):\n      upper_bound_tensor = None\n      for variable in variables:\n        tensor = variable(structure_memoizer)\n        if tensor.name.startswith(\"tfco_upper_bound\"):\n          self.assertIsNone(upper_bound_tensor)\n          upper_bound_tensor = tensor\n      self.assertIsNotNone(upper_bound_tensor)\n      return [upper_bound_tensor.assign(bound_value)]\n\n    # Extract the set of constraints, and make sure that there is one for each\n    # quantity that is being bounded.\n    self.assertEqual(len(values), len(bounded.extra_constraints))\n    constraints = list(bounded.extra_constraints)\n\n    # Evaluate the constraint expressions.\n    actual_values = []\n    for constraint in constraints:\n      actual_penalty_value, actual_constraint_value = self._evaluate_expression(\n          constraint.expression, update_ops_fn)\n      self.assertEqual(actual_penalty_value, actual_constraint_value)\n      actual_values.append(actual_penalty_value)\n    # Constraints take the form expression <= 0, and these are upper-bound\n    # constraints, so we're expecting:\n    #     value1 - bound_value <= 0\n    #     value2 - bound_value <= 0\n    #       ....\n    # We sort the constraint expression values since they occur in no particular\n    # order.\n    actual_values = sorted(actual_values)\n    expected_values = sorted(value - bound_value for value in values)\n    self.assertAllClose(expected_values, actual_values, rtol=0, atol=1e-6)\n\n  def test_lower_bound_raises_on_empty_list(self):\n    \"\"\"Make sure that lower_bound() raises for an empty expressions list.\"\"\"\n    with self.assertRaises(ValueError):\n      operations.lower_bound([])\n\n  def test_lower_bound_raises_on_tensor(self):\n    \"\"\"Make sure that lower_bound() raises when given a non-Expression.\"\"\"\n    value1 = 3.1\n    value2 = 2.7\n    tensor1 = tf.constant(value1, dtype=tf.float32)\n    expression2 = operations.wrap_rate(tf.constant(value2, dtype=tf.float64))\n\n    # List element is a Tensor, instead of an Expression.\n    with self.assertRaises(TypeError):\n      operations.lower_bound([tensor1, expression2])\n\n  def test_lower_bound_raises_on_minimization(self):\n    \"\"\"Make sure that lower_bound() raises if it's minimized.\"\"\"\n    bounded = operations.lower_bound([operations.wrap_rate(1.0)])\n    # All three of \"penalty_expression\", \"constraint_expression\" and\n    # \"extra_constraints\" should raise.\n    with self.assertRaises(RuntimeError):\n      _ = bounded.penalty_expression\n    with self.assertRaises(RuntimeError):\n      _ = bounded.constraint_expression\n    with self.assertRaises(RuntimeError):\n      _ = bounded.extra_constraints\n\n  def test_lower_bound(self):\n    \"\"\"Make sure that lower_bound() creates the correct Expression.\"\"\"\n    values = [0.8, 3.1, -1.6, 2.7]\n    bound_value = 1.4\n    expressions = [operations.wrap_rate(value) for value in values]\n\n    # We need to negate \"bounded\" since it's implicitly being minimized, and we\n    # cannot minimize a lower bound.\n    bounded = -operations.lower_bound(expressions)\n\n    # Before evaluating any expressions, we'll assign \"bound_value\" to the slack\n    # variable, so that we can make sure that the same slack variable is being\n    # used for all of the constraints.\n    def update_ops_fn(structure_memoizer, variables):\n      lower_bound_tensor = None\n      for variable in variables:\n        tensor = variable(structure_memoizer)\n        if tensor.name.startswith(\"tfco_lower_bound\"):\n          self.assertIsNone(lower_bound_tensor)\n          lower_bound_tensor = tensor\n      self.assertIsNotNone(lower_bound_tensor)\n      return [lower_bound_tensor.assign(bound_value)]\n\n    # Extract the set of constraints, and make sure that there is one for each\n    # quantity that is being bounded.\n    self.assertEqual(len(values), len(bounded.extra_constraints))\n    constraints = list(bounded.extra_constraints)\n\n    # Evaluate the constraint expressions.\n    actual_values = []\n    for constraint in constraints:\n      actual_penalty_value, actual_constraint_value = self._evaluate_expression(\n          constraint.expression, update_ops_fn)\n      self.assertEqual(actual_penalty_value, actual_constraint_value)\n      actual_values.append(actual_penalty_value)\n    # Constraints take the form expression <= 0, and these are lower-bound\n    # constraints, so we're expecting:\n    #     bound_value - value1 <= 0\n    #     bound_value - value2 <= 0\n    #       ....\n    # We sort the constraint expression values since they occur in no particular\n    # order.\n    actual_values = sorted(actual_values)\n    expected_values = sorted(bound_value - value for value in values)\n    self.assertAllClose(expected_values, actual_values, rtol=0, atol=1e-6)\n\n\nif __name__ == \"__main__\":\n  tf.test.main()\n","repo_name":"google-research/tensorflow_constrained_optimization","sub_path":"tensorflow_constrained_optimization/python/rates/operations_test.py","file_name":"operations_test.py","file_ext":"py","file_size_in_byte":10754,"program_lang":"python","lang":"en","doc_type":"code","stars":297,"dataset":"github-code","pt":"77"}
+{"seq_id":"22517136547","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nfrom typing import Optional\n\n# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\nclass Solution:\n    def btreeGameWinningMove(self, root: Optional[TreeNode], n: int, x: int) -> bool:\n        count_map = {}    # 节点值 -> 该节点子树的总结点数\n        node_map = {}    # 节点值 -> 节点对象\n\n        # 获取子树总结点数的dfs函数\n        def dfs(node):\n            if node is None: return 0\n            cnt = dfs(node.left) + dfs(node.right) + 1\n            count_map[node.val] = cnt\n            node_map[node.val] = node\n            return cnt\n\n        dfs(root)\n\n        total = count_map[root.val]\n\n        # 第一种获胜的情况\n        if total - count_map[x] > count_map[x]:\n            return True\n\n        # 第二种获胜的情况\n        l, r = node_map[x].left, node_map[x].right\n        if total - count_map[l.val] < count_map[l.val] or total - count_map[r.val] < count_map[r.val]:\n            return True\n\n        return False","repo_name":"ftakanashi/JobProjects","sub_path":"LeetCode/1145.二叉树着色游戏/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"28383830181","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Dec  2 13:33:18 2020\r\n\r\n@author: AWSCLOUD\\l6oi\r\n\"\"\"\r\n\r\nimport sys\r\nfrom pyspark.sql import SparkSession\r\n#from awsglue.transforms import *\r\nfrom awsglue.utils import getResolvedOptions\r\nfrom pyspark.context import SparkContext\r\n#from awsglue.context import GlueContext\r\n#from awsglue.job import Job\r\n#from awsglue.dynamicframe import DynamicFrame\r\n#from pyspark.sql.functions import *\r\nfrom pyspark.sql.types import StructType, StructField, StringType\r\nfrom datetime import datetime\r\nimport boto3\r\n#from pyspark.sql.window import Window\r\nimport json\r\nimport re\r\n\r\n# schema to host the table column names - and set up them as string\r\ndef generateSchema(cols):\r\n    \"\"\"generates schema for spark dataframe creation\"\"\"\r\n    schema = StructType([StructField(col,StringType()) for col in cols])\r\n    return schema\r\n\r\n#\r\n# main\r\n#\r\n    \r\n#parameters to be given while running this job manually --programName, --programFiletype, --filename\r\n\"\"\" The AWS Glue getResolvedOptions(args, options) utility function gives you access to the arguments\r\n    that are passed to your script when you run a job. To use this function, start by importing it \r\n    from the AWS Glue utils module, along with the sys module: \"\"\"\r\nargs = getResolvedOptions(sys.argv,\r\n                          ['inputBucketName',\r\n                           'outputBucketName',\r\n                           'configBucketName',\r\n                           'targetOutboundBucket',\r\n                           'filename',\r\n                           'debug_level'])\r\n\r\ntoday = datetime.today()\r\nyear = today.year\r\nmonth = today.month\r\nday = today.day\r\n# This one is used for the T1 and outbound \r\ndate = f\"{year}-{month}-{day}\"\r\n\r\n\r\ninputBucketName = args['inputBucketName']\r\noutputBucketName = args['outputBucketName']\r\nconfigBucketName = args['configBucketName']\r\ntargetOutboundBucket = args['targetOutboundBucket']\r\nfilename = args['filename']\r\ndebug = int(args['debug_level'])\r\n\r\nprint(f\"inputBucketName={inputBucketName}, \" +\r\n      f\"outputBucketName={outputBucketName}, \" +\r\n      f\"configBucketName={configBucketName}, \" +\r\n      f\"targetOutboundBucket={targetOutboundBucket}, \" +\r\n      f\"filename={filename}. \")\r\n\r\n# S3.Client - A low-level client representing Amazon Simple Storage Service (S3)\r\n# use this to retrieve the files \r\ns3_client = boto3.client(\"s3\")\r\n\r\n# this will be used later for copy/delete\r\ns3_resource = boto3.resource('s3')\r\n\r\n#\r\n# loads column names and their lengths from config\r\n#\r\nobj = s3_client.get_object(Bucket=f\"{configBucketName}\", Key='config/1800_fixed.json')\r\n\r\n# whole file Body is read as string\r\ndata = obj.get('Body').read()\r\n\r\n# Python dictionary\r\nconfig = json.loads(data)\r\n\r\n# set up log level\r\nsc = SparkContext()\r\nsc.setLogLevel('ERROR')\r\n\r\n# create Spark Session to create DataFrame\r\nspark = SparkSession.builder \\\r\n        .appName(\"parquet conversion\").master(\"local[*]\")\\\r\n        .config(\"spark.sql.shuffle.partitions\",\"3\")\\\r\n        .config(\"spark.sql.parquet.compression.codec\", \"uncompressed\")\\\r\n        .getOrCreate()\r\n\r\n#\r\n# list for all input files starting as SUCCESS.T#EFT.UN.ACOT.NGD1800.\r\n#\r\n\r\n# if filename == \"ALL\", files with prefix will be checked or listed\r\n# if filename != \"ALL\", each individual files will be listed\r\nif filename == \"ALL\":\r\n    resp = s3_client.list_objects_v2(Bucket=inputBucketName, Prefix=\"SUCCESS.T#EFT.UN.ACOT.NGD1800.\")\r\n    files = []\r\n    for obj in resp['Contents']:\r\n        files.append(obj[\"Key\"])\r\nelse:\r\n    files = [filename]\r\n    \r\n# process all the files in a loop\r\nfor filename in files:\r\n    program = None\r\n    cfg = None\r\n    for key in config.keys():\r\n        pat = config[key][\"filename_pat\"]\r\n        if re.match(pat, filename):\r\n            program = key\r\n            cfg = config[key]\r\n            break\r\n\r\n# If filename does not match any of the pattern, then it will go to next file\r\n    if program is None:\r\n        print(f\"File {filename} is not 1800 file. \")\r\n        continue\r\n    \r\n    print(f\"Processing {program} program file {filename} ... \")\r\n    \r\n    # read data from the file\r\n    table_array = []\r\n    obj = s3_client.get_object(Bucket=inputBucketName, Key=filename)\r\n    \r\n    # byte array\r\n    data = obj.get(\"Body\").read()\r\n    error = 0\r\n    trailer_code_length = cfg[\"trailer_fields\"][\"code\"]\r\n    # used to check record counts\r\n    line_num = 0\r\n    \r\n    for line in data.splitlines():\r\n        line_num += 1\r\n        if line_num == 1:\r\n            # read and verify header code\r\n            header_code_length = cfg[\"header_fields\"][\"code\"]\r\n            #check header if equal to header in Json\r\n            header_code = line[0:header_code_length]\r\n            header_code = header_code.decode(\"latin_1\")\r\n            if cfg[\"header_code\"] != header_code:\r\n                print(f\"Invalid header code '{header_code}'. \")\r\n                error = 1\r\n                break\r\n            continue\r\n        \r\n        # is it a trailer line?\r\n        trailer_code = line[0:trailer_code_length]\r\n        trailer_code = trailer_code.decode(\"latin_1\")\r\n        if cfg[\"trailer_code\"] == trailer_code:\r\n            # last line\r\n            trailer_date_len = cfg[\"trailer_fields\"][\"date\"]\r\n            trailer_count_len = cfg[\"trailer_fields\"][\"count\"]\r\n            count_start = trailer_code_length + trailer_date_len\r\n            count_end = trailer_code_length + trailer_date_len + trailer_count_len\r\n            expected_count = int(line[count_start: count_end].decode(\"latin_1\"))\r\n            if expected_count != line_num - 2:\r\n                print(f\"File has {line_num - 2} data rows. Expect {expected_count} rows. \")\r\n            break\r\n        \r\n        # read data row\r\n        pos = 0\r\n        fields = cfg[\"data_fields\"]\r\n        line_len = len(line)\r\n        table_row = {}\r\n        \r\n        for col in fields.keys():\r\n            field_len = fields[col]\r\n            if pos + field_len > line_len:\r\n                print(f\"ERROR: line {line_num} is too short at {line_len} characters. Expect {pos + field_len}. \")\r\n                value = \"\"\r\n            else:\r\n                value = line[pos: pos + field_len]\r\n                value = value.decode(\"latin_1\").strip()\r\n            table_row[col] = value\r\n            pos += field_len\r\n            \r\n        table_array.append(table_row)\r\n        \r\n        if debug == 1:\r\n            print(f\"Data row {line_num - 1}: \", table_row, \". \")\r\n            \r\n        # end of data line\r\n        \r\n    # end of reading from a file\r\n    \r\n    del data\r\n    if error != 0:\r\n        continue\r\n    \r\n    # create spark sql DataFrame\r\n    cols = cfg[\"data_fields\"].keys()\r\n    schema = generateSchema(cols)\r\n    df = spark.createDataFrame(table_array, schema)\r\n    #df.show(1)\r\n    \r\n    # create parquet\r\n    outputPath = f\"s3://{outputBucketName}/1800/{program}/parquet/{date}/\"\r\n\r\n    df.write.option(\"maxRecordsPerFile\", 100) \\\r\n        .option(\"compression\",\"uncompressed\") \\\r\n        .mode(\"overwrite\") \\\r\n        .format(\"parquet\").save(outputPath)\r\n\r\n    del df\r\n\r\n    #s3 copy to outbound folder\r\n    prefix =f\"1800/{program}/parquet/{date}/\"\r\n    resp = s3_client.list_objects_v2(Bucket=outputBucketName, Prefix = prefix)\r\n    for obj in resp['Contents']:\r\n        key = obj[\"Key\"]\r\n        copy_source = {\r\n            'Bucket': outputBucketName,\r\n            'Key': key\r\n        }\r\n        s3_resource.meta.client.copy(copy_source, targetOutboundBucket, key)\r\n        \r\n    \r\n    # #Copy the original file into T1 and delete the file from source \r\n    # copy_source = {\r\n    #         \"Bucket\": inputBucketName,\r\n    #         \"Key\": filename\r\n    #         }\r\n    # s3_resource.meta.client.copy(copy_source, outputBucketName, f\"1800/{program}/original/{date}/{filename}\")\r\n    # s3_client.delete_object(Bucket=inputBucketName, Key=filename)\r\n\r\n    print(f\"Done processing program {program} file {filename}. \")\r\n    \r\n    # end of processing file\r\n    \r\nprint(\"Job finished! \")\r\n\r\n\r\n","repo_name":"placidworld/funtodevelop","sub_path":"GlueJob_Fully_Automated.py","file_name":"GlueJob_Fully_Automated.py","file_ext":"py","file_size_in_byte":8005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37497173651","text":"import os\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\n\ndef transform_to_relative_timestamps(df: pd.DataFrame, drop_old_columns: bool = True,\n                                     restrict_to_time_range: bool = False, desired_time_range: int = 72,\n                                     enforce_min_time_range: bool = False, min_time_range: int = 12,\n                                     log_dir: str = '') -> pd.DataFrame:\n    \"\"\"\n    Transform the datetime column to relative timestamps in hours from first measurement.\n    - Restriction to time range can be applied (all data after time limit is dropped)\n    - Enforcement of minimum time range can be applied (all cases with a sampling range < min_time_range are dropped)\n\n    Reference for start of monitoring: start of continuous monitoring\n    continuous_monitoring_labels = ['systolic_blood_pressure','diastolic_blood_pressure','mean_blood_pressure',\n                                    'heart_rate','respiratory_rate','oxygen_saturation','temperature','FIO2',\n                                    'NIHSS','Glasgow Coma Scale ']\n\n    :param df: Dataframe with sample_date column\n    :param drop_old_columns: Drop columns with old timestamps\n    :param restrict_to_time_range: bool, if true, restrict to time range\n    :param desired_time_range: int, if restrict_to_time_range is True, restrict to this upper limit in hours, default is 72 hours\n    :param enforce_min_time_range: bool, if true, enforce minimum time range\n    :param min_time_range: int, if enforce_min_time_range is True, enforce minimum time range to this value in hours, default is 12 hours\n    :param log_dir: str, path to log directory\n    :return: Dataframe with relative timestamps\n    \"\"\"\n    datatime_format = '%Y-%m-%d %H:%M:%S'\n\n    df['sample_date'] = pd.to_datetime(df['sample_date'], format=datatime_format)\n\n    # SET REFERENCE time point 0 for each case_admission id\n    # - default reference as start: first sample date of monitoring in EHR (as lab is drawn in ED)\n    # Find first sample date of all EHR\n    first_ehr_sample_date = df[(df.source == 'EHR')] \\\n        .groupby('case_admission_id').sample_date.min().reset_index(level=0)\n    first_ehr_sample_date.rename(columns={'sample_date': 'first_ehr_sample_date'}, inplace=True)\n\n    # Find first sample date of EHR that is part of continuous monitoring (thus start of monitoring in the ICU)\n    continuous_monitoring_labels = ['systolic_blood_pressure','diastolic_blood_pressure','mean_blood_pressure',\n                                    'heart_rate','respiratory_rate','oxygen_saturation','temperature','FIO2',\n                                    'NIHSS','Glasgow Coma Scale ']\n    first_monitoring_sample_date = df[(df.source == 'EHR') & (df.sample_label.isin(continuous_monitoring_labels))] \\\n        .groupby('case_admission_id').sample_date.min().reset_index(level=0)\n    first_monitoring_sample_date.rename(columns={'sample_date': 'first_monitoring_sample_date'}, inplace=True)\n\n    # Find first sample date of admission notes\n    first_notes_sample_date = df[df.source == 'notes'].groupby(\n        'case_admission_id').sample_date.min().reset_index(level=0)\n    first_notes_sample_date.rename(columns={'sample_date': 'first_notes_sample_date'}, inplace=True)\n\n    merged_first_sample_dates_df = first_ehr_sample_date.merge(first_notes_sample_date, on='case_admission_id')\n    merged_first_sample_dates_df = merged_first_sample_dates_df.merge(first_monitoring_sample_date, on='case_admission_id')\n\n    merged_first_sample_dates_df['delta_first_sample_date_h'] = (\n                        merged_first_sample_dates_df[\n                            'first_ehr_sample_date']\n                        - merged_first_sample_dates_df[\n                            'first_notes_sample_date']) / np.timedelta64(1, 'h')\n\n    merged_first_sample_dates_df['delta_first_monitoring_sample_date_h'] = (\n                        merged_first_sample_dates_df[\n                            'first_ehr_sample_date']\n                        - merged_first_sample_dates_df[\n                            'first_monitoring_sample_date']) / np.timedelta64(1, 'h')\n\n    # verify that notes are not too distant from EHR\n    if any(merged_first_sample_dates_df['delta_first_sample_date_h'].abs() > 24):\n        raise Exception('Data extracted from admission notes should always occur before EHR!')\n\n    # verify that monitoring is not too distant from first lab data in EHR\n    if any(merged_first_sample_dates_df['delta_first_monitoring_sample_date_h'].abs() > 24):\n        warnings.warn('Lab data and monitoring data are too distant! Check the logs in \"verify_monitoring_too_distant_from_lab.csv\"')\n        if log_dir != '':\n            merged_first_sample_dates_df[merged_first_sample_dates_df['delta_first_monitoring_sample_date_h'].abs() > 24].to_csv(\n                os.path.join(log_dir, 'verify_monitoring_too_distant_from_lab.csv'))\n\n    merged_first_sample_dates_df['reference_first_sample_date'] = merged_first_sample_dates_df['first_monitoring_sample_date']\n    merged_first_sample_dates_df = merged_first_sample_dates_df[['case_admission_id', 'reference_first_sample_date']]\n\n    # TRANSFORM TO RELATIVE TIMESTAMPS\n    df['case_admission_id'] = df['case_admission_id'].astype(str)\n    merged_first_sample_dates_df['case_admission_id'] = merged_first_sample_dates_df['case_admission_id'].astype(str)\n    merged_first_sample_dates_df.rename(columns={'reference_first_sample_date': 'first_sample_date'}, inplace=True)\n    df = df.merge(merged_first_sample_dates_df, on='case_admission_id')\n    df['relative_sample_date'] = (pd.to_datetime(df['sample_date'], format=datatime_format)\n                                  - pd.to_datetime(df['first_sample_date'], format=datatime_format)) \\\n                                 / np.timedelta64(1, 'h')\n\n    # ensure notes data at timepoint 0\n    df.loc[df.source == 'notes', 'relative_sample_date'] = 0.0\n\n    # reaining samples with relative_sample_date < 0 are labs drawn in the ED which should be kept and set at tp0\n    # Reason: reference tp0 is first sample date of non lab date from EHR\n    df.loc[df['relative_sample_date'] < 0, 'relative_sample_date'] = 0\n\n    if restrict_to_time_range:\n        df = df[df['relative_sample_date'] <= desired_time_range]\n\n    if enforce_min_time_range:\n        max_sampling_dates = df[df.source != 'notes'].groupby(\n            'case_admission_id').relative_sample_date.max().reset_index()\n        cid_with_short_range = max_sampling_dates[\n            max_sampling_dates.relative_sample_date < min_time_range].case_admission_id.unique()\n        print(f'Excluding {len(cid_with_short_range)} cases with a sampling range < {min_time_range} hours')\n        df = df[~df['case_admission_id'].isin(cid_with_short_range)]\n        if log_dir != '':\n            pd.DataFrame(cid_with_short_range, columns=['case_admission_id']) \\\n                .to_csv(os.path.join(log_dir, 'excluded_patients_with_sampling_range_too_short.tsv'), index=False)\n\n    if drop_old_columns:\n        df.drop(['sample_date', 'first_sample_date'], axis=1, inplace=True)\n\n    return df\n","repo_name":"JulianKlug/Stroke-Unit-Preprocessing","sub_path":"mimic_preprocessing/database_assembly/relative_timestamps.py","file_name":"relative_timestamps.py","file_ext":"py","file_size_in_byte":7126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40762083139","text":"import flask\n\nfrom v4l2py import Device, VideoCapture\n\napp = flask.Flask(\"basic-web-cam\")\n\n\nPREFIX = b\"--frame\\r\\nContent-Type: image/jpeg\\r\\n\\r\\n\"\nSUFFIX = b\"\\r\\n\"\nINDEX = \"\"\"\\\n<!doctype html>\n<html lang=\"en\">\n<head>\n  <link rel=\"icon\" href=\"data:;base64,iVBORw0KGgo=\">\n</head>\n<body><img src=\"/stream\" /></body>\n</html>\n\"\"\"\n\n\ndef gen_frames():\n    with Device.from_id(0) as device:\n        capture = VideoCapture(device)\n        capture.set_format(640, 480, \"MJPG\")\n        for frame in device:\n            yield b\"\".join((PREFIX, bytes(frame), SUFFIX))\n\n\n@app.get(\"/\")\ndef index():\n    return INDEX\n\n\n@app.get(\"/stream\")\ndef stream():\n    return flask.Response(\n        gen_frames(), mimetype=\"multipart/x-mixed-replace; boundary=frame\"\n    )\n","repo_name":"tiagocoutinho/v4l2py","sub_path":"examples/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"77"}
+{"seq_id":"30124513157","text":"#Use dir function to display all built-in Python methods has for lists:\n\n#print(dir(list))\n\n# append adds a new element to the end of a list:\n\nt = ['a', 'b', 'c']\nt.append('100')\nprint(t)\n\n# extend takes a list as an argument and appends all of the elements:\n\nt1 = ['1', '2', '3']\nt2 = ['98', '99']\nt1.extend(t2) # Note that t2 is unchanged.\nprint(t1) \n\n# sort arranges the elements of the list from low to high:\n\nx = ['d', 'c', 'e', 'b', 'a']\ny = [1.0, 1, 99, 99.0]\nx.sort()\ny.sort()\nprint(x)\nprint(y)\n\n# remove modifies the list and removes the element: \n\nt = ['a', 'b', 'c']\nt.remove('b')\nprint(t)\n\n# pop modifies the list and removes an element with an index parameter: \n# Note that it also returns the removed element.\n\nt = ['a', 'b', 'c']\nx = t.pop(1)\nprint(t)\nprint(x)\n\n# The del statement can also be used to remove elements from a list:\n\nt = ['a', 'b', 'c']\ndel t[1]\nprint(t)\n\nt = [1, 2, 3, 4, 5, 6, 7]\ndel t[2:4]\nprint(t)\n","repo_name":"kylev114/PY4E","sub_path":"Chapter 08 Lists/8.6_listMethod.py","file_name":"8.6_listMethod.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14529219279","text":"\"\"\"\nTests for Hangman game\n\"\"\"\nimport pytest\n\nimport hangman\n\nSCENARIOS = [\n    {\n        'status': 'fail',\n        'input_data': ['a', 'b', 'c', 'd', 'f', 'e', 'g'],\n        'word': 'HELLO',\n        'guesses_count': 6\n    },\n    {\n        'status': 'fail',\n        'input_data': ['a', 'a', 't', 'c', 'd', 'f', 'e', 'g'],\n        'word': 'HELLO',\n        'guesses_count': 6\n    },\n    {\n        'status': 'fail',\n        'input_data': ['a', 'br', 'b', 'c', 'd', 'f', 'e', 'g'],\n        'word': 'HELLO',\n        'guesses_count': 6\n    },\n    {\n        'status': 'fail',\n        'input_data': ['a', '2', 'b', 'c', 'd', 'f', 'e', 'g'],\n        'word': 'HELLO',\n        'guesses_count': 6\n    },\n    {\n        'status': 'success',\n        'input_data': ['h', 'e', 'l', 'o'],\n        'word': 'HELLO',\n        'guesses_count': 6\n    },\n]\n\nSCENARIOS_MOCKED_INPUT = [(scenario, scenario) for scenario in SCENARIOS]\n\n\n@pytest.fixture(scope=\"function\")\ndef redefine_input(request):\n    \"\"\"\n    Pytest fixture for redefining input() function\n    :param request:\n    :return:\n    \"\"\"\n\n    class Input:\n        \"\"\"\n        Redefined input object\n        \"\"\"\n\n        def __init__(self, data):\n            self.data = data\n            self.count = 0\n\n        def __call__(self):\n            self.count += 1\n            if self.count > len(self.data):\n                raise Exception(\"Not enough input!\")\n            return self.data[self.count - 1]\n\n        def get_data(self):\n            \"\"\"\n            returns Input data\n            :return:\n            \"\"\"\n            return self.data\n\n    hangman.input = Input(request.param['input_data'])\n\n    def back():\n        hangman.input = input\n\n    request.addfinalizer(back)\n\n\ndef test_choose_the_word_function():\n    \"\"\"Test the correctness of choose_the_word function.\"\"\"\n    assert isinstance(hangman.choose_the_word([\"hello\", \"word\"]), str)\n\n\ndef test_create_bag_of_vocabulary_words_function():\n    \"\"\"Test the correctness of create_bag_of_vocabulary_words function.\"\"\"\n    assert hangman.create_bag_of_vocabulary_words()\n\n\n@pytest.mark.parametrize('scenario, redefine_input', SCENARIOS_MOCKED_INPUT,\n                         indirect=['redefine_input'])\n@pytest.mark.usefixtures('redefine_input')\ndef test_game_process_function_function(capsys, scenario):\n    \"\"\"Test the correctness of game_process function.\"\"\"\n    hangman.game_process(scenario['word'], scenario['guesses_count'])\n    captured = capsys.readouterr()\n    if scenario['status'] == 'fail':\n        assert \"You are lost\" in str(captured.out)\n    elif scenario['status'] == 'success':\n        assert \"You are won\" in str(captured.out)\n","repo_name":"sashanight/Hangman","sub_path":"tests/test_functions.py","file_name":"test_functions.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9573442969","text":"from django.shortcuts import render\nfrom django.core.paginator import Paginator\nfrom .models import Carpet\nfrom .filter_carpets import filter_carpets\n\n\nDEFAULT_FORM_INFO = {\"prf_width\": \"\", \"prf_height\": \"\",\n\t\t\t\t\t \"prf_accuracy\": 10,\n\t\t\t\t\t \"prf_style\": \"Alle\",\n\t\t\t\t\t \"prf_material\": \"Alle\",}\n\n# this is a comment\n\n\ndef check_prf(arg, prf):\n\tif bool(arg):\n\t\treturn arg\n\treturn DEFAULT_FORM_INFO[prf]\n\n\ndef create_prf(info):\n\toutput = {}\n\toutput[\"prf_width\"] = check_prf(info.get(\"prf_width\"), \"prf_width\")\n\toutput[\"prf_height\"] = check_prf(info.get(\"prf_height\"), \"prf_height\")\n\toutput[\"prf_accuracy\"] = check_prf(info.get(\"prf_accuracy\"), \"prf_accuracy\")\n\toutput[\"prf_style\"] = check_prf(info.get(\"prf_style\"), \"prf_style\")\n\toutput[\"prf_material\"] = check_prf(info.get(\"prf_material\"), \"prf_material\")\n\n\treturn output\n\n\n# Create your views here.\n\ndef collection(response):\n\tcarpets = Carpet.objects.all()\n\tinfo = response.GET\n\tprf = create_prf(info)\n\n\tvalid_carpets = list(filter(lambda carpet: filter_carpets(carpet, prf), carpets)) \n\tpaginator = Paginator(valid_carpets, 4)\n\n\tpage_number = info.get('page')\n\tpage_obj = paginator.get_page(page_number)\n\tpages = page_obj.paginator.num_pages\n\n\treturn render(response, \"collection/collection.html\", {\"actdict\": {\"collec\": \"active\",},\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"carpets\": page_obj,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"prf\": prf,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \"range_pages\": range(1, pages+1)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t   })\n\ndef view(response, id):\n\tid = id - 102345\n\tcarpet  = Carpet.objects.get(id=id)\n\treturn render(response, \"collection/view.html\", {\"actdict\": {\"collec\": \"active\",},\n\t\t\t\t\t\t\t\t\t\t\t\t\t \"carpet\": carpet})\n","repo_name":"creator64/npt-dev","sub_path":"collection/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7945358130","text":"import cv2\nfrom tqdm import tqdm\nimport os\nimport pandas as pd\nimport sys\n# from FINAL_IMG_PREP import *\nfrom video_info import *\n\nfolder_path = '/Users/monica_air/Documents/CCTV/1. 해외환경(1500개)/1. 배회(325개)'\n\nlist_ = os.listdir(folder_path)\nfile_list=[]\nfor i in list_:\n    if '.mp4'in i:\n        file_list.append(i)\n        \nstart_list_v2= []\nend_list_v2=[]\nfile_name_v2 = []\n\nfor i in tqdm (file_list):\n    try:\n        path = '/Users/monica_air/Documents/CCTV/1. 해외환경(1500개)/1. 배회(325개)/'+i\n            \n        cap, width, height, fps, fourcc, filename, out = vid_info(path, 'XVID','0903_test')\n        # print(fps)\n        if not cap.isOpened():\n            print('Video open failed!')\n            sys.exit()\n\n        # 배경 영상 등록\n        ret, back = cap.read()\n\n        if not ret:\n            print('Background image registration failed!')\n            sys.exit()\n            \n        # 연산 속도를 높이기 위해 그레이스케일 영상으로 변환\n        back = cv2.cvtColor(back, cv2.COLOR_BGR2GRAY)\n\n        # 가우시안 블러로 노이즈 제거 (모폴로지, 열기, 닫기 연산도 가능)\n        back = cv2.GaussianBlur(back, (0, 0), 1.0)\n        frame_num = []\n        z=0\n        # 비디오 매 프레임 처리\n        while True:\n            ret, frame = cap.read()\n            z+=1\n            if not ret:\n                break\n            \n            # 현재 프레임 영상 그레이스케일 변환\n            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n            # 노이즈 제거\n            gray = cv2.GaussianBlur(gray, (0, 0), 1.0)\n            \n            # 차영상 구하기 $ 이진화\n            # absdiff는 차 영상에 절대값\n            diff = cv2.absdiff(gray, back)\n            # gray_mean() or 밝기정도 지정 가능\n            ####!!!!!back.mean()으로 하면 smoke까지 가능?\n            _, diff = cv2.threshold(diff, 100, 255, cv2.THRESH_BINARY)\n            \n            # 레이브링을 이용하여 바운딩 박스 표시\n            cnt, _, stats, _ = cv2.connectedComponentsWithStats(diff)\n            \n            #contour\n        #     dilated = cv2.dilate(diff, None, iteration=3)\n\n            # contours, _ = cv2.findContours(diff, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n            \n            \n            for i in range(1, cnt):\n                x, y, w, h, s = stats[i]\n                \n                if s < 100: # 작을 수록 더 많이 추출\n                    continue\n                \n                # cv2.drawContours(frame, contours, -1, (0, 0, 255),2)\n                if x+y+w+h >=500:\n                    pass\n                    # cv2.rectangle(frame, (x, y, w, h), (0, 255, 0), 2)\n                    # cv2.putText(frame, \"Frame:{}\".format(z), (10,20),cv2.FONT_HERSHEY_SIMPLEX,\n                    #         1, (0,0,255),3)\n                    frame_num.append(z)\n        start = str(frame_num[0])\n        end = str(frame_num[-1])\n        \n        file_name_v2.append(file_list[i])\n        start_list_v2.append(start)\n        end_list_v2.append(end)\n    except:\n        pass\n    \n\ndf = pd.DataFrame({'file_name':file_name_v2, 'start':start_list_v2, 'end':end_list_v2})\ndf.to_csv('1. 해외환경\\(1500개\\).csv')","repo_name":"MonicaKim89/Smart_CCTV","sub_path":"motion_detector/frame_extractor.py","file_name":"frame_extractor.py","file_ext":"py","file_size_in_byte":3351,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30299785372","text":"#!/usr/bin/python\n# Given a list of condor history log files, this script parse each log file\n# into classads and evaluate all the attributes inside each classad. The \n# purpose is to make sure that for a certain attribute, it will be always \n# evaluted to be the right format (numerical or boolean), if it is evaluated \n# to be Undefined, remove this attribute from its belonging classad\n\nimport subprocess\nimport classad\nimport types\n\ndef classad_attr_atoi(ad, attr):\n  \"\"\"\n  convert numeric classad attribute from string to long\n  \"\"\"\n  if attr in ad.keys():\n    if ad[attr] == \"\":\n      del ad[attr]\n    else:\n      if type(ad[attr]) is types.StringType:\n        ad[attr] = long(ad[attr])\n\n# Get the list of condor history log files\ndir = \"/home/bockelman/zzhang/ELK_stack/condor_history_log_backup_new\"\np = subprocess.Popen(\"ls \" + dir, stdout=subprocess.PIPE, shell=True)\n(output, err) = p.communicate()\n\np_status = p.wait()\n\nlines = output.split('\\n')\n# remove the empty lines\nlines = filter(lambda a: a != '', lines)\n\n# iterate and process each condor history log file\nfor line in lines:\n  input = open(dir+\"/\"+line, \"r\")\n  output = open(dir+\"/p_\"+line, \"a+\")\n  global_job_id_set = set()\n  while True:\n    try:\n      ad = classad.parseNext(input)\n      for k in ad:\n        if ad.eval(k) is classad.Value.Undefined:\n          del ad[k]\n        else:\n          ad[k] = ad.eval(k)\n      if \"GlobalJobId\" in ad.keys():\n        if ad[\"GlobalJobId\"] in global_job_id_set:\n          pass\n        else:\n          global_job_id_set.add(ad[\"GlobalJobId\"])\n          classad_attr_atoi(ad, \"Chirp_StashCp_DlTimeMs\")\n          classad_attr_atoi(ad, \"Chirp_StashCp_FileSize\")\n          output.write(ad.printOld()+\"\\n\")\n      else:\n        classad_attr_atoi(ad, \"Chirp_StashCp_DlTimeMs\")\n        classad_attr_atoi(ad, \"Chirp_StashCp_FileSize\")\n        output.write(ad.printOld()+\"\\n\")\n    except StopIteration:\n      break\n\n  input.close()\n  output.close()\n","repo_name":"lostwinder/network_matching","sub_path":"log_collection/scripts/parse_eval_classad.py","file_name":"parse_eval_classad.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"33767994298","text":"from constraints import *\n\nclass Sudoku(object):\n\n    def getBox(self, i, j):\n\n        ri = (i // self.r) * self.r\n        cj = (j // self.c) * self.c\n\n        neighs = []\n\n        for ni in range(ri,ri+self.r):\n            for nj in range(cj,cj+self.c):\n                neighs.append((ni,nj))\n\n        return neighs\n\n    def __init__(self, puzzle, r, c):\n        self.size = r*c\n        self.r = r\n        self.c = c\n        self.puzzle = puzzle\n\n    def setupCSP(self):\n        thevars = [ (x, y) for x in range(self.size) for y in range(self.size) ]\n        variables = dict()\n\n        for var in thevars:\n            variables[var] = ConstraintVar([i for i in range(1, self.size+1)], var)\n\n        constraints = list()\n\n        different = lambda x,y: x != y\n\n        for i in variables:\n            for j in variables:\n                # If two distinct cells are in the same row or column must be different\n                if ( i != j and (i[0] == j[0] or i[1] == j[1] or self.getBox(i[0],i[1]) == self.getBox(j[0],j[1]) ) ) :\n                    constraints.append(BinaryConstraint(variables[i], variables[j], different))\n\n        #domain reduction\n        for i in range(self.size):\n            for j in range(self.size):\n                value = self.puzzle[i][j]\n                if value != 0:\n                    nodeConsistent( UnaryConstraint(variables[(i,j)], lambda x: x == value) )\n\n        for var in variables:\n            variables[var].initNeighbours(constraints)\n\n        return (variables, constraints)\n","repo_name":"zachvoll/Sudoku","sub_path":"sudokuCSP.py","file_name":"sudokuCSP.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34556761598","text":"import plotly.graph_objects as go\nimport pandas as pd\n\n\ndef plot_frequency(infile, outfile):\n    with open(infile) as in_handler:\n        df = pd.read_json(in_handler)\n        fig = go.Figure(\n            data=[\n                go.Bar(name='T', x=df.index, y=df['T'], marker={'color':'#1f76b4'}),\n                go.Bar(name='C', x=df.index, y=df['C'], marker={'color':'#ff7e0e'}),\n                go.Bar(name='G', x=df.index, y=df['G'], marker={'color':'#2da02c'}),\n                go.Bar(name='A', x=df.index, y=df['A'], marker={'color':'#d62728'})\n                ],\n            layout=go.Layout(\n                title='Nucleotide distribution of 26nt oligomer',\n                barmode='stack',\n                yaxis={'title': 'Frequency'},\n                xaxis={'title': 'Position'}\n            )\n        )\n        fig.write_image(outfile)\n\nif __name__ == \"__main__\":\n    plot_frequency(\"lemur_frequency.json\", \"lemur_frequency.pdf\")\n    plot_frequency(\"human_frequency.json\", \"human_frequency.pdf\")","repo_name":"CompGenomeLab/lemurRepair","sub_path":"nucleotide_frequency.py","file_name":"nucleotide_frequency.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34724789755","text":"import datetime\nimport random\nfrom itertools import combinations\nfrom random import getrandbits\nimport SchnorrATS\nfrom PKE_pg import ELGamal\nimport KASE\nimport DTPKE\nimport Sig\nfrom tools import Commit\n\nencrypt_pks = [0]\nencrypt_sks = [0]\ncombiningKeys = [[]]\ntracingKeys = [[]]\n\n\ndef Setup(qBit, rBit, t, n, n1, n2, n3):\n    print(\"Generating SchnorrATS keyPairs\")\n    S_param = SchnorrATS.KeyGen(n, t)\n    print(\"Commitment pk\")\n    r_pk = getrandbits(rBit)\n    com_pk = Commit.commit(S_param[0], r_pk)\n    print(\"DTPKE Setup starts\")\n    D_param = DTPKE.Setup(qBit, rBit)\n    D_dk, D_vk = (), ()\n    print(\"Generating n1 Combiners' signing keys\")\n    sign_pks, sign_sks = Sig.KeyGen(n1)\n    print(\"Generating n1 PKE' encryption keys\")\n    for i in range(1, n1 + 1):\n        keypair = ELGamal.gen_key()\n        encrypt_sks.append(keypair[0])\n        encrypt_pks.append(keypair[1])\n    print(\"Join n3 notaries into DTPKE\")\n    join_keys = [0]\n    for i in range(1, n3 + 1):\n        keypair = DTPKE.Join(D_param[7], i, D_param[0], D_param[-1])\n        join_keys.append(keypair)\n    print(\"KASE setup starts\")\n    G = [i for i in range(1, signatures_nums + 1)]\n    K_param = KASE.Setup(qBit, rBit, len(G))\n    print(\"Generating KASE keypair\")\n    mpk_msk = KASE.Keygen(K_param[0], K_param[1])\n    ka = KASE.Extract(mpk_msk[1], G, K_param[0], K_param[2], K_param[3])\n\n    print(\"Generating n1 Combining' keys\")\n\n    for i in range(1, n1 + 1):\n        pk_list = S_param[0]\n        sk_e = encrypt_sks[i]\n        ek = D_param[8]\n        sk_c = [pk_list, sk_e, t, ek, r_pk]\n        combiningKeys.append(sk_c)\n    print(\"Generating n2 Tracing' keys\")\n\n    for j in range(1, n2 + 1):\n        sk_e = encrypt_sks[j]\n        ck = D_param[9]\n        pk_list = S_param[0]\n        tracingKey = [sk_e, ck, pk_list]\n        tracingKeys.append(tracingKey)\n    gid_list = G\n    PK = [ka, com_pk, D_param[8], D_dk, D_vk, sign_pks, encrypt_pks, K_param[3], mpk_msk[0], gid_list]\n    return PK, S_param, D_param, K_param\n\n\ndef Sign(m, gid_list, all_S_list, all_N_list, pk_list, pk_e, sk_list, S_param):\n    all_encrypts_list = []\n    for i in range(len(all_S_list)):\n        gid = gid_list[i]\n        S = all_S_list[i]\n        N = all_N_list[i]\n        S_p, S_q, S_g = S_param[2], S_param[3], S_param[4]\n        signs = SchnorrATS.Sign(m, S, pk_list, sk_list, S_p, S_q, S_g)\n        encrypts = []\n        for i in range(len(signs)):\n            sign = signs[i]\n            plaintext = m + \",\" + str(sign[0]) + \",\" + str(sign[1]) + \",\" + str(gid) + \",\" + \",\".join(map(str, N))\n            encrypt = ELGamal.encrypt(plaintext, ELGamal.p, pk_e, ELGamal.r)\n            encrypts.append(encrypt)\n            # print(\"encrypt overhead:%s MB\" % (objsize.get_deep_size(encrypt) / 1024 / 1024))\n        all_encrypts_list.append(encrypts)\n    return all_encrypts_list\n\n\ndef getN(plaintext, n3):\n    l = plaintext.split(\",\")\n    N = l[-1 * n3:]\n    return [int(i) for i in N]\n\n\ndef generateM(k):\n    m = \"\"\n    length = 1024 * k\n    for i in range(length):\n        m += str(1)\n    return m\n\n\ndef generateN(all_N_list, n3, D_param):\n    all_N_PK_list = []\n    all_sk_list = []\n    all_user_keys = []\n    for N in all_N_list:\n        N_PK = []\n        sk_list = []\n        user_keys = []\n        for i in range(n3):\n            user_key = DTPKE.Join(D_param[7], i, D_param[0], D_param[-1])\n            N_PK.append(user_key[0])\n            sk_list.append(user_key[1])\n            user_keys.append(user_key)\n        all_N_PK_list.append(N_PK)\n        all_sk_list.append(sk_list)\n        all_user_keys.append(user_keys)\n    return all_N_PK_list, all_sk_list, all_user_keys\n\n\nqBit = 512\nrBit = 160\nn = 10\n# m length 2-16\nm = generateM(10)\nt = 5\nt2 = 5\nsignatures_nums = 1\norg_S = list(combinations(range(1, n + 1), t))\norg_N = list(combinations(range(1, n + 1), t2))\nrandom.shuffle(org_S)\nrandom.shuffle(org_N)\norg_S = org_S[:signatures_nums]\norg_N = org_N[:signatures_nums]\nn1 = 5\nn2 = 5\nall_S_list = [list(x) for x in org_S]\nall_N_list = [list(x) for x in org_N]\n# n3 == t'\nn3 = len(all_N_list[0])\n\nprint(\"Start SetUp\")\nstarttime = datetime.datetime.now()\nPK, S_param, D_param, K_param = Setup(qBit, rBit, t, n, n1, n2, n)\nall_N_PK_list, all_sk_list, all_user_keys = generateN(all_N_list, n3, D_param)\nendtime = datetime.datetime.now()\nprint(\"SetUpTime：%s ms\" % ((endtime - starttime).total_seconds() * 1000))\n\nprint(\"Start Signing\")\nstarttime = datetime.datetime.now()\nall_encrypts_list = Sign(m, PK[-1], all_S_list, all_N_list, S_param[0], encrypt_pks[1], S_param[1], S_param)\nendtime = datetime.datetime.now()\nprint(\"Sign Time：%s ms\" % ((endtime - starttime).total_seconds() * 1000))\nprint(\"Combine Param\")\nD_dummy_users, D_pairing, D_y, D_h, D_a = D_param[6], D_param[0], D_param[3], D_param[2], D_param[4]\nK_pairing, K_g, K_n, K_pubk = K_param\nSGX1_param = (\ncombiningKeys, all_N_PK_list, all_N_list, all_S_list, all_encrypts_list, PK, D_dummy_users, D_pairing, D_y, D_h, D_a,\nK_pairing, K_g, K_n, K_pubk)\nNoSGX2_param = (m, D_param, K_param, PK, S_param, all_N_list, all_N_PK_list, all_sk_list, encrypt_pks, signatures_nums)\nSGX2_param = (tracingKeys, t, n, m, all_user_keys, D_param, K_param, S_param, encrypt_pks)\n","repo_name":"UbiPLab/DeTAPS","sub_path":"main/DeTAPS_NoSGX.py","file_name":"DeTAPS_NoSGX.py","file_ext":"py","file_size_in_byte":5189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33291382909","text":"# -*- coding: utf-8 -*-\n'''\n'''\n\nimport pandas as pd\nimport numpy as np\n#import spotpy.hydrology.signatures as sig\n#import spotpy.objectivefunctions as obj\nimport matplotlib.pyplot as plt\nimport os\nimport matplotlib.lines as mlines\n\n\nclass Signatures():\n    \n    def __init__(self):            \n        self.conversion_factor = 86400 # converts m³/day to m³/s\n        self.hyd_year_start, self.hyd_year_end = 10, 9\n        \n    def check_data_inputs(self, simulated, observed):\n        simulated = simulated.index.droplevel(1)\n        sim_years = simulated.year.unique()\n        \n        observed = observed.index.droplevel(1)\n        obs_years = observed.year.unique()\n        \n        n = 0\n        for year in sim_years:\n            if year in obs_years:\n                n = n+1\n        pct = int((n/len(sim_years))*100)\n        del n\n        print('Observation covers %s pct of simulation period' % pct)\n        return pct\n            \n    def createFolder(self, directory):\n        \"\"\"\n        Creates a folder\n        \n        @params: target directory\n        \n        \"\"\"\n        try:\n            if not os.path.exists(directory):\n                os.makedirs(directory)\n        except OSError:\n            print ('Error: Creating directory. ' + directory)\n            \n                           \n    def plot_ObjectiveFunctions(self, fname, NSE_val, bias_val, r2_val, period, results_path):\n        \"\"\"\n        plots the results of the objective functions\n        \n        @params: \n            self.fname = Name of project\n            NSE_val = NSE list values for period*\n            bias_val = bias list values for period*\n            r2_val = r2 list values for period*\n            period* = month or year\n        \n        \"\"\"\n        fig1 = plt.figure(1)\n                \n        plt.subplot(311)\n        plt.scatter(*zip(*NSE_val), color='r')\n        plt.grid(True)\n        plt.ylabel('NSE')\n\n        plt.subplot(312)\n        plt.scatter(*zip(*bias_val), color='b')\n        plt.grid(True)\n        plt.ylabel('bias')\n                     \n        plt.subplot(313)\n        plt.scatter(*zip(*r2_val),color='g')\n        plt.grid(True)\n        plt.ylabel('r²')\n#        \n        plt.savefig(os.path.join(results_path, 'OBJ_plots_%s_%s.png' % (fname, period)), dpi=150)\n        plt.close(fig1)\n\n        \n    def plot_FDC(self, fname, simulated, observed, year, ylim, results_path):\n        '''\n            Calculates and plots a flow duration curve from x. \n\n        All observations/simulations are ordered and the empirical probability is\n        calculated. This is then plotted as a flow duration curve. \n\n        When x has more than one dimension along axis, a range flow duration curve \n        is plotted. This means that for every probability a min and max flow is \n        determined\n        '''\n        \n        sort_sim = np.sort(simulated)[::-1]\n        exceedence_simulated = np.arange(1.,len(sort_sim)+1) / len(sort_sim)\n        plt.plot(exceedence_simulated*100, sort_sim, label='simulated')\n        plt.xlabel(\"Exceedence [%]\")\n        plt.ylabel(\"Flow rate\")\n        try:\n            sort_observed = np.sort(observed)[::-1]\n            exceedence_observed = np.arange(1.,len(sort_observed)+1) / len(sort_observed)\n            plt.plot(exceedence_observed*100, sort_observed, label='observed')\n        except:\n            pass\n        try:\n            fname = \"%s_%s\" % (fname,year)\n        except:\n            pass\n        try:\n            plt.ylim(0,ylim)\n        except:\n            pass\n        plt.xlabel(\"Exceedence [%]\")\n        plt.ylabel(\"Flow rate [m³/s]\")\n        plt.legend()\n        plt.grid()\n        plt.show()\n        plt.savefig(os.path.join(results_path, 'FDC_%s.png' % fname), format=\"png\")\n        plt.close()\n    \n    def plot_FDC_single(self, fname, simulated, year, ylim, results_path):\n        '''\n        Calculates and plots a flow duration curve from x. \n\n        All simulations are ordered and the empirical probability is\n        calculated. This is then plotted as a flow duration curve. \n\n        When x has more than one dimension along axis, a range flow duration curve \n        is plotted. This means that for every probability a min and max flow is \n        determined\n        '''\n        \n        sort_sim = np.sort(simulated)[::-1]\n        exceedence_simulated = np.arange(1.,len(sort_sim)+1) / len(sort_sim)\n        plt.plot(exceedence_simulated*100, sort_sim, label='simulated')\n        plt.xlabel(\"Exceedence [%]\")\n        plt.ylabel(\"Flow rate\")\n        try:\n            fname = \"%s_%s\" % (fname,year)\n        except:\n            pass\n        try:\n            plt.ylim(0,ylim+0.1)\n        except:\n            pass\n        plt.xlabel(\"Exceedence [%]\")\n        plt.ylabel(\"Flow rate [m³/s]\")\n        plt.legend()\n        plt.grid()\n        plt.show()\n        plt.savefig(os.path.join(results_path, 'FDC_%s.png' % fname), format=\"png\")\n        plt.close()\n    \n    def make_plot(self, fname, observed, df_sim, r_squared, NSE, results_path, rain=None):\n        \"\"\"\n        plots the simulation results and observed flow as lineplot\n        \n        @params: \n            fname = str - Name of project\n            observed = list - observed\n            simulated = pd.DataFrame - simulated\n            r_squared = float - r² value (used for box information)\n            NSE = float - NSE value (used for box information)\n            rain = pd.DataFrame - rainfall timeseries\n      \n        \"\"\"        \n        #read climate data\n        # get\n        begin = df_sim.index[0]\n        end = df_sim.index[-1]\n        \n        \n        fig = plt.figure(figsize=(10,7))\n        \n        \n            \n        try:\n            rain = rain[(rain.index>=pd.Timestamp(begin))&(rain.index<=pd.Timestamp(end))]\n            if rain.empty == False:\n            \n                r = rain.resample(\"24H\").sum()/24\n            \n                #plot rainfall\n                ax1 = fig.add_axes([0.1,0.7,0.8,0.2]) # x,y, lenght, height\n                ax1.bar(r.index, r.rain.values,align='center',color=\"k\")\n                ax1.invert_yaxis()\n                ax1.xaxis.tick_top()\n                ax1.xaxis.set_ticks_position('both') # THIS IS THE ONLY CHANGE\n                ax1.xaxis_date()\n                ax1.grid(True)\n                ax1.spines['bottom'].set_color('none')\n                ax1.xaxis.set_ticks_position('top')\n                ax1.set_ylabel(\"Rain [mm day$^{-1}$]\")\n                ax1.set_xlim(pd.Timestamp(begin),pd.Timestamp(end))    \n                ax1.yaxis.tick_right()\n                ax1.yaxis.set_label_position(\"right\")\n                legend_rain = mlines.Line2D([],[],color=\"k\",label = \"Rainfall\")\n        except:\n            pass\n        \n        \n        #plot strea, flow\n        ax = fig.add_axes([0.1,0.2,0.8,0.5]) # x,y, lenght, height\n        ax.plot(df_sim.index,observed.values,color=\"b\",linestyle=\"--\",label=\"Observed\")\n\n        ax.plot(df_sim.index,df_sim.values,color=\"r\",label=\"Simulated\", alpha=.8)\n        \n        # important if only simulated is available\n\n        \n        ax.set_xlim(pd.Timestamp(begin),pd.Timestamp(end))\n        ymax = max(observed.values)+.1\n        ax.set_ylim(0,ymax)\n        ax.set_ylabel(\"Flow [m$^3$ sec$^{-1}$]\")\n        ax.set_yticks(np.arange(0,ymax,0.25))\n        ax.grid(True)\n        \n        ax.spines['top'].set_color('none')\n        ax.xaxis.set_ticks_position('bottom')\n        #plot legend\n        ax5  = fig.add_axes([0.1,0.1,0.8,0.1])# x,y, lenght, height\n        \n        legend_sim = mlines.Line2D([],[],color=\"red\",label=\"Simulated\")\n        legend_obs = mlines.Line2D([],[],color=\"blue\",label = \"Observed\")\n        \n        \n        \n        try:\n            ax5.legend(handles=[legend_sim,legend_obs,legend_rain], ncol=3,bbox_to_anchor=(0.00, 0.5, 1., .102),fontsize=10.,frameon=True)\n        except:\n            ax5.legend(handles=[legend_sim,legend_obs], ncol=2,bbox_to_anchor=(0.00, 0.5, 1., .102),fontsize=10.,frameon=True)\n\n            \n        ax5.axis(\"off\")        \n        ax5.text(0.2, 0.1,\"Gauging station\"  + \"\\n\"+ \"r$^2$: \"+\"%.2f NSE: %.2f\"%(r_squared,NSE),\n                verticalalignment='bottom', horizontalalignment='left',\n                transform=ax5.transAxes,\n                color='k', fontsize=9, \n                bbox=dict(facecolor='0.7', edgecolor='None', boxstyle='round,pad=.5',alpha=.5))\n        \n        fig.autofmt_xdate() \n        fig.savefig(os.path.join(results_path, '%s_Plot.png' % fname), dpi=800)\n        plt.close('all')\n        del fig\n            \n        \n    def make_plot_single(self, fname, df_sim, results_path, rain=None):\n        \"\"\"\n        plots the simulation results and observed flow as lineplot\n        \n        @params: \n            fname = str - Name of project\n            observed = list - observed\n            simulated = pd.DataFrame - simulated\n            r_squared = float - r² value (used for box information)\n            NSE = float - NSE value (used for box information)\n            rain = pd.DataFrame - rainfall timeseries\n      \n        \"\"\"       \n        #read climate data\n        # get\n        begin = df_sim.index[0]\n        end = df_sim.index[-1]\n                     \n        try:\n            \n            rain = rain[(rain.index>=pd.Timestamp(begin))&(rain.index<=pd.Timestamp(end))]\n            if rain.empty == False:\n                fig = plt.figure(figsize=(10,7))\n                r = rain.resample(\"24H\").sum()/24\n                             \n                #plot rainfall\n                ax1 = fig.add_axes([0.1,0.7,0.8,0.2]) # x,y, lenght, height\n                ax1.bar(r.index, r.rain.values,align='center',color=\"k\", label=\"Rainfall\")\n                ax1.invert_yaxis()\n                ax1.xaxis.tick_top()\n                ax1.xaxis.set_ticks_position('both') \n                ax1.xaxis_date()\n                ax1.grid(True)\n                ax1.spines['bottom'].set_color('none')\n                ax1.xaxis.set_ticks_position('top')\n                ax1.set_ylabel(\"Rain [mm day$^{-1}$]\")\n                ax1.set_xlim(pd.Timestamp(begin),pd.Timestamp(end))    \n                ax1.yaxis.tick_right()\n                ax1.yaxis.set_label_position(\"right\")\n                ax = fig.add_axes([0.1,0.2,0.8,0.5])\n                ax.spines['top'].set_color('none')\n            else:\n                fig = plt.figure(figsize=(10,5))\n                ax = fig.add_subplot(1, 1, 1)\n            \n        except:\n            fig = plt.figure(figsize=(10,5))\n            ax = fig.add_subplot(1, 1, 1)\n            \n        ax.plot(df_sim.index,df_sim.values,color=\"b\",label=\"Simulated\")\n               \n        ax.set_xlim(pd.Timestamp(begin),pd.Timestamp(end))\n        ymax = max(df_sim.values)+.1\n        ax.set_ylim(0,ymax)\n        ax.set_ylabel(\"Flow [m$^3$ sec$^{-1}$]\")\n#        ax.set_yticks(np.arange(0,ymax,0.25))\n        ax.grid(True)\n                \n        ax.xaxis.set_ticks_position('bottom')\n\n        try: \n            ax1.legend()\n            ax.legend()\n        except:\n            ax.legend()\n            \n            \n        fig.autofmt_xdate() \n        fig.savefig(os.path.join(results_path, '%s_Plot.png' % fname), dpi=800)\n        plt.close('all')\n        del fig\n        \n    def calcBasicSig(self, simulated):\n        \"\"\"\n        calculates the basic signatures using the SPOTPY libary\n        \n        @params: simulated = simulated flow as list\n        \"\"\"\n    \n        MeanFlow = sig.get_mean(simulated)\n    \n        Skewness = sig.get_skewness(simulated)\n        \n        Q001 = sig.get_q0_01(simulated)\n        Q01 = sig.get_q0_1(simulated)    \n        Q1 = sig.get_q1(simulated)\n        Q5 = sig.get_q5(simulated)\n        Q10 = sig.get_q10(simulated)\n        Q20 = sig.get_q20(simulated)\n        Q85 = sig.get_q85(simulated)\n        Q95 = sig.get_q95(simulated)\n        Q99 = sig.get_q99(simulated)\n        SFDC = sig.get_sfdc(simulated)\n        QVC = sig.get_qcv(simulated)\n        QHF = sig.get_qhf(simulated)\n        QLF = sig.get_qlf(simulated)\n        QLV = sig.get_qlv(simulated)\n        recession = sig.get_recession(simulated)\n        zero_q = sig.get_zero_q_freq(simulated)\n        baseflow_index = sig.get_bfi(simulated)\n        \n        return MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99,SFDC, QVC,QHF, QLF,QLV, recession,zero_q, baseflow_index\n\n\n\n    def objectivefunction(self, observed, simulated):\n        \"\"\"\n        calculates the objective functions using the SPOTPY libary\n        \n        @params: \n            observed = observed flow as list\n            simulated = simulated flow as list\n        \"\"\"\n        NSE = obj.nashsutcliffe(observed, simulated)\n        bias = obj.bias(observed, simulated)\n        r2 = obj.rsquared(observed, simulated)\n        return NSE, bias, r2\n\n               \n    def calc_signatures(self, simulated_inp, observed_inp, gaugingstation, fname, results_path, rain=None):\n        \"\"\"\n        calculates all basic signatures using the SPOTPY libary\n        \n        @params: \n            simulated_inp = pd.DataFrame - simulated values\n            observed_inp = pd.DataFrame - observed values\n            gaugingstation = str - Reach_ID used for selection in simulation DF\n            fname = str - projekt name\n            results_path = str - path where results are stored\n            rain = pd DataFrame - rainfall data\n        \"\"\"\n\n        print('compare_to_observed')\n        \n        new = []  \n        df_sim = simulated_inp.xs(gaugingstation,level=1) \n\n        df_sim = df_sim.flow.resample('D').mean()/self.conversion_factor\n                \n        observed = observed_inp.xs(gaugingstation,level=1)            \n        observed = observed.resample('D').mean()/self.conversion_factor\n        \n        observed = observed[(observed.index>=pd.Timestamp(df_sim.index[0]))&(observed.index<=pd.Timestamp(df_sim.index[-1]))]\n              \n        observed_values = observed.flow.values\n        \n        simulated = df_sim.values.tolist()\n        ylim = max(simulated)\n        year='All'\n              \n        self.plot_FDC(fname, simulated, observed_values, year, ylim, results_path)\n        \n        print(len(observed_values), len(simulated))\n            \n        NSE, bias, r2 = self.objectivefunction(observed_values, simulated)\n            \n        self.make_plot(fname, observed, df_sim, r2, NSE, results_path, rain)\n       \n        MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99, SFDC, QVC, QHF, QLF, QLV, recession,zero_q, baseflow_index  = self.calcBasicSig(simulated)\n                                 \n        new.append([fname, NSE, bias, r2, MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99,SFDC,QVC,QHF,QLF,QLV,recession,zero_q,baseflow_index])\n        del df_sim, simulated, observed, observed_values, NSE, bias, r2\n            \n        columns = ['run_name','NSE', 'bias', 'r2', 'MeanFlow', 'Skewness', 'Q001', 'Q01', 'Q1', 'Q5', 'Q10', 'Q20', 'Q85', 'Q95', 'Q99','SFDC','QVC','QHF','QLF','QLV','recession', 'zero_q','baseflow_index']\n        res = pd.DataFrame(new, columns=columns)\n                        \n        res.to_csv(os.path.join(results_path, 'Signatures_%s.csv' % fname), index=None)\n        del new\n           \n\n    def calc_yearly_TS(self, simulated_inp, observed_inp, gaugingstation, fname, results_path):\n       \n        \"\"\"\n        calculates all basic signatures using the SPOTPY libary and plots flow duration curves for each year\n        \n        @params: \n            simulated_inp = pd.DataFrame - simulated values\n            observed_inp = pd.DataFrame - observed values\n            gaugingstation = str - Reach_ID used for selection in simulation DF\n            fname = str - projekt name\n            results_path = str - path where results are stored\n            rain = pd DataFrame - rainfall data\n        \"\"\"\n        print('calc_yearly_TS')\n                \n        new = []  \n\n        NSE_val = []\n        bias_val = []\n        r2_val = []\n\n        df_sim = simulated_inp.xs(gaugingstation,level=1)        \n        df_sim = df_sim.flow.resample('D').mean()/self.conversion_factor\n        \n        ylim = max(df_sim.values.tolist())\n                   \n        years = df_sim.index.year.unique()           \n        for year in years[:-1]:\n\n            next_year = year + 1\n            \n            df_calc = df_sim['%s-%s'% (year,self.hyd_year_start) : '%s-09' % next_year]\n            \n            observed = observed_inp.xs(gaugingstation,level=1)            \n            observed = observed.resample('D').mean()/self.conversion_factor\n            observed = observed[(observed.index>=pd.Timestamp(df_calc.index[0]))&(observed.index<=pd.Timestamp(df_calc.index[-1]))]\n            observed = observed.flow.values\n            \n            print(df_calc.dtypes)\n            simulated = df_calc.values.tolist()\n            \n            self.plot_FDC(fname, simulated, observed, year, ylim, results_path)\n            \n            NSE, bias, r2 = self.objectivefunction(observed, simulated)\n            MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99, SFDC, QVC, QHF, QLF, QLV, recession,zero_q, baseflow_index  = self.calcBasicSig(simulated)                \n            \n            print(year, len(observed), len(simulated), 'NSE:', NSE, 'bias:', bias, 'r²:', r2)\n           \n            NSE_val.append([year, NSE])\n            bias_val.append([year, bias])\n            r2_val.append([year, r2])\n                                               \n            new.append([fname, year, NSE, bias, r2, MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99,SFDC,QVC,QHF,QLF,QLV,recession,zero_q,baseflow_index])\n            del df_calc, NSE, bias, r2, observed, simulated, year\n            \n        \n        del NSE_val, bias_val, r2_val\n                   \n        columns = ['run_name','Year', 'NSE', 'bias', 'r2', 'MeanFlow', 'Skewness', 'Q001', 'Q01', 'Q1', 'Q5', 'Q10', 'Q20', 'Q85', 'Q95', 'Q99','SFDC','QVC','QHF','QLF','QLV','recession', 'zero_q','baseflow_index']\n        res = pd.DataFrame(new, columns=columns)\n                        \n        res.to_csv(os.path.join(results_path, '%s_yearly_TS_to_obs.csv' % fname), index=None)\n    \n    \n    def calc_signatures_sim_only(self, simulated_inp, gaugingstation, fname, results_path, rain=None):\n        \"\"\"\n        calculates basic signatures for simulation timeseries without gauging data comparison using the SPOTPY libary\n        \n        @params: \n            simulated_inp = pd.DataFrame - simulated values\n            gaugingstation = str - Reach_ID used for selection in simulation DF\n            fname = str - projekt name\n            results_path = str - path where results are stored\n            rain = pd DataFrame - rainfall data\n        \"\"\"\n\n        print('calculate simulation signatures')\n        \n        new = []  \n        df_sim = simulated_inp.xs(gaugingstation,level=1) \n\n        df_sim = df_sim.flow.resample('D').mean()/self.conversion_factor\n                \n        \n        simulated = df_sim.values.tolist()\n        ylim = max(simulated)\n        year='All'\n              \n        self.plot_FDC_single(fname, simulated, year, ylim, results_path)\n                   \n        self.make_plot_single(fname, df_sim, results_path, rain)\n       \n        MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99, SFDC, QVC, QHF, QLF, QLV, recession,zero_q, baseflow_index  = self.calcBasicSig(simulated)\n                                 \n        new.append([fname,MeanFlow, Skewness, Q001, Q01, Q1, Q5, Q10, Q20, Q85, Q95, Q99,SFDC,QVC,QHF,QLF,QLV,recession,zero_q,baseflow_index])\n        del df_sim, simulated, ylim, year \n            \n        columns = ['run_name','MeanFlow', 'Skewness', 'Q001', 'Q01', 'Q1', 'Q5', 'Q10', 'Q20', 'Q85', 'Q95', 'Q99','SFDC','QVC','QHF','QLF','QLV','recession', 'zero_q','baseflow_index']\n        res = pd.DataFrame(new, columns=columns)\n                        \n        res.to_csv(os.path.join(results_path, 'Signatures_%s_single.csv' % fname), index=None)\n        del new\n","repo_name":"xlandscape/CmfContinuous-Component","sub_path":"module/bin/LandscapeModel/utils/Signatures.py","file_name":"Signatures.py","file_ext":"py","file_size_in_byte":20010,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15019283416","text":"# coding: utf-8\n\nimport numpy as np\nfrom collections import defaultdict\n\ndef dedup(coordinates, lines, rings):\n\n    arc_index = defaultdict(list)\n    arcs = list()\n\n    def minimum_offset(arc):\n\n        mid = arc.a\n        minimum = mid\n        minimum_point = coordinates[mid]\n\n        while mid < arc.b-1:\n            mid += 1\n            point = coordinates[mid]\n            if point[0] < minimum_point[0] or point[0] == minimum_point[0] and point[1] < minimum_point[1]:\n                minimum = mid\n                minimum_point = point\n\n        return minimum - arc.a\n\n    def equal_line(x, y):\n\n        n = x.b - x.a\n        if n != (y.b - y.a):\n            return False\n\n        return np.all([ coordinates[ x.a + i ] == coordinates[ y.a + i ] for i in range(n+1) ])\n\n    def equal_line_reverse(x, y):\n\n        n = x.b - x.a\n        if n != (y.b - y.a):\n            return False\n\n        return np.all([ coordinates[ x.a + i ] == coordinates[ y.b - i ]\n                     for i in range(n+1) ])\n\n    def equal_ring(x, y):\n\n        n = x.b - x.a\n        if n != (y.b - y.a):\n            return False\n\n        offset_x = minimum_offset(x)\n        offset_y = minimum_offset(y)\n\n        return np.all([ coordinates[ x.a + ((i + offset_x) % n) ] == coordinates[ y.a + ((i + offset_y) % n) ]\n                     for i in range(n+1) ])\n\n    def equal_ring_reverse(x, y):\n\n        n = x.b - x.a\n        if n != (y.b - y.a):\n            return False\n\n        offset_x = minimum_offset(x)\n        offset_y = n - minimum_offset(y)\n\n        return np.all([ coordinates[ x.a + ((i + offset_x) % n) ] == coordinates[ y.b - ((i + offset_y) % n) ]\n                     for i in range(n+1) ])\n\n    def dedup_line(arc):\n\n        for other_arc in arc_index[tuple(coordinates[arc.a])]:\n            if equal_line(arc, other_arc):\n                arc.a = other_arc.a\n                arc.b = other_arc.b\n                return\n\n        for other_arc in arc_index[tuple(coordinates[arc.b])]:\n            if equal_line_reverse(arc, other_arc):\n                arc.a = other_arc.b\n                arc.b = other_arc.a\n                return\n\n        arc_index[tuple(coordinates[arc.a])].append(arc)\n        arc_index[tuple(coordinates[arc.b])].append(arc)\n        \n        arcs.append(arc)\n\n    def dedup_ring(arc):\n\n        for other_arc in arc_index[tuple(coordinates[arc.a])]:\n            if equal_line(arc, other_arc):\n                arc.a = other_arc.a\n                arc.b = other_arc.b\n                return\n            if equal_line_reverse(arc, other_arc):\n                arc.a = other_arc.b\n                arc.b = other_arc.a\n                return\n\n        offset = minimum_offset(arc)\n\n        for other_arc in arc_index[tuple(coordinates[arc.a + offset])]:\n            if equal_ring(arc, other_arc):\n                arc.a = other_arc.a\n                arc.b = other_arc.b\n                return\n            if equal_ring_reverse(arc, other_arc):\n                arc.a = other_arc.b\n                arc.b = other_arc.a\n                return\n            \n        arc_index[tuple(coordinates[arc.a + offset])].append(arc)\n\n        arcs.append(arc)\n\n    for line in lines:\n        while line:\n            dedup_line(line)\n            line = line.next\n\n    for ring in rings:\n        if ring.next:\n            while ring:\n                dedup_line(ring)\n                ring = ring.next\n        else:\n            dedup_ring(ring)\n\n    return arcs","repo_name":"tramebleue/python-topojson","sub_path":"topojson/dedup.py","file_name":"dedup.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"20868769114","text":"import csv\nimport random\n\nimport numpy as np\nimport torch\n\nfrom modelzoo.common.pytorch.input_utils import (\n    bucketed_batch,\n    get_streaming_batch_size,\n)\nfrom modelzoo.transformers.pytorch.bert.input.utils import (\n    build_vocab,\n    create_masked_lm_predictions,\n    get_meta_data,\n    parse_text,\n)\nfrom modelzoo.transformers.pytorch.input_utils import (\n    get_data_for_task,\n    num_tasks,\n    shard_list_interleaved,\n    task_id,\n)\n\n\nclass BertCSVDynamicMaskDataProcessor(torch.utils.data.IterableDataset):\n    \"\"\"\n    Reads csv files containing the input text tokens, adds MLM features\n    on the fly.\n    :param <dict> params: dict containing input parameters for creating dataset.\n    Expects the following fields:\n\n    - \"data_dir\" (string): path to the data files to use.\n    - \"batch_size\" (int): Batch size.\n    - \"shuffle\" (bool): Flag to enable data shuffling.\n    - \"shuffle_seed\" (int): Shuffle seed.\n    - \"shuffle_buffer\" (int): Shuffle buffer size.\n    - \"mask_whole_word\" (bool): Flag to whether mask the entire word.\n    - \"do_lower\" (bool): Flag to lower case the texts.\n    - \"dynamic_mlm_scale\" (bool): Flag to dynamically scale the loss.\n    - \"num_workers\" (int):  How many subprocesses to use for data loading.\n    - \"drop_last\" (bool): If True and the dataset size is not divisible\n       by the batch size, the last incomplete batch will be dropped.\n    - \"prefetch_factor\" (int): Number of samples loaded in advance by each worker.\n    - \"persistent_workers\" (bool): If True, the data loader will not shutdown\n       the worker processes after a dataset has been consumed once.\n    - \"oov_token\" (string): Out of vocabulary token.\n    - \"mask_token\" (string): Mask token.\n    - \"document_separator_token\" (string): Seperator token.\n    - \"exclude_from_masking\" list(string): tokens that should be excluded from being masked.\n    - \"max_sequence_length\" (int): Maximum length of the sequence to generate.\n    - \"max_predictions_per_seq\" (int): Maximum number of masked tokens per sequence.\n    - \"masked_lm_prob\" (float): Ratio of the masked tokens over the sequence length.\n    - \"gather_mlm_labels\" (bool): Flag to gather mlm labels.\n    - \"mixed_precision\" (bool): Casts input mask to fp16 if set to True.\n      Otherwise, the generated mask is float32.\n    \"\"\"\n\n    def __init__(self, params):\n        super(BertCSVDynamicMaskDataProcessor, self).__init__()\n\n        # Input params.\n        self.meta_data = get_meta_data(params[\"data_dir\"])\n\n        self.meta_data_values = list(self.meta_data.values())\n        self.meta_data_filenames = list(self.meta_data.keys())\n        # Please note the appending of [0]\n        self.meta_data_values_cum_sum = np.cumsum([0] + self.meta_data_values)\n\n        self.num_examples = sum(map(int, self.meta_data.values()))\n        self.disable_nsp = params.get(\"disable_nsp\", False)\n        self.batch_size = get_streaming_batch_size(params[\"batch_size\"])\n\n        self.num_batches = self.num_examples // self.batch_size\n        assert (\n            self.num_batches > 0\n        ), \"Dataset does not contain enough samples for one batch. Please choose a smaller batch size\"\n\n        self.num_tasks = num_tasks()\n        self.task_id = task_id()\n        self.num_batch_per_task = self.num_batches // self.num_tasks\n\n        assert (\n            self.num_batch_per_task > 0\n        ), \"Dataset cannot be evenly distributed across the given tasks. Please choose fewer tasks to run with\"\n\n        self.num_examples_per_task = self.num_batch_per_task * self.batch_size\n        self.files_in_task = get_data_for_task(\n            self.task_id,\n            self.meta_data_values_cum_sum,\n            self.num_examples_per_task,\n            self.meta_data_values,\n            self.meta_data_filenames,\n        )\n\n        self.shuffle = params.get(\"shuffle\", True)\n        self.shuffle_seed = params.get(\"shuffle_seed\", None)\n        self.shuffle_buffer = params.get(\"shuffle_buffer\", 10 * self.batch_size)\n        self.mask_whole_word = params.get(\"mask_whole_word\", False)\n        self.do_lower = params.get(\"do_lower\", False)\n        self.dynamic_mlm_scale = params.get(\"dynamic_mlm_scale\", False)\n        self.buckets = params.get(\"buckets\", None)\n\n        # Multi-processing params.\n        self.num_workers = params.get(\"num_workers\", 0)\n        self.drop_last = params.get(\"drop_last\", True)\n        self.prefetch_factor = params.get(\"prefetch_factor\", 10)\n        self.persistent_workers = params.get(\"persistent_workers\", True)\n\n        # Get special tokens and tokens that should not be masked.\n        self.special_tokens = {\n            \"oov_token\": params.get(\"oov_token\", \"[UNK]\"),\n            \"mask_token\": params.get(\"mask_token\", \"[MASK]\"),\n            \"document_separator_token\": params.get(\n                \"document_separator_token\", \"[SEP]\"\n            ),\n        }\n        self.exclude_from_masking = params.get(\n            \"exclude_from_masking\", [\"[CLS]\", \"[SEP]\", \"[PAD]\", \"[MASK]\"]\n        )\n\n        if self.do_lower:\n            self.special_tokens = {\n                key: value.lower() for key, value in self.special_tokens.items()\n            }\n\n            self.exclude_from_masking = list(\n                map(lambda token: token.lower(), self.exclude_from_masking)\n            )\n\n        # Get vocab file and size.\n        self.vocab_file = params[\"vocab_file\"]\n        self.vocab, self.vocab_size = build_vocab(\n            self.vocab_file, self.do_lower, self.special_tokens[\"oov_token\"]\n        )\n\n        # Init tokenizer.\n        self.tokenize = self.vocab.forward\n\n        # Getting indices for special tokens.\n        self.special_tokens_indices = {\n            key: self.tokenize([value])[0]\n            for key, value in self.special_tokens.items()\n        }\n\n        self.exclude_from_masking_ids = [\n            self.tokenize([token])[0] for token in self.exclude_from_masking\n        ]\n\n        # We create a pool with tokens that can be used to randomly replace input tokens\n        # for BERT MLM task.\n        self.replacement_pool = list(\n            set(range(self.vocab_size)) - set(self.exclude_from_masking_ids)\n        )\n\n        # Padding indices.\n        # See https://huggingface.co/transformers/glossary.html#labels.\n        self.labels_pad_id = params.get(\"labels_pad_id\", 0)\n        self.input_pad_id = params.get(\"input_pad_id\", 0)\n        self.attn_mask_pad_id = params.get(\"attn_mask_pad_id\", 0)\n        if not self.disable_nsp:\n            self.segment_pad_id = params.get(\"segment_pad_id\", 0)\n\n        # Max sequence lengths size params.\n        self.max_sequence_length = params[\"max_sequence_length\"]\n        self.max_predictions_per_seq = params[\"max_predictions_per_seq\"]\n        self.masked_lm_prob = params.get(\"masked_lm_prob\", 0.15)\n        self.gather_mlm_labels = params.get(\"gather_mlm_labels\", True)\n\n        # Store params.\n        self.data_buffer = []\n        self.csv_files_per_task_per_worker = []\n        self.processed_buffers = 0\n\n    def load_buffer(self):\n        \"\"\"\n        Generator to read the data in chunks of size of `data_buffer`.\n\n        :returns: Yields the data stored in the `data_buffer`.\n\n        \"\"\"\n        self.processed_buffers = 0\n        self.data_buffer = []\n\n        while self.processed_buffers < len(self.csv_files_per_task_per_worker):\n            (\n                current_file_path,\n                num_examples,\n                start_id,\n            ) = self.csv_files_per_task_per_worker[self.processed_buffers]\n\n            with open(current_file_path, \"r\", newline=\"\") as fin:\n                data_reader = csv.DictReader(fin)\n\n                for row_id, row in enumerate(data_reader):\n                    if start_id <= row_id < start_id + num_examples:\n                        self.data_buffer.append(row)\n                    else:\n                        continue\n\n                    if len(self.data_buffer) == self.shuffle_buffer:\n                        if self.shuffle:\n                            self.rng.shuffle(self.data_buffer)\n\n                        for ind in range(len(self.data_buffer)):\n                            yield self.data_buffer[ind]\n                        self.data_buffer = []\n\n                self.processed_buffers += 1\n\n        if self.shuffle:\n            self.rng.shuffle(self.data_buffer)\n\n        for ind in range(len(self.data_buffer)):\n            yield self.data_buffer[ind]\n        self.data_buffer = []\n\n    def __len__(self):\n        # Returns the len of dataset on the task process\n        if not self.drop_last:\n            return (\n                self.num_examples_per_task + self.batch_size - 1\n            ) // self.batch_size\n        elif self.buckets is None:\n            return self.num_examples_per_task // self.batch_size\n        else:\n            # give an under-estimate in case we don't fully fill some buckets\n            length = self.num_examples_per_task // self.batch_size\n            length -= len(self.buckets)\n            return length\n\n    def get_single_item(self):\n        \"\"\"\n        Iterating over the data to construct input features.\n\n        :return: A tuple with training features:\n            * np.array[int.32] input_ids: Numpy array with input token indices.\n                Shape: (`max_sequence_length`).\n            * np.array[int.32] labels: Numpy array with labels.\n               Shape: (`max_sequence_length`).\n            * np.array[int.32] attention_mask\n               Shape: (`max_sequence_length`).\n            * np.array[int.32] token_type_ids: Numpy array with segment indices.\n               Shape: (`max_sequence_length`).\n            * np.array[int.32] next_sentence_label: Numpy array with labels for NSP task.\n               Shape: (1).\n            * np.array[int.32] masked_lm_mask: Numpy array with a mask of\n               predicted tokens.\n               Shape: (`max_predictions`)\n               `0` indicates the non masked token, and `1` indicates the masked token.\n        \"\"\"\n        # Iterate over the data rows to create input features.\n        for data_row in self.load_buffer():\n            # `data_row` is a dict with keys:\n            # [\"tokens\", \"segment_ids\", \"is_random_next\"].\n            tokens = parse_text(data_row[\"tokens\"], do_lower=self.do_lower)\n\n            if self.disable_nsp:\n                # truncate tokens to MSL\n                tokens = tokens[: self.max_sequence_length]\n            else:\n                assert (\n                    len(tokens) <= self.max_sequence_length\n                ), \"When using NSP head, make sure that len(tokens) <= MSL.\"\n\n            (\n                input_ids,\n                labels,\n                attention_mask,\n                masked_lm_mask,\n            ) = create_masked_lm_predictions(\n                tokens,\n                self.max_sequence_length,\n                self.special_tokens_indices[\"mask_token\"],\n                self.max_predictions_per_seq,\n                self.input_pad_id,\n                self.attn_mask_pad_id,\n                self.labels_pad_id,\n                self.tokenize,\n                self.vocab_size,\n                self.masked_lm_prob,\n                self.rng,\n                self.exclude_from_masking,\n                self.mask_whole_word,\n                self.replacement_pool,\n            )\n            features = {\n                \"input_ids\": input_ids,\n                \"attention_mask\": attention_mask,\n            }\n            if self.gather_mlm_labels:\n                # Gather MLM positions\n                _mlm_positions = np.nonzero(masked_lm_mask)[0]\n                _num_preds = len(_mlm_positions)\n                gathered_mlm_positions = np.zeros(\n                    (self.max_predictions_per_seq,), dtype=np.int32\n                )\n                gathered_mlm_positions[:_num_preds] = _mlm_positions\n                gathered_labels = np.zeros(\n                    (self.max_predictions_per_seq,), dtype=np.int32\n                )\n                gathered_labels[:_num_preds] = labels[_mlm_positions]\n                gathered_mlm_mask = np.zeros(\n                    (self.max_predictions_per_seq,), dtype=np.int32\n                )\n                gathered_mlm_mask[:_num_preds] = masked_lm_mask[_mlm_positions]\n                features[\"labels\"] = gathered_labels\n                features[\"masked_lm_mask\"] = gathered_mlm_mask\n                features[\"masked_lm_positions\"] = gathered_mlm_positions\n            else:\n                features[\"labels\"] = labels\n                features[\"masked_lm_mask\"] = masked_lm_mask\n\n            if not self.disable_nsp:\n                next_sentence_label = np.zeros((1,), dtype=np.int32)\n\n                token_type_ids = (\n                    np.ones((self.max_sequence_length,), dtype=np.int32)\n                    * self.segment_pad_id\n                )\n\n                segment_ids = data_row[\"segment_ids\"].strip(\"[]\").split(\", \")\n                token_type_ids[: len(segment_ids)] = list(map(int, segment_ids))\n                next_sentence_label[0] = int(data_row[\"is_random_next\"])\n                features[\"token_type_ids\"] = token_type_ids\n                features[\"next_sentence_label\"] = next_sentence_label\n\n            yield features\n\n    def __iter__(self):\n        batched_dataset = bucketed_batch(\n            self.get_single_item(),\n            self.batch_size,\n            buckets=self.buckets,\n            element_length_fn=lambda feats: np.sum(feats[\"attention_mask\"]),\n            drop_last=self.drop_last,\n            seed=self.shuffle_seed,\n        )\n        for batch in batched_dataset:\n            if self.dynamic_mlm_scale:\n                scale = self.batch_size / torch.sum(batch[\"masked_lm_mask\"])\n                batch[\"mlm_loss_scale\"] = scale.expand(self.batch_size, 1)\n            yield batch\n\n    def _worker_init_fn(self, worker_id):\n        worker_info = torch.utils.data.get_worker_info()\n\n        if worker_info is not None:\n            worker_id = worker_info.id\n            num_workers = worker_info.num_workers\n        else:\n            # Single-process\n            worker_id = 0\n            num_workers = 1\n\n        self.processed_buffers = 0\n        if self.shuffle_seed is not None:\n            self.shuffle_seed += worker_id + 1\n        self.rng = random.Random(self.shuffle_seed)\n\n        # Shard the data across multiple processes.\n        self.csv_files_per_task_per_worker = shard_list_interleaved(\n            self.files_in_task, worker_id, num_workers\n        )\n        if self.shuffle:\n            self.rng.shuffle(self.csv_files_per_task_per_worker)\n\n    def create_dataloader(self):\n        \"\"\"\n        Classmethod to create the dataloader object.\n        \"\"\"\n        if self.num_workers:\n            dataloader = torch.utils.data.DataLoader(\n                self,\n                batch_size=None,\n                num_workers=self.num_workers,\n                prefetch_factor=self.prefetch_factor,\n                persistent_workers=self.persistent_workers,\n                worker_init_fn=self._worker_init_fn,\n            )\n        else:\n            dataloader = torch.utils.data.DataLoader(self, batch_size=None)\n            self._worker_init_fn(0)\n\n        return dataloader\n","repo_name":"Cerebras/modelzoo","sub_path":"modelzoo/transformers/pytorch/bert/input/BertCSVDynamicMaskDataProcessor.py","file_name":"BertCSVDynamicMaskDataProcessor.py","file_ext":"py","file_size_in_byte":15196,"program_lang":"python","lang":"en","doc_type":"code","stars":747,"dataset":"github-code","pt":"77"}
+{"seq_id":"41753509585","text":"import pickle\nimport argparse\nimport numpy as np\nfrom inputFormat import *\nfrom network import network\nfrom theano import tensor as T\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"source\", type=str, help=\"Pickled network to steal params from.\")\nparser.add_argument(\"dest\", type=str, help=\"File to place new network in.\")\nparser.add_argument(\"--cpu\", \"-c\", dest=\"cpu\", action='store_const',\n\t\t\t\t\tconst=True, default=False,\n\t\t\t\t\thelp=\"Convert network to run on a CPU.\")\nargs = parser.parse_args()\n\nprint(\"loading model...\")\nf = open(args.source, 'rb')\nold_network = pickle.load(f)\nf.close()\n\nparams = old_network.params\nif args.cpu:\n\tprint(\"converting gpu parameters...\")\n\tnew_params=[]\n\tfor param in params:\n\t\tparam = T._shared(param.get_value())\n\t\tnew_params.append(param)\n\tparams = new_params\n\nnew_network = network(batch_size=None, params = params)\n\nprint(\"saving model...\")\nf = open(args.dest, 'wb')\npickle.dump(new_network, f, protocol=pickle.HIGHEST_PROTOCOL)\nf.close()","repo_name":"Upjaka/Neurohex","sub_path":"fix_network.py","file_name":"fix_network.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11433607628","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom perlin_noise import PerlinNoise\nfrom opensimplex import OpenSimplex\nfrom scipy import interpolate as interpolation\nimport logging\nlogging.basicConfig(level=logging.WARNING, format='%(levelname)-8s [%(filename)s:%(lineno)d] %(message)s')\n\n\nclass TerrainGenerator:\n    \"\"\"This class generates terrain in 3D\n    \"\"\"\n\n    def __init__(self, dimension=(5, 5), roughness=0.5, algorithm=\"perlin\", seed=0):\n        \"\"\"Intializes the Terrain generator\n\n        Args:\n            dimension (tuple, optional): Dimension of the surface (length x width). Defaults to (5, 5).\n            roughness (float, optional): surface roughness. Defaults to 0.5.\n            algorithm (str, optional): Choice of algorithm 1. perlin 2. open-simplex. Defaults to \"perlin\".\n            seed (int, optional): User defined seed value for generating random numbers. Defaults to 0.\n        \"\"\"\n        try:\n            # verify the the arg\n            if not isinstance(dimension, set):\n                raise TypeError(\n                    \"dimension need to be set. Dimension represents 2D surface of (length,width)\")\n\n            self.perlin_noise = PerlinNoise(octaves=roughness)\n            self.open_simplex_noise = OpenSimplex(seed)\n            self.dimension = dimension\n            self.x_axis, self.y_axis = np.meshgrid(np.linspace(\n                0, dimension[0], dimension[0]), np.linspace(0, dimension[1], dimension[1]))\n            self.z_axis = np.full(shape=dimension, fill_value=1.0, dtype=float)\n            self.height_function = None\n            self.algorithm = algorithm\n        except TypeError as e:\n            logging.critical(e)\n            exit(-1)\n        except Exception as e:\n            logging.critical(e)\n            exit(-1)\n\n    def generate(self):\n        \"\"\"Generates Terrain in 3D\n        \"\"\"\n        try:\n            if self.algorithm == \"perlin\":\n                # 2D array to store pernil-noise\n                perlin_noise_value_buffer2D = []\n                # Generate perlin-noise\n                for i in range(self.dimension[0]):\n                    row = []\n                    for j in range(self.dimension[1]):\n                        noise_val = self.perlin_noise(\n                            [i / self.dimension[0], j / self.dimension[1]])\n                        row.append(noise_val)\n                    perlin_noise_value_buffer2D.append(row)\n\n                # Convert perlin noise value to surface height\n                surface_height = perlin_noise_value_buffer2D\n            elif self.algorithm == \"open-simplex\":\n                # 2D array to store pernil-noise\n                open_simple_noise_value_buffer2D = []\n                # Generate perlin-noise\n                for i in range(self.dimension[0]):\n                    row = []\n                    for j in range(self.dimension[1]):\n                        noise_val = self.open_simplex_noise.noise2d(i, j)\n                        row.append(noise_val)\n                    open_simple_noise_value_buffer2D.append(row)\n                # Convert perlin noise value to surface height\n                surface_height = open_simple_noise_value_buffer2D\n            else:\n                raise RuntimeError(\"Algorithm is not supported\")\n            self.z_axis = np.array([np.array(xi) for xi in surface_height])\n            self.height_function = interpolation.RectBivariateSpline(np.linspace(\n                0, self.dimension[0], self.dimension[0]), np.linspace(0, self.dimension[1], self.dimension[1]), self.z_axis)\n        except RuntimeError as e:\n            logging.critical(e)\n            exit(-1)\n        except Exception as e:\n            logging.critical(e)\n            exit(-1)\n\n    def get_height(self, x, y):\n        \"\"\"gets height of the surface \n\n        Args:\n            x (float): x-axis dimension of the surface\n            y (float): y-axis dimension of the surface\n\n        Returns:\n            float: height of the terrain\n        \"\"\"\n        if self.height_function is not None:\n            height_val = self.height_function(x, y)\n            return np.float(height_val[0][0])\n        else:\n            return 0\n\n\nif __name__ == \"__main__\":\n    area_dimension = (100, 100)\n    surface_roughness = 0.5\n\n    surface_gen = TerrainGenerator(\n        dimension=area_dimension, roughness=surface_roughness)\n    surface_gen.generate()\n\n    fig = plt.figure()\n    ax = plt.axes(projection='3d')\n    ax.plot_wireframe(surface_gen.x_axis, surface_gen.y_axis,\n                      surface_gen.z_axis, color='black')\n    ax.set_title('wireframe')\n    plt.show()\n\n    print(surface_gen.get_height(0, 1))\n","repo_name":"eternalamit5/Indoor-Localisation","sub_path":"hrc/personnel/motion/terrain_gen.py","file_name":"terrain_gen.py","file_ext":"py","file_size_in_byte":4655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15341865589","text":"import os\nimport unittest\n\nfrom stdnum.ch import uid\n\n\n@unittest.skipIf(\n    not os.environ.get('ONLINE_TESTS'),\n    'Do not overload online services')\nclass TestUid(unittest.TestCase):\n    \"\"\"Test the UID Webservice provided by the Swiss Federal Statistical\n    Office for validating UID numbers.\"\"\"\n\n    def test_check_uid(self):\n        \"\"\"Test stdnum.ch.uid.check_uid()\"\"\"\n        result = uid.check_uid('CHE113690319')\n        self.assertTrue(result)\n        self.assertEqual(result['organisation']['organisationIdentification']['uid']['uidOrganisationId'], 113690319)\n        self.assertEqual(result['organisation']['organisationIdentification']['legalForm'], '0220')\n        self.assertEqual(result['vatRegisterInformation']['vatStatus'], '2')\n","repo_name":"arthurdejong/python-stdnum","sub_path":"tests/test_ch_uid.py","file_name":"test_ch_uid.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":453,"dataset":"github-code","pt":"77"}
+{"seq_id":"74580835127","text":"from flask import Flask, render_template, request, redirect, flash\nfrom mysqlconnection import connectToMySQL\napp = Flask(__name__)\napp.secret_key = 'pineapple'\n\n@app.route(\"/\")\ndef index():\n    mysql = connectToMySQL('flask_friends')\n    friends = mysql.query_db('SELECT * FROM friends;')\n    print(friends)\n    return render_template(\"index.html\", all_friends=friends)\n\n@app.route(\"/create_friend\", methods=[\"POST\"])\ndef add_friends_to_db():\n    is_valid = True\n    if len(request.form['fname']) < 1:\n        is_valid = False\n        flash('Please enter a first name.')\n    if len(request.form['lname']) < 1:\n        is_valid = False\n        flash('Please enter a last name.')\n    if len(request.form['occ']) < 2:\n        is_valid = False\n        flash('Occupation must be more than 2 characters')\n\n    if is_valid:\n        mysql = connectToMySQL('flask_friends')\n        query = \"INSERT INTO friends (first_name, last_name, occupation, created_at, updated_at) VALUES (%(fn)s, %(ls)s, %(occup)s, NOW(), NOW());\"\n        data = {\n            \"fn\": request.form[\"fname\"],\n            \"ln\": request.form[\"lname\"],\n            \"occup\": request.form[\"occ\"]\n        }\n        new_friend_id = mysql.query_db(query, data)\n        print(request.form)\n    return redirect('/')\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"codingdojotulsajanuary2019/Skylar_M","sub_path":"Python_Stack/flask/flask_mysql/first_flask_mysql/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15476128797","text":"# -*- coding: utf-8 -*-\n\n__author__ = 'Bruce Frank Wong'\n\n\nfrom typing import Callable\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import pyqtSlot, pyqtSignal, QThread\nimport pyqtgraph as pg\nimport pandas as pd\nimport numpy as np\n\n\n# appropriate types\nColorMap = pg.ColorMap\n\n# module definitions, mostly colors\nlegend_border_color = '#000000dd'\nlegend_fill_color   = '#00000055'\nlegend_text_color   = '#dddddd66'\nsoft_colors = [\n    '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'\n]\nhard_colors = [\n    '#000000', '#772211', '#000066', '#555555', '#0022cc', '#ffcc00'\n]\ncolmap_clash = ColorMap(\n    [0.0, 0.2, 0.6, 1.0],\n    [[127, 127, 255, 51], [0, 0, 127, 51], [255, 51, 102, 51], [255, 178, 76, 51]]\n)\nforeground = '#000000'\nbackground = '#ffffff'\nodd_plot_background = '#f0f0f0'\ncandle_bull_color = '#26a69a'\ncandle_bear_color = '#ef5350'\ncandle_bull_body_color = background\nvolume_bull_color = '#92d2cc'\nvolume_bear_color = '#f7a9a7'\nvolume_bull_body_color = volume_bull_color\nvolume_neutral_color = '#b0b0b0'\npoc_color = '#000060'\nband_color = '#d2dfe6'\ncross_hair_color = '#00000077'\ndraw_line_color = '#000000'\ndraw_done_color = '#555555'\nsignificant_decimals = 8\nsignificant_eps = 1e-8\nmax_zoom_points = 20        # number of visible candles when maximum zoomed in\ntop_graph_scale = 2\nclamp_grid = True\nright_margin_candles = 5    # whitespace at the right-hand side\nside_margin = 0.5\nlod_candles = 3000\nlod_labels = 700\ncache_candle_factor = 3     # factor extra candles rendered to buffer\ny_label_width = 65\nlong_time = 2*365*24*60*60*1e9\ndisplay_timezone = None  # default to local\nwinx, winy, winw, winh = 400, 300, 800, 400\n\napp = None\nwindows = []    # no gc\ntimers = []     # no gc\nsounds = {}     # no gc\nepoch_period = 1e30\nlast_ax = None      # always assume we want to plot in the last axis, unless explicitly specified\noverlay_axs = []    # for keeping track of candlesticks in overlays\nviewrestore = False\nmaster_data = {}\n\n\nclass FinPlotItem(pg.GraphicsObject):\n    def __init__(self, ax, data_source, lod):\n        super().__init__()\n        self.ax = ax\n        self.data_source = data_source\n        self.picture = QtGui.QPicture()\n        self.painter = QtGui.QPainter()\n        self.dirty = True\n        self.lod = lod\n        self.cachedRect = None\n\n    def repaint(self):\n        self.dirty = True\n        self.paint(self.painter)\n\n    def paint(self, p, *args):\n        if self.datasrc.is_sparse:\n            self.dirty = True\n        self.update_dirty_picture(self.viewRect())\n        p.drawPicture(0, 0, self.picture)\n\n    def update_dirty_picture(self, visible_rect):\n        if self.dirty or \\\n            (self.lod and   # regenerate when zoom changes?\n                (visible_rect.left() < self.cachedRect.left() or\n                 visible_rect.right() > self.cachedRect.right() or\n                 visible_rect.width() < self.cachedRect.width() / cache_candle_factor)):     # optimize when zooming in\n            self._generate_picture(visible_rect)\n\n    def _generate_picture(self, boundingRect):\n        w = boundingRect.width()\n        self.cachedRect = QtCore.QRectF(boundingRect.left()-(cache_candle_factor-1)*0.5*w, 0, cache_candle_factor*w, 0)\n        self.painter.begin(self.picture)\n        self._generate_dummy_picture(self.viewRect())\n        self.generate_picture(self.cachedRect)\n        self.painter.end()\n        self.dirty = False\n\n    def _generate_dummy_picture(self, boundingRect):\n        if self.datasrc.is_sparse:\n            # just draw something to ensure PyQt will paint us again\n            self.painter.setPen(pg.mkPen(background))\n            self.painter.setBrush(pg.mkBrush(background))\n            l, r = boundingRect.left(), boundingRect.right()\n            self.painter.drawRect(QtCore.QRectF(l, boundingRect.top(), 1e-3, boundingRect.height()*1e-5))\n            self.painter.drawRect(QtCore.QRectF(r, boundingRect.bottom(), -1e-3, -boundingRect.height()*1e-5))\n\n    def boundingRect(self):\n        return QtCore.QRectF(self.picture.boundingRect())\n\n\nclass CandlestickItem(FinPlotItem):\n    def __init__(self, ax, data_source, draw_body, draw_shadow, candle_width, color_func):\n        self.colors = dict(\n            bull_shadow      =candle_bull_color,\n            bull_frame       =candle_bull_color,\n            bull_body        =candle_bull_body_color,\n            bear_shadow      =candle_bear_color,\n            bear_frame       =candle_bear_color,\n            bear_body        =candle_bear_color,\n            weak_bull_shadow =brighten(candle_bull_color, 1.2),\n            weak_bull_frame  =brighten(candle_bull_color, 1.2),\n            weak_bull_body   =brighten(candle_bull_color, 1.2),\n            weak_bear_shadow =brighten(candle_bear_color, 1.5),\n            weak_bear_frame  =brighten(candle_bear_color, 1.5),\n            weak_bear_body   =brighten(candle_bear_color, 1.5)\n        )\n        self.draw_body = draw_body\n        self.draw_shadow = draw_shadow\n        self.candle_width = candle_width\n        self.shadow_width = 1\n        self.color_func = color_func\n        self.x_offset = 0\n        super().__init__(ax, data_source, lod=True)\n\n    def generate_picture(self, boundingRect):\n        w = self.candle_width\n        w2 = w * 0.5\n        left, right = boundingRect.left(), boundingRect.right()\n        p = self.painter\n        df, origlen = self.datasrc.rows(5, left, right, yscale=self.ax.vb.yscale)\n        drawing_many_shadows = self.draw_shadow and origlen > lod_candles*2//3\n        for shadow, frame, body, df_rows in self.colorfunc(self, self.datasrc, df):\n            idxs = df_rows.index\n            rows = df_rows.values\n            if self.x_offset:\n                idxs += self.x_offset\n            if self.draw_shadow:\n                p.setPen(pg.mkPen(shadow, width=self.shadow_width))\n                for x, (t, open, close, high, low) in zip(idxs, rows):\n                    if high > low:\n                        p.drawLine(QtCore.QPointF(x, low), QtCore.QPointF(x, high))\n            if self.draw_body and not drawing_many_shadows:     # settle with only drawing shadows if too much detail\n                p.setPen(pg.mkPen(frame))\n                p.setBrush(pg.mkBrush(body))\n                for x, (t, open, close, high, low) in zip(idxs, rows):\n                    p.drawRect(QtCore.QRectF(x-w2, open, w, close-open))\n\n    def rowcolors(self, prefix):\n        return [self.colors[prefix+'_shadow'], self.colors[prefix+'_frame'], self.colors[prefix+'_body']]\n\n\ndef brighten(color, f):\n    if not color:\n        return color\n    return pg.mkColor(color).lighter(int(f*100))\n\n\ndef price_color_filter(item, data_source, df):\n    column_open = df.columns['open']\n    column_close = df.columns['close']\n    is_up = df[column_open] <= df[column_close]     # open lower than close = goes up\n    yield item.rowcolors('bull') + [df.loc[is_up, :]]\n    yield item.rowcolors('bear') + [df.loc[~is_up, :]]\n\n\ndef _create_datasrc(ax, *args):\n    def do_create(argument):\n        if len(argument) == 1:\n            if type(argument[0]) == PandasDataSource:\n                return argument[0]\n            elif type(argument[0]) in (list, tuple):\n                argument = [np.array(argument[0])]\n            elif type(argument[0]) == np.ndarray:\n                argument = [pd.DataFrame(argument[0].T)]\n            elif type(argument[0]) == pd.DataFrame:\n                return PandasDataSource(argument[0])\n        argument = [_create_series(a) for a in argument]\n        return PandasDataSource(pd.concat(argument, axis=1))\n\n    iargs = [a for a in args if a is not None]\n    data_source = do_create(iargs)\n    # check if time column missing\n    if len(data_source.df.columns) == 1:\n        # assume time data has already been added before\n        for a in ax.vb.win.axs:\n            if a.vb.datasrc and len(a.vb.datasrc.df.columns) >= 2:\n                data_source.df.columns = a.vb.datasrc.df.columns[1:len(data_source.df.columns)+1]\n                col = a.vb.datasrc.df.columns[0]\n                data_source.df.insert(0, col, a.vb.datasrc.df[col])\n                data_source = PandasDataSource(data_source.df)\n                break\n    elif len(iargs) >= 2 and len(data_source.df.columns) == len(iargs)+1 and len(iargs) == len(args):\n        try:\n            if '.Int' in str(type(iargs[0].index)):\n                print('WARNING: performance penalty and crash may occur when using int64 instead of range indices.')\n                if (iargs[0].index == range(len(iargs[0]))).all():\n                    print(' - Fix by .reset_index(drop=True)')\n                    return _create_datasrc(ax, data_source.df[data_source.df.columns[1:]])\n        except:\n            print('WARNING: input data source may cause performance penalty and crash.')\n\n    # FIX: stupid QT bug causes rectangles larger than 2G to flicker, so scale rendering down some\n    if data_source.df.iloc[:, 1:].max(numeric_only=True).max() > 1e8:   # too close to 2G for comfort\n        ax.vb.yscale.set_scale(int(1e8))\n    return data_source\n\n\ndef _create_plot(ax=None, **kwargs):\n    if ax:\n        return ax\n    if last_ax:\n        return last_ax\n    return create_plot(**kwargs)\n\n\ndef create_plot(title='Finance Plot', rows=1, init_zoom_periods=1e10, maximize=True, yscale='linear'):\n    pg.setConfigOptions(foreground=foreground, background=background)\n    win = FinWindow(title)\n    # normally first graph is of higher significance, so enlarge\n    win.ci.layout.setRowStretchFactor(0, top_graph_scale)\n    win.show_maximized = maximize\n    ax0 = axs = create_plot_widget(master=win, rows=rows, init_zoom_periods=init_zoom_periods, yscale=yscale)\n    axs = axs if type(axs) in (tuple,list) else [axs]\n    for ax in axs:\n        win.addItem(ax)\n        win.nextRow()\n    return ax0\n\n\ndef create_plot_widget(master, rows=1, init_zoom_periods=1e10, yscale='linear'):\n    pg.setConfigOptions(foreground=foreground, background=background)\n    global last_ax\n    if master not in windows:\n        windows.append(master)\n    axs = []\n    prev_ax = None\n    for n in range(rows):\n        ysc = yscale[n] if type(yscale) in (list,tuple) else yscale\n        ysc = YScale(ysc, 1)\n        v_zoom_scale = 0.97\n        viewbox = FinViewBox(master, init_steps=init_zoom_periods, yscale=ysc, v_zoom_scale=v_zoom_scale, enableMenu=False)\n        ax = prev_ax = _add_timestamp_plot(master=master, prev_ax=prev_ax, viewbox=viewbox, index=n, yscale=ysc)\n        if axs:\n            ax.setXLink(axs[0].vb)\n        else:\n            viewbox.setFocus()\n        axs += [ax]\n    if isinstance(master, pg.GraphicsLayoutWidget):\n        proxy = pg.SignalProxy(master.scene().sigMouseMoved, rateLimit=144, slot=partial(_mouse_moved, master))\n    else:\n        proxy = []\n        for ax in axs:\n            proxy += [pg.SignalProxy(ax.ax_widget.scene().sigMouseMoved, rateLimit=144, slot=partial(_mouse_moved, master))]\n    master_data[master] = dict(proxymm=proxy, last_mouse_evs=None, last_mouse_y=0)\n    last_ax = axs[0]\n    return axs[0] if len(axs) == 1 else axs\n\n\ndef candlestick_ochl(\n        data_source,\n        draw_body: bool = True,\n        draw_shadow: bool = True,\n        candle_width: float = 0.6,\n        ax=None,\n        color_func: Callable = price_color_filter):\n    ax = _create_plot(ax=ax, maximize=False)\n    data_source = _create_datasrc(ax, data_source)\n    data_source.scale_cols = [3, 4]     # only hi+lo scales\n    _set_datasrc(ax, data_source)\n    item = CandlestickItem(\n        ax=ax,\n        data_source=data_source,\n        draw_body=draw_body,\n        draw_shadow=draw_shadow,\n        candle_width=candle_width,\n        color_func=color_func\n    )\n    _update_significants(ax, data_source, force=True)\n    item.update_data = partial(_update_data, None, None, item)\n    item.update_gfx = partial(_update_gfx, item)\n    ax.addItem(item)\n    return item\n\n\nclass CandlestickItem2(pg.GraphicsObject):\n    def __init__(self, data: pd.DataFrame):\n        super().__init__(self)\n        self.data: pd.DataFrame = data\n        self.generatePicture()\n\n    def generatePicture(self):\n        self.picture = QtGui.QPicture()\n        painter = QtGui.QPainter(self.picture)\n        painter.setPen(pg.mkPen('w'))\n        w = (self.data[1][0] - self.data[0][0]) / 3.\n        for (t, open, close, min, max) in self.data:\n            print(open, close, type(open))\n            if open > close:\n                painter.setPen(pg.mkPen('g'))\n                painter.setBrush(pg.mkBrush('g'))\n            else:\n                painter.setPen(pg.mkPen('r'))\n                painter.setBrush(pg.mkBrush('r'))\n            painter.drawLine(QtCore.QPointF(t, min), QtCore.QPointF(t, max))\n            painter.drawRect(QtCore.QRectF(t - w, open, w * 2, close - open))\n        painter.end()\n\n    def paint(self, p, *args):\n        p.drawPicture(0, 0, self.picture)\n\n    def boundingRect(self):\n        return QtCore.QRectF(self.picture.boundingRect())\n\n\nclass WidgetChart(QtWidgets.QGraphicsView):\n    def __init__(self,\n                 parent: QtWidgets.QWidget = None,\n                 size: QtCore.QSize = None,\n                 title: str = None,\n                 show: bool = False,\n                 **kargs\n                 ):\n        super().__init__(parent)\n        self.plot = pg.GraphicsLayoutWidget(parent=self)\n\n        self.chart_candlestick = self.plot.addPlot(\n            row=0,\n            col=0,\n            title='<span style=\"font-size: 10pt\">SHFE, ag 2101'\n        )\n        self.chart_candlestick.titleLabel.setAttr('justify', 'left')\n        self.chart_attached: dict = {}\n        self.chart_volume = self.plot.addPlot(row=1, col=0)\n        self.chart_macd = self.plot.addPlot(row=2, col=0)\n\n        if size is not None:\n            self.plot.resize(size)\n\n        if title is not None:\n            self.plot.setWindowTitle(title)\n\n        if show is True:\n            self.plot.show()\n\n    def resizeEvent(self, event: QtGui.QResizeEvent) -> None:\n        self.plot.resize(event.size())\n","repo_name":"BruceFrankWong/FuturesWorkshop","sub_path":"FuturesWorkshop/ui/widget_chart.py","file_name":"widget_chart.py","file_ext":"py","file_size_in_byte":14052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23889437425","text":"from extract import get_df_from_parquet\nfrom load import df_to_parquet\nfrom transform import view_cia, view_aero\n\nfrom rich.progress import Progress\n\nif __name__ == '__main__':\n    progress = Progress()\n    progress.start()\n    \n    pipeline_task = progress.add_task(\"Lendo parquet's da camada Staging\")\n    df_air_cia = get_df_from_parquet('./output/AIR_CIA/')\n    df_vra = get_df_from_parquet('./output/VRA/')\n    df_aerodromos = get_df_from_parquet('./output/AERODROMOS/')\n    \n    progress.update(pipeline_task, advance=25, description= \"Gerando df_view_cia\")\n    df_view_cia = view_cia(df_air_cia, df_vra, df_aerodromos)\n    \n    progress.update(pipeline_task, advance = 35)\n    df_ranking_aero_cia = view_aero(df_air_cia, df_vra, df_aerodromos)    \n    \n    progress.update(pipeline_task, advance=30, description= \"Salvando os .parquet's na camada Analítica\")\n    df_to_parquet(df_view_cia, './output/VIEW_CIA', 'view_cia')\n    df_to_parquet(df_ranking_aero_cia, './output/RANKING_AERO_CIA', 'ranking_aero_cia')\n    progress.update(pipeline_task, advance=10, description= 'Finalizado!')\n    progress.stop()","repo_name":"GCAntunes/BigDataAirlines","sub_path":"src/analytic/pipeline_analytic.py","file_name":"pipeline_analytic.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"35761981012","text":"#!/usr/bin/env python\n\"\"\"Skeleton program to demonstrate reading and analyzing simulation output.\n\nThis program reads a simulation output file 'demo.fits' and then loops over\nall overlapping groups with exactly two members, with some additional cuts on\nthe galaxy properties, finally saving images of each pair to an output\nfile 'pairs.fits'.\n\"\"\"\nfrom __future__ import print_function, division\n\nimport numpy as np\nimport galsim\nimport descwl\n\n# Initialize an array of GalSim images that we will save.\nimages_to_save = [ ]\n\n# Load the results of a previous simulation.\nresults = descwl.output.Reader('demo.fits').results\ncatalog = results.table\n\n# Select the brightest sources from groups with exactly 2 members.\nselected = results.select('grp_size==2','grp_rank==0')\n\n# Loop over these pair groups.\nfor index in selected:\n\t# Skip groups where the overlap has essentially no effect on the brighter galaxy.\n\tif catalog['purity'][index] > 0.95: continue\n\t# Select all (both) members of this group.\n\tgrp_id = catalog['db_id'][index]\n\tgroup = results.select('grp_id==%ld' % grp_id)\n\t# Print the fluxes of each galaxy (in order of increasing SNR).\n\tprint('fluxes for group id %ld are %.3f,%.3f electrons.' % (\n\t\tgrp_id,catalog['flux'][group[0]],catalog['flux'][group[1]]))\n\t# Create and save an image of just this group.\n\timage = results.get_subimage(group)\n\timages_to_save.append(image)\n\t# Save images of each galaxy individually, using the same bounding box.\n\tfor galaxy_index in group:\n\t\tgalaxy = image.copy()\n\t\tgalaxy.array[:] = 0.\n\t\tstamp = results.get_stamp(galaxy_index)\n\t\tgalaxy[stamp.bounds] = stamp\n\t\timages_to_save.append(galaxy)\n\nprint('Saving %d images.' % len(images_to_save))\ngalsim.fits.writeMulti(images_to_save,file_name = 'pairs.fits')\n","repo_name":"LSSTDESC/WeakLensingDeblending","sub_path":"skeleton.py","file_name":"skeleton.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"77"}
+{"seq_id":"14447314092","text":"class Solution:\n    def findDisappearedNumbers(self, nums: list) -> list:\n\n        def put(num):\n            if num != nums[num - 1]:\n                tmp = nums[num - 1]\n                nums[num - 1] = num\n                put(tmp)\n\n        for i in nums:\n            put(i)\n\n        result = []\n        for idx, item in enumerate(nums):\n            if item-1 != idx:\n                result.append(idx+1)\n        return result\n\n\nnums = [4, 3, 2, 7, 8, 2, 3, 1]\nprint(Solution().findDisappearedNumbers(nums))\n","repo_name":"xxxxcj/leetcode","sub_path":"448(Find All Numbers Disappeared in an Array).py","file_name":"448(Find All Numbers Disappeared in an Array).py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17178476543","text":"### use pytest to test this file ###\n\nfrom grammar import *\nimport test_scanner as ts\nimport test_ll_k_parser as tll\nfrom lr_k_parser import parse_from_string, parse_from_tokens\nfrom scanner import Token, Scan, make_scanner\n\n\ndef test_base_case(capfd):\n    empty = start_separated(Grammar[str, str]((\"S\",), (), (), \"S\"), \"S'\")\n    assert not parse_from_string(empty, 0, \"\")[0]\n    assert not parse_from_string(empty, 0, \"x\")[0]\n\n    epsilon = start_separated(\n        Grammar[str, str]((\"S\",), (), (Production(\"S\", ()),), \"S\"), \"S'\"\n    )\n    assert parse_from_string(epsilon, 0, \"\")[0]\n    assert not parse_from_string(epsilon, 0, \"x\")[0]\n\n    single = start_separated(\n        Grammar[str, str]((\"S\",), (\"x\",), (Production(\"S\", (\"x\",)),), \"S\"), \"S'\"\n    )\n    assert not parse_from_string(single, 0, \"\")[0]\n    assert parse_from_string(single, 0, \"x\")[0]\n    assert not parse_from_string(single, 0, \"xx\")[0]\n\n\ndef test_recursive(capfd):\n    recursive_grammar = start_separated(tll.recursive_grammar, \"S'\")\n    assert not parse_from_string(recursive_grammar, 0, \"\")[0]\n    assert not parse_from_string(recursive_grammar, 0, \"ba\")[0]\n    assert parse_from_string(recursive_grammar, 0, \"ab\")[0]\n    assert parse_from_string(recursive_grammar, 0, \"bab\")[0]\n    assert parse_from_string(recursive_grammar, 0, \"bbab\")[0]\n    assert not parse_from_string(recursive_grammar, 0, \"bcab\")[0]\n    assert not parse_from_string(recursive_grammar, 0, \"bbabb\")[0]\n    parse_from_string(recursive_grammar, 0, \"bbab\")\n    out, err = capfd.readouterr()\n    assert \"not LR(0)\" not in out\n\n\ndef test_complex(capfd):\n    complex_grammar = start_separated(tll.complex_grammar, \"S'\")\n    parse = lambda k, inp: parse_from_tokens(\n        complex_grammar, k, list(make_scanner(ts.scan_complex, inp))\n    )\n    assert not parse(1, \"\")[0]\n    assert parse(1, \"10 + hello\")[0]\n    assert parse(1, \"10 + hello - 0\")[0]\n    assert parse(1, \"10 + hello - (a - a)\")[0]\n    assert parse(1, \"0 * ((1 * (2)) * 3)\")[0]\n    assert parse(1, \"0*((1*(2))*3)\")[0]\n    assert parse(1, \"0*   (\\t(1*\\n(2))    \\n *3)\")[0]\n    assert parse(1, \"(0 * ((1 * (2)) * 3))\")[0]\n    assert not parse(1, \"0 * ((1 * (2)) * 3) <=\")[0]\n    assert not parse(1, \"0 * ((1 * (2)) * 3) 4\")[0]\n    assert not parse(1, \"(0 * ((1 * (2)) * 3)\")[0]\n    assert not parse(1, \"(0 * ((1 ** (2)) * 3))\")[0]\n    out, err = capfd.readouterr()\n    assert \"not LR(1)\" not in out\n    parse(0, \"(0 * ((1 * (2)) * 3))\")\n    out, err = capfd.readouterr()\n    assert \"not LR(0)\" in out\n\n\n### abstract syntax  ###\n\n\n@dataclass(frozen=True)\nclass AST:\n    pass\n\n\n@dataclass(frozen=True)\nclass Const(AST):\n    value: int\n\n\n@dataclass(frozen=True)\nclass Var(AST):\n    value: str\n\n\n@dataclass(frozen=True)\nclass BinOp(AST):\n    left: AST\n    op: str\n    right: AST\n\n\n@dataclass(frozen=True)\nclass BinRel(AST):\n    left: AST\n    rel: str\n    right: AST\n\n\n@dataclass(frozen=True)\nclass IfExp(AST):\n    if_cond: AST\n    then_exp: AST\n    else_exp: AST\n\n\n@dataclass(frozen=True)\nclass Ret(AST):\n    exp: AST\n\n\n@dataclass(frozen=True)\nclass Prnt(AST):\n    exp: AST\n\n\n@dataclass(frozen=True)\nclass Let(AST):\n    var: str\n    exp: AST\n\n\n@dataclass(frozen=True)\nclass Module(AST):\n    stmts: list[AST]\n\n\nNumber = ts.Number(0)\nIdentifier = ts.Identifier(\"\")\nOperator = ts.Operator(\"\")\nRelation = ts.Relation(\"\")\nLeft = ts.Left()\nRight = ts.Right()\nEnd = ts.End()\nIf = ts.If()\nThen = ts.Then()\nElse = ts.Else()\nReturn = ts.Return()\nPrint = ts.Print()\nAssign = ts.Assign()\nWhiteSpace = ts.WhiteSpace()\n\n\ncomplex_grammar = Grammar[str, Token](\n    (\"Module\", \"Stmt\", \"Exp\"),\n    (\n        Number,\n        Identifier,\n        Operator,\n        Relation,\n        Left,\n        Right,\n        End,\n        If,\n        Then,\n        Else,\n        Return,\n        Print,\n        Assign,\n        WhiteSpace,\n    ),\n    (\n        Production(\n            \"Module\", (NT(\"Stmt\"), NT(\"Module\")), lambda s, m: Module(s + m.stmts)\n        ),\n        Production(\"Module\", (), lambda: Module([])),\n        Production(\"Stmt\", (End,), lambda end: []),\n        Production(\"Stmt\", (Print, NT(\"Exp\"), End), lambda p, exp, end: [Prnt(exp)]),\n        Production(\"Stmt\", (Return, NT(\"Exp\"), End), lambda r, exp, end: [Ret(exp)]),\n        Production(\n            \"Stmt\",\n            (Identifier, Assign, NT(\"Exp\"), End),\n            lambda i, a, exp, end: [Let(i.value, exp)],\n        ),\n        Production(\"Exp\", (Number,), lambda n: Const(n.value)),\n        Production(\"Exp\", (Identifier,), lambda i: Var(i.value)),\n        Production(\n            \"Exp\",\n            (NT(\"Exp\"), Operator, NT(\"Exp\")),\n            lambda e1, o, e2: BinOp(e1, o.value, e2),\n        ),\n        Production(\n            \"Exp\",\n            (NT(\"Exp\"), Relation, NT(\"Exp\")),\n            lambda e1, r, e2: BinRel(e1, r.value, e2),\n        ),\n        Production(\n            \"Exp\",\n            (If, NT(\"Exp\"), Then, NT(\"Exp\"), Else, NT(\"Exp\")),\n            lambda i, e1, t, e2, e, e3: IfExp(e1, e2, e3),\n        ),\n        Production(\n            \"Exp\",\n            (Left, NT(\"Exp\"), Right),\n            lambda l, e, r: e,\n        ),\n    ),\n    \"Module\",\n)\ncomplex_grammar = start_separated(complex_grammar, \"S'\")\n\n\ndef test_complex_ast(capfd):\n    parse = lambda k, inp: parse_from_tokens(\n        complex_grammar, k, list(make_scanner(ts.scan_complex, inp))\n    )\n    assert parse(1, \"\") == (True, Module([]))\n    assert parse(1, \"print hi;\") == (True, Module([Prnt(Var(\"hi\"))]))\n    assert parse(1, \"return 1;\") == (True, Module([Ret(Const(1))]))\n    assert parse(1, \"hi := 1;\") == (True, Module([Let(\"hi\", Const(1))]))\n    assert parse(1, \"hi := 1;\\nprint(hi + 1);\") == (\n        True,\n        Module([Let(\"hi\", Const(1)), Prnt(BinOp(Var(\"hi\"), \"+\", Const(1)))]),\n    )\n    assert parse(1, \"hi := if hi == 0 then 1 else 0;\") == (\n        True,\n        Module(\n            [Let(\"hi\", IfExp(BinRel(Var(\"hi\"), \"==\", Const(0)), Const(1), Const(0)))]\n        ),\n    )\n    assert parse(1, \"hi := if if hi == 0 then 0 else 1 then 1 else 0;\") == (\n        True,\n        Module(\n            [\n                Let(\n                    \"hi\",\n                    IfExp(\n                        IfExp(BinRel(Var(\"hi\"), \"==\", Const(0)), Const(0), Const(1)),\n                        Const(1),\n                        Const(0),\n                    ),\n                )\n            ]\n        ),\n    )\n    assert parse(\n        1, \"hi := if if hi == 0 then 0 else 1 then if i then 0 else 1 else 0;\"\n    ) == (\n        True,\n        Module(\n            [\n                Let(\n                    \"hi\",\n                    IfExp(\n                        IfExp(BinRel(Var(\"hi\"), \"==\", Const(0)), Const(0), Const(1)),\n                        IfExp(Var(\"i\"), Const(0), Const(1)),\n                        Const(0),\n                    ),\n                )\n            ]\n        ),\n    )\n    assert parse(1, \"hi := if if hi == 0 then 0 else 1 then 1 else (0 * (1 / 2));\") == (\n        True,\n        Module(\n            [\n                Let(\n                    \"hi\",\n                    IfExp(\n                        IfExp(BinRel(Var(\"hi\"), \"==\", Const(0)), Const(0), Const(1)),\n                        Const(1),\n                        BinOp(Const(0), \"*\", BinOp(Const(1), \"/\", Const(2))),\n                    ),\n                )\n            ]\n        ),\n    )\n    assert parse(1, \"hi := if hi == 0 then 1 else if i then 1 else 0;\") == (\n        True,\n        Module(\n            [\n                Let(\n                    \"hi\",\n                    IfExp(\n                        BinRel(Var(\"hi\"), \"==\", Const(0)),\n                        Const(1),\n                        IfExp(Var(\"i\"), Const(1), Const(0)),\n                    ),\n                )\n            ]\n        ),\n    )\n    assert parse(1, \"return 1 + (2 <= 3);\") == (\n        True,\n        Module([Ret(BinOp(Const(1), \"+\", BinRel(Const(2), \"<=\", Const(3))))]),\n    )\n    assert parse(1, \"    return 1 + (2 <= 3);    \") == (\n        True,\n        Module([Ret(BinOp(Const(1), \"+\", BinRel(Const(2), \"<=\", Const(3))))]),\n    )\n    assert parse(1, \"return 1 + (2 <= 3);return 1+1;;;;\") == (\n        True,\n        Module(\n            [\n                Ret(BinOp(Const(1), \"+\", BinRel(Const(2), \"<=\", Const(3)))),\n                Ret(BinOp(Const(1), \"+\", Const(1))),\n            ]\n        ),\n    )\n    assert parse(1, \"return (1 + 2) <= 3;\") == (\n        True,\n        Module([Ret(BinRel(BinOp(Const(1), \"+\", Const(2)), \"<=\", Const(3)))]),\n    )\n    assert parse(1, \"return((1)+(2))<=(3);\") == (\n        True,\n        Module([Ret(BinRel(BinOp(Const(1), \"+\", Const(2)), \"<=\", Const(3)))]),\n    )\n    assert parse(1, \"return((1)\\n+(2\\t))  <=   (3);\") == (\n        True,\n        Module([Ret(BinRel(BinOp(Const(1), \"+\", Const(2)), \"<=\", Const(3)))]),\n    )\n    assert parse(1, \"print hi\") == (False, None)\n    assert parse(1, \"return 1 + (2 <= print 3);\") == (False, None)\n    assert parse(1, \"return 1 + (2 <= 4 := 3);\") == (False, None)\n    assert parse(1, \"return 1 + (2 <= 4));\") == (False, None)\n    assert parse(1, \"return 1 ++ (2 <= 4);\") == (False, None)\n    assert parse(1, \"return <= (2 + 4);\") == (False, None)\n    assert parse(1, \"return 1 + (2 <= 3) return 1+1;;;;\") == (False, None)\n    assert parse(1, \"return if i then if j then 0 else 1;\") == (False, None)\n    out, err = capfd.readouterr()\n    assert \"not LR(1)\" not in out\n    parse(0, \"return (0 * ((1 * (2)) * 3))\")\n    out, err = capfd.readouterr()\n    assert \"not LR(0)\" in out\n","repo_name":"Proglang-Uni-Freiburg/Algorithms-for-Compiler-Construction-in-Python","sub_path":"test_lr_k_parser.py","file_name":"test_lr_k_parser.py","file_ext":"py","file_size_in_byte":9441,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22245879706","text":"#!/usr/bin/python3\n\nimport re\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef is_url(msg):\n   regex = r\"(https?:\\/\\/(?:www\\.)?\\S+)\"\n   match = re.search(regex, msg)\n   if match:\n      url = match.group(1)\n      return url\n   else:\n      return None\n\n\ndef get_data(url):\n   r = requests.get(url)\n   data = r.text\n   r.close()\n   return data\n\n\ndef get_title(url):\n   data = get_data(url)\n   soup = BeautifulSoup(data, 'html.parser')\n   title = soup.title.string\n   del soup\n   return title\n\n","repo_name":"kb-elmo/bertbot","sub_path":"urlinfo.py","file_name":"urlinfo.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4265919752","text":"# найдите максимальную стоимость частей предметов,\n# помещающихся в рюкзак, с точностью не менее трёх знаков после запятой\n\n\ndef backpack(pavs, w_max):\n    if pavs:\n        p, w = 0, 0\n        sorted_lst = sorted(pavs, key=lambda x: x[0] / x[1], reverse=True)\n        for pav in sorted_lst:\n            if w + pav[1] >= w_max:\n                p += (w_max - w) / pav[1] * pav[0]\n                return round(p, 3)\n            else:\n                p += pav[0]\n                w += pav[1]\n        return round(p, 3)\n\n\ndef main():\n    n, w_max = map(int, input().split())\n    prices_and_volumes = []\n    for _ in range(n):\n        prices_and_volumes.append(list(map(int, input().split())))\n    print(backpack(prices_and_volumes, w_max))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"MaximTar/st_csc_alg","sub_path":"7_backpack.py","file_name":"7_backpack.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23423507846","text":"fruit = {\"orange\":\"a sweet, orange citrus fruit\",\n         \"apple\": \"good for making cider\",\n         \"lemon\": \"a sour, yellow citrus fruit\",\n         \"grape\": \"a small, sweet fruit growing in bunches\",\n         \"lime\": \"a sour, green citrus fruit\"}\n\nprint(fruit)\n\nveg = {\"cabbage\":\"every child's favourite\",\n       \"sprouts\":\"hmmmmm, lovely\",\n       \"spinach\":\"may i have some fruit please\"}\n\nprint(veg)\n\nveggie = veg.copy()\n\nprint(\"=\"*50)\n\nprint(veggie)\n\nveggie.update(fruit)\n\nprint(\"=\"*50)\n\nprint(veggie)","repo_name":"nyowusu/pymasterclass","sub_path":"pmclass_env/codes/fruits.py","file_name":"fruits.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37275240622","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 28 22:01:04 2022\n\n@author: Supun Madushanka\n\"\"\"\n\nimport pandas as pd\nfrom FilesForTraining.MainModel import PreProcessing \nimport cv2\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\n\nfrom FilesForTraining.MainModel.Features.BrownAreaAndCircularity import Brown_Area_And_Circularity\nfrom FilesForTraining.MainModel.Features.YellowAreaAndCircularity import Yellow_Area_And_Circularity\nfrom FilesForTraining.MainModel.Features.WhiteAreaAndCircularity import White_Area_And_Circularity\nfrom FilesForTraining.MainModel.Features.TextureParametersSobel import TextureParametersSobel\nfrom FilesForTraining.MainModel.Features.Hog import HogFilter\nfrom FilesForTraining.MainModel.Features.Sift import SiftFilter\nfrom FilesForTraining.MainModel.Features.Gradient import GradientFeature\nfrom FilesForTraining.MainModel.Features.LaplacianTextureFeatures import LaplacianTextureFeatures\nfrom FilesForTraining.MainModel.Features.Canny import CannyFeatures\n\n\ndef feature_extractor_Custom(dataset):\n    image_dataset = pd.DataFrame()\n    for image in range(dataset.shape[0]):  \n        \n        df = pd.DataFrame()\n        img = dataset[image, :,:]\n        \n        bg_rem_img = PreProcessing.bg_remove_int(img)\n        \n        \n        # FEATURE 1 GLCM\n        b,g,r = cv2.split(bg_rem_img)\n        num = 1;\n        for color in (r,g,b):\n            for pi in (0, np.pi/4, np.pi/2, 3*np.pi / 4):\n                GLCM = graycomatrix(color, [3], [pi], symmetric=True, normed=True)       \n                \n                GLCM_Energy = graycoprops(GLCM, 'energy')[0]\n                GLCM_corr = graycoprops(GLCM, 'correlation')[0]\n                GLCM_diss = graycoprops(GLCM, 'dissimilarity')[0]\n                GLCM_hom = graycoprops(GLCM, 'homogeneity')[0]      \n                GLCM_contr = graycoprops(GLCM, 'contrast')[0]\n                \n                df[\"Energy\" + str(num)] = GLCM_Energy\n                df[\"Corr\" + str(num)] = GLCM_corr\n                df[\"Diss_sim\" + str(num)] = GLCM_diss\n                df[\"Homogen\" + str(num)] = GLCM_hom\n                df[\"Contrast\" + str(num)] = GLCM_contr\n                \n                num += 1\n                \n        \n        # FEATURE 2 perimeter & circularity of brown\n        AreaAndCircularityBrown=Brown_Area_And_Circularity(bg_rem_img)\n        \n        # FEATURE 3 Laplacian mean and sd\n        SobelMeanAndSD=TextureParametersSobel(bg_rem_img)\n        \n        # FEATURE 4 perimeter & circularity of yellow\n        AreaAndCircularityWhite=White_Area_And_Circularity(bg_rem_img)\n        \n        # FEATURE 5 gradient\n        GradientFeatures = GradientFeature(img)\n        \n        # FEATURE 6 laplacian texture features\n        LaplacianFeatures = LaplacianTextureFeatures(img)\n        \n        # FEATURE 7 canny edge features\n        CannyEdgeFeatures = CannyFeatures(img)\n        \n\n        df1 = pd.DataFrame({\n            'BrownArea1': [AreaAndCircularityBrown[0]], \n            'BrownCircularity1': [AreaAndCircularityBrown[1]],\n            'BrownArea2': [AreaAndCircularityBrown[2]], \n            'BrownCircularity2': [AreaAndCircularityBrown[3]],\n            'BrownArea3': [AreaAndCircularityBrown[4]], \n            'BrownCircularity3': [AreaAndCircularityBrown[5]],\n            'BrownArea4': [AreaAndCircularityBrown[6]], \n            'BrownCircularity4': [AreaAndCircularityBrown[7]],\n            'BrownArea5': [AreaAndCircularityBrown[8]], \n            'BrownCircularity5': [AreaAndCircularityBrown[9]],\n            'NoOfBrownSpots': [AreaAndCircularityBrown[10]],\n            \n            'SobelMean': [SobelMeanAndSD[0]],\n            'SobelSD': [SobelMeanAndSD[1]],\n            'SobelVariance': [SobelMeanAndSD[2]],\n            \n            'WhiteArea1': [AreaAndCircularityWhite[0]], \n            'WhiteCircularity1': [AreaAndCircularityWhite[1]],\n            'WhiteArea2': [AreaAndCircularityWhite[2]], \n            'WhiteCircularity2': [AreaAndCircularityWhite[3]],\n            'WhiteArea3': [AreaAndCircularityWhite[4]], \n            'WhiteCircularity3': [AreaAndCircularityWhite[5]],\n            'NoOfWhiteSpots': [AreaAndCircularityWhite[10]],\n            \n            'MeanMagnitude': [GradientFeatures[0]], \n            'SDofMagnitude': [GradientFeatures[1]], \n            'MeanDirection': [GradientFeatures[2]], \n            'SDofDirection': [GradientFeatures[3]],\n            \n            'Roughness': [LaplacianFeatures[0]], \n            'Smoothness': [LaplacianFeatures[1]], \n            'Coarseness': [LaplacianFeatures[2]], \n            'Regularity': [LaplacianFeatures[3]], \n            'Contrast': [LaplacianFeatures[4]], \n            'Homogeneity': [LaplacianFeatures[5]], \n            'Entropy':  [LaplacianFeatures[6]], \n            'Energy':  [LaplacianFeatures[7]], \n            'Correlation':  [LaplacianFeatures[8]], \n            'Directionality':  [LaplacianFeatures[9]], \n            'FractalDimension': [LaplacianFeatures[10]], \n            \n            'MeanEdgeIntensity': CannyEdgeFeatures[0],\n            'StdDevEdgeIntensity': CannyEdgeFeatures[1],\n            'SkewnessEdgeIntensity': CannyEdgeFeatures[2],\n            'KurtosisEdgeIntensity': CannyEdgeFeatures[3]\n            \n            })\n        \n        new_df = pd.concat([df, df1], axis=1)\n        \n        image_dataset = pd.concat([image_dataset,new_df])\n        print(image_dataset.shape)\n        \n        \n    return image_dataset","repo_name":"supunmadushanka/TomatoDiseaseClassification","sub_path":"FilesForTraining/MainModel/FeatureExtractionMain.py","file_name":"FeatureExtractionMain.py","file_ext":"py","file_size_in_byte":5456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72799233209","text":"'''\nGiven a binary tree, find its maximum depth (or height)\n\n        12\n      /     \\\n    7        1\n  /         / \\\n 9        10   5\n        /\n      11\n\nSimilar to Minimum Depth of a Binary Tree, instead of returning as soon as we find a leaf node, we will keep traversing for all the levels, incrementing treeHeight each time we complete a level.\n'''\nfrom collections import deque\n\nclass TreeNode:\n  def __init__(self, val=0, left=None, right=None):\n    self.val = val\n    self.left = left\n    self.right = right\n\ndef maximumDepth(root):\n  if root is None:\n    return 0\n\n  queue = deque()\n  queue.append(root)\n  treeHeight = 0\n\n  while queue:\n    levelSize = len(queue)\n    treeHeight += 1\n\n    for _ in range(levelSize):\n      currentNode = queue.popleft()\n\n      if currentNode.left:\n        queue.append(currentNode.left)\n      if currentNode.right:\n        queue.append(currentNode.right)\n  \n  return treeHeight\n\ndef main():\n  root = TreeNode(12)\n\n  root.left = TreeNode(7)\n  root.right = TreeNode(1)\n\n  root.right.left = TreeNode(10)\n  root.right.right = TreeNode(5)\n\n  print(\"Tree Maxiumum Depth: \" + str(maximumDepth(root)))\n\n  root.left.left = TreeNode(9)\n  root.right.left.left = TreeNode(11)\n\n  print(\"Tree Maxiumum Depth: \" + str(maximumDepth(root)))\n\nmain()\n\n'''\nTime Complexity: O(n) -> where 'n' is the total number of nodes in the tree\nSpace Complexity: O(n) -> With the use of the queue, we can have a max of n/2 nodes at any level (this only happens at the lowest level),\n                          therefore O(n) space is needed to store them in the queue\n'''\n","repo_name":"LennyGonz/LeetCode-Questions","sub_path":"Patterns/Tree_Breadth_First_Search/5b_Maximum_Depth_of_a_Binary_Tree.py","file_name":"5b_Maximum_Depth_of_a_Binary_Tree.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27628373387","text":"\nfrom atexit import register\nfrom re import compile\nfrom threading import Thread\nfrom time import ctime\nfrom urllib.request import urlopen as uopen\n\nREGEX = compile('#([\\d,]+) in Books ')\nAMZN = 'http://amazon.com/dp/'\nISBNs = {\n         \"0132269937\": \"Core Python Programming\",\n         \"0132356139\": \"Python Web Development with Django\",\n         \"0137143419\": \"Python Fundamentals\",\n         }\n\n\ndef getRanking(isbn):\n\t'''获取排名列表'''\n\t'''\n    根据ISBN，创建与AMazon服务器通信的最终url，然后打开这个地址。一旦服务器连接成功，就可以得到服务器返回的类似文件的对象。\n    然后调用 read() 函数下载整个网页，以及关闭这个文件。\n    如果正则表达式与预期一样精确，应当有且只有一个匹配\n    因此从生成的列表中抓取这个值，并将其返回。\n\t'''\n\tpage = uopen(\"%s%s\" % (AMZN, isbn)) #str.format()isbn为国际标准书号\n\tdata = page.read()\n\tpage.close()\n\treturn REGEX.findall(data.decode(\"utf-8\"))[0] # 将bytes转变成string\n\ndef _showRanking(isbn):\n\t'''展示排名'''\n\t'''\n\t通过ISBN，查询其对应的书名，调用getRank()函数从Amazon 网站上获得这本书的当前排名，然后把这些值输出给用户。\n\t函数名前面的单下划线表示这是一个特殊函数，只能被模块的代码使用，不能被其他使用本文件作为库或者工具模块的应用导入\n\t'''\n\tprint(\"- %r ranked %s\" % (ISBNs[isbn], getRanking(isbn)))\n\ndef main():\n \tprint(\"At\", ctime(), \"on Amazon...\")\n \tthreads = []\n \tfor isbn in ISBNs:\n \t\t# _showRanking(isbn) # 单线程\n \t  \tthread = Thread(target=_showRanking, args=(isbn, ))\n \t  \tthreads.append(thread)\n \t  \t# thread.start()\n\n \tfor t in threads:\n \t\tt.start();\n\n\n@register # 告知脚本何时结束\ndef _atexit():\n\tprint(\"all DONE at:\", ctime())\n\nif __name__ == \"__main__\":\n\tmain()\n\n","repo_name":"AnatoleZho/Python3","sub_path":"CorePython/threadPractice/bookrank.py","file_name":"bookrank.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14140285629","text":"import cv2\nimport time\nimport numpy as np\nimport os.path as ops\nfrom .show_score import sc_show\n\n\nclass Camera(object):\n    def __init__(self, \n                 c_mode=0, \n                 c_width=768, \n                 c_height=768,\n                 update_func=None,\n                 class_func=None,\n                 key_press=False):\n        # 0：电脑摄像头，1：外接摄像头\n        if c_mode not in [0, 1]:\n            raise ValueError(\"The c_mode must be 0 or 1!\")\n        self.cap = cv2.VideoCapture(c_mode)\n        self.cap.set(3, c_width)  # 宽\n        self.cap.set(4, c_height)  # 高\n        self.update_func = update_func  # 图像显示方法\n        self.class_func =class_func  # 图像分类\n        self.key_press = key_press  # 键盘控制\n\n    def get_img(self, save_path=None):\n        while(self.cap.isOpened()):\n            ret_flag, Vshow = self.cap.read()  # 是否正确，图像\n            if not ret_flag:\n                raise ValueError(\"Failed to get image\")\n            hand_img, vis_img = None, Vshow\n            # 手势检测\n            if self.update_func is not None:\n                hand_img, vis_img = self.update_func(Vshow)\n            # 手势分类\n            if hand_img is not None:\n                if self.class_func is not None:\n                    sc_show(\n                        hand_img, \n                        self.class_func, self.key_press)\n            else:\n                if self.class_func is not None:\n                    sc_show(\n                        np.zeros((224, 224, 3), dtype=\"uint8\"), \n                        self.class_func, self.key_press)\n            cv2.imshow('Capture', vis_img)\n            # 保存\n            if save_path is not None:\n                img_path = ops.join(\n                    save_path,\n                    (str(time.time()).replace(\".\", \"_\") + \".jpg\"))\n                time.sleep(0.1)  # 每0.1秒获取一次\n                if self.update_func is not None:\n                    if hand_img is not None:\n                        cv2.imwrite(img_path, hand_img)\n                else:\n                    cv2.imwrite(img_path, vis_img)\n            # 按q退出\n            usdown = cv2.waitKey(1)\n            # print(usdown)\n            if usdown == 113:  # q\n                break\n        self.cap.release()\n        cv2.destroyAllWindows()","repo_name":"Nanyi-Brigade/GBIM","sub_path":"utils/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"31293729728","text":"import re\n\n\ndef count_words(sentence: str) -> dict:\n    \"\"\"Counts the occurrences of each word in a phrase.\"\"\"\n    words = re.findall(r\"[a-z0-9]+(?:'[a-z]+)?\", sentence.lower())\n    return {\n        wrd: words.count(wrd)\n        for wrd in set(words)\n    }\n","repo_name":"Thewessen/hello-world","sub_path":"Exercism/python/word-count/word_count.py","file_name":"word_count.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8863323322","text":"import cv2\n\n\n# OpenCV读取视频\nif __name__ == '__main__':\n    # 创建一个实例\n    cap = cv2.VideoCapture()\n    isRunning = True\n\n    # 打开视频\n    cap.open('data/test.mp4')\n\n    while isRunning:\n        # ret表示读取成功，frame表示读取到的视频帧\n        ret, frame = cap.read()\n        if ret:\n            cv2.imshow('Camera', frame)\n        # 得到按键\n        key = cv2.waitKey(30)\n        if key == ord('q'):\n            isRunning = False\n\n    # 释放VideoCapture\n    cap.release()","repo_name":"taowenyin/HelloCV","sub_path":"opencv_example/S1/S1.6.py","file_name":"S1.6.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"33551995441","text":"import re\n\nwith open('data.txt') as f:\n    data = f.readlines()\n\nsum = 0\ndigits = ['zero','one','two','three','four','five','six','seven','eight','nine']\nfor line in data:\n    g = \"(\\\\d|one|two|three|four|five|six|seven|eight|nine)\"\n    n1 = re.match(f\"^.*?{g}\",line).group(1)\n    n2 = re.match(f\"^.*{g}\",line).group(1)\n    nums = filter(lambda a:a is not None, [n1, n2])\n    nums = map(lambda a: str(digits.index(a)) if a in digits else a, nums)\n    sum += int(\"\".join(nums))\n\nprint(sum)\n","repo_name":"zac112/adventOfCode","sub_path":"code2023/day1/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"6545436398","text":"\"\"\"\nQuick crunch notebook steps\n\"\"\"\nfrom __future__ import annotations\n\nfrom collections.abc import Iterable\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING\n\nfrom ..pydoit_nb.config_handling import get_config_for_step_id\nfrom ..pydoit_nb.notebook import ConfiguredNotebook, UnconfiguredNotebook\nfrom ..pydoit_nb.notebook_step import UnconfiguredNotebookBasedStep\n\nif TYPE_CHECKING:\n    from ..config.base import ConfigBundle\n\n\ndef configure_notebooks(\n    unconfigured_notebooks: Iterable[UnconfiguredNotebook],\n    config_bundle: ConfigBundle,\n    step_name: str,\n    step_config_id: str,\n) -> list[ConfiguredNotebook]:\n    \"\"\"\n    Configure notebooks\n\n    Parameters\n    ----------\n    unconfigured_notebooks\n        Unconfigured notebooks\n\n    config_bundle\n        Configuration bundle from which to take configuration values\n\n    step_name\n        Name of the step\n\n    step_config_id\n        Step config ID to use when configuring the notebook\n\n    Returns\n    -------\n        Configured notebooks\n    \"\"\"\n    uc_nbs_dict = {nb.notebook_path: nb for nb in unconfigured_notebooks}\n\n    config = config_bundle.config_hydrated\n\n    config_step = get_config_for_step_id(\n        config=config, step=step_name, step_config_id=step_config_id\n    )\n    config_process = get_config_for_step_id(\n        config=config, step=\"process\", step_config_id=\"only\"\n    )\n\n    configured_notebooks = [\n        ConfiguredNotebook(\n            unconfigured_notebook=uc_nbs_dict[\n                Path(\"0aaa_quick-crunch\") / \"yyyy_quick-crunch-global-mean\"\n            ],\n            configuration=None,\n            dependencies=(\n                config_process.gggrn.processed_file_global_mean,\n                config_process.law_dome.processed_file,\n            ),\n            targets=(config_step.processed_data_file_global_means,),\n            config_file=config_bundle.config_hydrated_path,\n            step_config_id=step_config_id,\n        )\n    ]\n\n    return configured_notebooks\n\n\nstep = UnconfiguredNotebookBasedStep(\n    step_name=\"quick_crunch\",\n    unconfigured_notebooks=[\n        UnconfiguredNotebook(\n            notebook_path=Path(\"0aaa_quick-crunch\") / \"yyyy_quick-crunch-global-mean\",\n            raw_notebook_ext=\".py\",\n            summary=\"quick crunch - Create combined global-mean\",\n            doc=(\n                \"Create combined global-mean. This is just a placeholder until \"\n                \"we implemented a more sophisticated routine\"\n            ),\n        )\n    ],\n    configure_notebooks=configure_notebooks,\n)\n","repo_name":"climate-resource/CMIP-GHG-Concentration-Generation","sub_path":"src/local/notebook_steps/quick_crunch.py","file_name":"quick_crunch.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21518651707","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom color import *\n\nfont_size=20\nfont = {'size': font_size, 'family': 'Helvetica Neue', 'weight': 'medium'}\nplt.rc('font', family='Helvetica Neue', weight='medium')\nfig, ax = plt.subplots()\nfig.set_size_inches(8, 6)\nplt.subplots_adjust(top=0.95, right=0.95)\n\nx = [\"apache\", \"apache-build\", \"openssl\", \"Blowfish\", \"MD5\", \"povray\",\n        \"7zip\", \"AVG\"]\ngiantvm_gini = [0.11, 0.12, 0.07, 0.08, 0.08, 0.09, 0.09, 0.09]\nnaive_gini = [0.2, 0.19, 0.22, 0.22, 0.22, 0.23, 0.22, 0.21]\n\nxrange = np.arange(len(x));\n\ntitle = \"\"\ndot_style = ['s', 'x', 'd', '^', '.', 'D']\nline_style = [':', '-.', '--', '-']\nhatches = ['///', '\\\\\\\\\\\\', '----', '//////',\n           '++++', 'xxxxxx', '\\\\\\\\\\\\\\\\\\\\\\\\', '----']\n\nbar_width = 0.45\nbar_interval = bar_width\nbar_zoom = 0.85\nlinewidth = 4\n\nax.set_title(title, fontdict=font)\n\nplt.xticks(xrange, x, rotation=20)\n\nax.set_ylabel(\"Gini Coefficient\", fontdict=font)\nax.tick_params(axis='both', which='major', labelsize=font_size)\n\nax.set_ylim(0, 0.3)\n\nfor i in xrange:\n    naive_gini_bar = ax.bar(i-0.5*bar_interval, naive_gini[i], width=bar_width*bar_zoom,\n        bottom=0, color=color_red, edgecolor=\"black\",\n        label=\"Gini Baseline\")\n    giantvm_gini_bar = ax.bar(i+0.5*bar_interval, giantvm_gini[i], width=bar_width*bar_zoom,\n        bottom=0, color=color_green, edgecolor=\"black\",\n        label=\"Gini GiantVM\")\n\nax.legend((naive_gini_bar, giantvm_gini_bar),\n        (\"Gini w/o Yanni\", \"Gini w/ Yanni\"),\n        loc=1, prop={'size': 16}, ncol=2, frameon=False)\n\nplt.grid(True)\nplt.tight_layout()\nplt.savefig('../../imgs/macro2-gini.pdf', dpi=300)\nplt.show()\n","repo_name":"snake0/drawing-club","sub_path":"Yanni/scripts/macro2-gini.py","file_name":"macro2-gini.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"43817073032","text":"class Solution:\n    \"\"\"Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\"\"\"\n    def isValid(self, s: str) -> bool:\n        # The general idea is to maintain a stack where open parantheses are waiting to be matched up\n        # As they get matched up, they'll be popped off\n        # If s is valid, then by the end, the stack should be empty. \n        stack = []\n        closeToOpen = {\")\":\"(\",\n                      \"}\": \"{\",\n                       \"]\": \"[\"}\n        for c in s:\n            # If the character is a closing parenthesis\n            if c in closeToOpen:\n                # Make sure stack isn't empty and the top of stack is an open parenthesis\n                if stack and stack[-1] == closeToOpen[c]:\n                    stack.pop()\n                # invalid string (has to do with no matching open parenthesis for c)\n                else:\n                    return False\n            # This character is an opening parenthesis, and will be added to the stack\n            else:\n                stack.append(c)\n        # Not stack returns true if it's empty, false if it still has elements.\n        return not stack","repo_name":"aroy105/LeetCode","sub_path":"Stack/20-valid_parentheses.py","file_name":"20-valid_parentheses.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25361416185","text":"from flask import Flask, request, Response\nimport json\n\nimport config\nfrom solution_checker.solution_checker import check_solution\n\napp = Flask(__name__)\n\n\nclass ResponseJSON(Response):\n    default_mimetype = 'application/json'\n\n\n@app.errorhandler(400)\ndef handle_bad_request(e):\n    return json.dumps({'error': 'We accept only correct JSON.'}), 400\n\n\n@app.route('/check_solution', methods=['POST'])\ndef check_solution_view():\n    api_key = request.args.get('api_key', None)\n    if api_key != config.API_KEY:\n        response = json.dumps({'error': 'You need to provide correct api_key as GET param to access this API'})\n        return ResponseJSON(response, status=401)\n\n    check_request = request.json\n\n    if type(check_request) != dict:\n        response = json.dumps({'error': 'We accept only dict as a root element.'})\n        return ResponseJSON(response, status=400)\n\n    source_code, tests = check_request.get('sourceCode', None), check_request.get('tests', None)\n    if source_code is None or tests is None:\n        response = json.dumps({'error': 'Required \"sourceCode\" or \"tests\" fields are missing!'})\n        return ResponseJSON(response, status=400)\n\n    if type(source_code) != dict or type(tests) != list:\n        response = json.dumps({'error': '\"sourceCode\" must be dict and \"tests\" must be list'})\n        return ResponseJSON(response, status=400)\n\n    build_timeout = check_request.get('buildTimeout', config.DEFAULT_BUILD_TIMEOUT)\n    if build_timeout > config.MAX_BUILD_TIMEOUT:\n        response = json.dumps({'error': 'buildTimeout is too big, maximum allowed is {}'.format(config.MAX_BUILD_TIMEOUT)})\n        return ResponseJSON(response, status=401)\n\n    test_timeout = check_request.get('testTimeout', config.DEFAULT_TEST_TIMEOUT)\n    if test_timeout * len(tests) > config.MAX_TESTING_TIMEOUT:\n        response = json.dumps({'error': 'testTimeout is too big, maximum allowed timeout for ALL tests is {}'.format(config.MAX_TESTING_TIMEOUT)})\n        return ResponseJSON(response, status=401)\n\n    try:\n        check_result = check_solution(check_request['sourceCode'], check_request['tests'], build_timeout, test_timeout)\n        return ResponseJSON(check_result.json())\n    except Exception as e:\n        response = json.dumps({'error': str(e)})\n        return ResponseJSON(response, status=500)\n","repo_name":"LioKor/LioKorCode_Checker","sub_path":"flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"15931833630","text":"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.utils.data import DataLoader\n\nfrom sklearn.neighbors import KernelDensity\nfrom sklearn.model_selection import GridSearchCV\n\nimport numpy as np\n\n\nclass KDE(object):\n    \"\"\"\n        Class of Kernel Density Estimation models. Modified version of\n        https://github.com/lukasruff/Deep-SAD-PyTorch/blob/master/src/baselines/kde.py\n\n            Attributes:\n                kernel : str\n                    Distibution of the kernel\n                n_jobs : int\n                    Number of jobs\n                seed : int\n                    Seed for reproducibility\n                model : sklearn.neighbors.KernelDensity\n                    Kernel density model\n                bandwidth : float\n                    Bandwidth for the kernel\n    \"\"\"\n\n    def __init__(self, kernel='gaussian', n_jobs=-1, seed=None, **kwargs):\n        \"\"\"\n            Initialize KDE instance\n\n                Parameters:\n                    kernel : str\n                        Distribution of the kernel\n                    n_jobs : int\n                        Number of jobs\n                    seed : int\n                        Seed for reproducibility\n        \"\"\"\n        self.kernel = kernel\n        self.n_jobs = n_jobs\n        self.seed = seed\n\n        self.model = KernelDensity(kernel=kernel, **kwargs)\n        self.bandwidth = None\n\n\n    def train(self, dataset, device, batch_size=128): #n_jobs_dataloader,\n        \"\"\"\n            Finds best bandwidth and fits KDE model on dataset\n\n                Parameters:\n                    dataset : torch.tensor of shape (num_examples, num_features)\n                        Training dataset\n                    device : torch.device\n                        Torch device\n                    batch_size : int\n                        Batch size\n        \"\"\"\n\n        # do not drop last batch for non-SGD optimization shallow_ssad\n        train_loader = DataLoader(dataset=dataset, batch_size=batch_size,\n                                    shuffle=True, drop_last=False)\n\n        # Get data from loader\n        X = ()\n        for data in train_loader:\n            inputs = data.to(device)\n            X_batch = inputs.view(inputs.size(0), -1)\n            # X_batch.shape = (batch_size, n_channels * height * width)\n            X += (X_batch.cpu().data.numpy(),)\n        X = np.concatenate(X)\n\n        # Training\n        if self.bandwidth is None:\n            print(\"\\nSearching over bandwidths...\", end=\"\")\n            # use grid search cross-validation to select bandwidth\n            params = {'bandwidth': np.logspace(0.5, 5, num=10, base=2)}\n            hyper_kde = GridSearchCV(KernelDensity(kernel=self.kernel), params, \n                                     n_jobs=self.n_jobs, cv=5, verbose=0)\n            print(\"Done!\")\n\n            print(\"Fitting data...\", end=\"\")\n            hyper_kde.fit(X)\n            self.bandwidth = hyper_kde.best_estimator_.bandwidth\n            self.model = hyper_kde.best_estimator_\n            print(\"Done!\")\n        else:\n            self.model = KernelDensity(kernel=self.kernel, bandwidth=self.bandwidth)\n            self.model.fit(X)\n\n\n    def log_likelihood(self, samples, device):\n        \"\"\"\n            Returns the log likelihood of samples\n\n                Parameters:\n                    samples : torch.tensor of shape (num_samples, num_features)\n                        Samples\n                    device : torch.device\n                        Torch device\n\n                Returns:\n                    log likelihood : float\n                        log likelihood of samples\n        \"\"\"\n        log_g = self.model.score_samples(samples.cpu().data.numpy())\n        log_g = log_g.reshape(log_g.shape[0],1)\n        log_g = torch.from_numpy(log_g).to(device)\n        return log_g","repo_name":"ankithmo/estimateMI","sub_path":"estimateMI/KDE.py","file_name":"KDE.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"18415112578","text":"import allure\nimport pytest\n\nfrom lib.assertions import Assertions\nfrom lib.base_case import BaseCase\nfrom lib.my_requests import MyRequests\n\n@allure.epic('User registration cases')\nclass TestUserRegister(BaseCase):\n\n    exclude_data = {\n        'password',\n        'username',\n        'firstName',\n        'lastName',\n        'email'\n    }\n\n    def setup_method(self):\n        self.url = \"/user/\"\n\n    @allure.severity(allure.severity_level.BLOCKER)\n    @allure.description('Test if user can register successfully')\n    def test_create_user_successfully(self):\n        data = self.prepare_registration_data()\n\n        response = MyRequests.post(self.url, data=data)\n        Assertions.assert_json_has_key(response, \"id\")\n        Assertions.assert_code_status(response, 200)\n\n    @allure.severity(allure.severity_level.CRITICAL)\n    @allure.description('Test if user can register with existing mail')\n    def test_create_user_with_exc_email(self):\n        email = 'vinkotov@example.com'\n        data = self.prepare_registration_data(email)\n\n        response = MyRequests.post(self.url, data=data)\n\n        Assertions.assert_code_status(response, 400)\n        assert response.content.decode(\"utf-8\") == f\"Users with email '{email}' already exists\", \\\n            f\"Unexpected content [{response.content}]\"\n\n    @allure.severity(allure.severity_level.NORMAL)\n    @allure.description('Test if user can create user with wrong mail')\n    def test_create_user_with_wrong_email(self):\n        email = 'nikita.donkov.fancymail.com'\n        data = self.prepare_registration_data(email)\n\n        response = MyRequests.post(self.url, data=data)\n        Assertions.assert_code_status(response, 400)\n\n    @allure.severity(allure.severity_level.MINOR)\n    @allure.description('Tests if user can register with no fields')\n    @pytest.mark.parametrize('condition', exclude_data)\n    def test_create_user_with_no_field(self, condition):\n        data = self.prepare_registration_data()\n        self.delete_field_in_data(data, condition)\n\n        response = MyRequests.post(self.url, data=data)\n        Assertions.assert_code_status(response, 400)\n        assert response.content.decode(\"utf-8\") == f\"The following required params are missed: {condition}\", \\\n            f\"Unexpected content [{response.content}]\"\n\n    @allure.severity(allure.severity_level.MINOR)\n    @allure.description('Test if user can register with 1 char username')\n    def test_create_user_with_onechar_username(self):\n        data = self.prepare_registration_data()\n        data['username'] = 'q'\n\n        response = MyRequests.post(self.url, data=data)\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_content(response, f\"The value of 'username' field is too short\")\n\n    @allure.severity(allure.severity_level.MINOR)\n    @allure.description('Test if user can register with too long username')\n    def test_create_user_with_long_username(self):\n        data = self.prepare_registration_data()\n        data['username'] = self.generate_string(255)\n\n        response = MyRequests.post(self.url, data=data)\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_content(response, f\"The value of 'username' field is too long\")\n","repo_name":"Seqils/LearnQA_API_Framework","sub_path":"tests/test_user_register.py","file_name":"test_user_register.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2065822438","text":"import wget\nimport os\n\nclass DownloadHelper(object):\n    def __init__(self, data_url=\"https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data\", data_file_name=\"sonar.all-data\",directory=\"data\"):\n        self.data_url = data_url\n        self.directory = directory\n        self.data_file_name = data_file_name\n\n    def download_data(self, debug=False):\n        data_dir = self.download_directory_path()\n        data_path = self.data_file_path()\n        if not os.path.exists(data_dir):\n            # create the download directory if it exists\n            os.makedirs(data_dir)\n        if not os.path.isfile(data_path):\n            wget.download(self.data_url, out=data_dir)\n        # always return the data_path\n        return data_path\n\n    def data_file_path(self):\n        return os.path.abspath(\n            os.path.join(self.download_directory_path(), self.data_file_name)\n        )\n\n    def download_directory_path(self):\n        \"\"\"\n        get the absolute path of week5/data directory\n        :return:\n        \"\"\"\n        return os.path.abspath(\n            # use dirname to get to the parent directory of crawler\n            os.path.join(os.path.dirname(self.parent_dir()), self.directory)\n        )\n\n    def parent_dir(self):\n        \"\"\"\n        :return: the absolute path of the directory of this file, the absolute path of crawler module\n        \"\"\"\n        # file is absolute, os.curdir() and os.pardir() is relative\n        return os.path.dirname(__file__)","repo_name":"yingding/hca17","sub_path":"extra1/crawler/downloadhelper.py","file_name":"downloadhelper.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11693924310","text":"# -*- coding:utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport sklearn.datasets as ds\nimport matplotlib.colors\nfrom sklearn.cluster import KMeans\n\n## 设置属性防止中文乱码\nmpl.rcParams['font.sans-serif'] = [u'SimHei']\nmpl.rcParams['axes.unicode_minus'] = False\n\n# 产生模拟数据\nN = 1500\ncenters = 4\ndata, y = ds.make_blobs(N, n_features=2, centers=centers, random_state=28)\ndata2, y2 = ds.make_blobs(N, n_features=2, centers=centers, random_state=28)\ndata3 = np.vstack((data[y == 0][:200], data[y == 1][:100], data[y == 2][:10], data[y == 3][:50]))\ny3 = np.array([0] * 200 + [1] * 100 + [2] * 10 + [3] * 50)\nprint(data.shape)\nprint(data3.shape)\nprint(y.shape)\n\n# 一、数据前期处理前面模型是一样\n# 二、模型的构建\nkm = KMeans(n_clusters=centers, init='random', random_state=28)\n# n_clusters就是k值，也就是聚类值\n# init初始化方法，可以是kmeans++，随机，或者自定义的array\nkm.fit(data, y)\n\ny_hat = km.predict(data)\nprint(\"所有样本距离聚簇中心点的总距离和:\", km.inertia_)\nprint(\"距离聚簇中心点的平均距离:\", (km.inertia_ / N))\ncluster_centers = km.cluster_centers_\nprint(\"聚簇中心点：\", cluster_centers)\n\ny_hat2 = km.predict(data2)\ny_hat3 = km.predict(data3)\n\n\ndef expandBorder(a, b):\n    d = (b - a) * 0 / 1\n    return a - d, b + d\n\n\n# 画图\ncm = mpl.colors.ListedColormap(list('rgbmyc'))\nplt.figure(figsize=(15, 9), facecolor='w')\nplt.subplot(241)\nplt.scatter(data[:, 0], data[:, 1], c=y, s=30, cmap=cm, edgecolors='none')\nx1_min, x2_min = np.min(data, axis=0)\nx1_max, x2_max = np.max(data, axis=0)\nx1_min, x1_max = expandBorder(x1_min, x1_max)\nx2_min, x2_max = expandBorder(x2_min, x2_max)\nplt.xlim(x1_min, x2_max)\nplt.ylim(x2_min, x2_max)\nplt.title('原始数据')\nplt.grid(True)\n\nplt.subplot(242)\nplt.scatter(data[:, 0], data[:, 1], c=y_hat, s=30, cmap=cm, edgecolors='none')\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('K-Means算法聚类结果')\nplt.grid(True)\n\nm = np.array(((1, 1), (0.5, 5)))\ndata_r = data.dot(m)\ny_r_hat = km.fit_predict(data_r)\nplt.subplot(243)\nplt.scatter(data_r[:, 0], data_r[:, 1], c=y, s=30, cmap=cm, edgecolors='none')\n\nx1_min, x2_min = np.min(data_r, axis=0)\nx1_max, x2_max = np.max(data_r, axis=0)\nx1_min, x1_max = expandBorder(x1_min, x1_max)\nx2_min, x2_max = expandBorder(x2_min, x2_max)\n\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('数据旋转后原始数据图')\nplt.grid(True)\n\nplt.subplot(244)\nplt.scatter(data_r[:, 0], data_r[:, 1], c=y_r_hat, s=30, cmap=cm, edgecolors='none')\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('数据旋转后预测图')\nplt.grid(True)\n\nplt.subplot(245)\nplt.scatter(data2[:, 0], data2[:, 1], c=y2, s=30, cmap=cm, edgecolors='none')\nx1_min, x2_min = np.min(data2, axis=0)\nx1_max, x2_max = np.max(data2, axis=0)\nx1_min, x1_max = expandBorder(x1_min, x1_max)\nx2_min, x2_max = expandBorder(x2_min, x2_max)\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('不同方差的原始数据')\nplt.grid(True)\n\nplt.subplot(246)\nplt.scatter(data2[:, 0], data2[:, 1], c=y_hat2, s=30, cmap=cm, edgecolors='none')\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('不同方差簇数据的K-Means算法聚类结果')\nplt.grid(True)\n\nplt.subplot(247)\nplt.scatter(data3[:, 0], data3[:, 1], c=y3, s=30, cmap=cm, edgecolors='none')\nx1_min, x2_min = np.min(data3, axis=0)\nx1_max, x2_max = np.max(data3, axis=0)\nx1_min, x1_max = expandBorder(x1_min, x1_max)\nx2_min, x2_max = expandBorder(x2_min, x2_max)\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('不同簇样本数量原始数据图')\nplt.grid(True)\n\nplt.subplot(248)\nplt.scatter(data3[:, 0], data3[:, 1], c=y_hat3, s=30, cmap=cm, edgecolors='none')\nplt.xlim((x1_min, x1_max))\nplt.ylim((x2_min, x2_max))\nplt.title('不同簇样本数量的K-Means算法聚类结果')\nplt.grid(True)\n\nplt.tight_layout(2, rect=(0, 0, 1, 0.97))\nplt.suptitle('数据分布对KMeans聚类的影响', fontsize=18)\nplt.show()\n","repo_name":"AidenLong/ai","sub_path":"ml/04_julei/01_kmeans.py","file_name":"01_kmeans.py","file_ext":"py","file_size_in_byte":4050,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"28086332437","text":"import random \r\nimport math\r\nimport pygame\r\n\r\npygame.init()\r\npygame.display.set_caption('Convex Hull')\r\nWIDTH, HEIGHT = 750, 750\r\nOFFSET = 10\r\nWHITE = (245, 245, 245)\r\nRED =  (255, 50, 50)\r\nGRAY = (64, 64, 64)\r\n\r\npoints = [(random.randrange(OFFSET, WIDTH - OFFSET), random.randrange(OFFSET, HEIGHT - OFFSET)) for _ in range(100)]\r\np0 = 0\r\nfps = 10\r\nstarted = 0\r\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\r\nfpsclock = pygame.time.Clock()\r\n\r\ndef orientation(P, Q, R):\r\n    (xp, yp), (xq, yq), (xr, yr) = P, Q, R\r\n    return xq * (yr - yp) + xp * (yq - yr) + xr * (yp - yq)\r\n\r\ndef dist(p1, p2):\r\n    x1, y1, x2, y2 = *p1, *p2\r\n    return (y2 - y1) ** 2 + (x2 - x1) ** 2\r\n\r\ndef polar_angle(p1, p2):\r\n    if p1[1] == p2[1]:\r\n        return -math.pi\r\n    dy = p1[1] - p2[1]\r\n    dx = p1[0] - p2[0]\r\n    return math.atan2(dy, dx)\r\n\r\ndef draw(all_points, *hull_points):\r\n    screen.fill(GRAY)\r\n    for x, y in all_points:\r\n        pygame.draw.circle(screen, WHITE, (x, y), 8)\r\n\r\n    for pts in hull_points:\r\n        for curr, nxt in zip(pts, pts[1:]):\r\n            pygame.draw.line(screen, RED, curr, nxt, 4)\r\n        try:\r\n            pygame.draw.line(screen, RED, pts[0], hull_points[1], 4)\r\n        except:\r\n            pass\r\n    fpsclock.tick(fps)\r\n    pygame.display.update()\r\n\r\ndef Graham_Scan():\r\n    global points, p0\r\n    p0 = min(points, key=lambda p: (p[1], p[0]))\r\n    points.sort(key=lambda p: (polar_angle(p0, p), dist(p0, p)))\r\n    hull = []\r\n    for p in points:\r\n        while len(hull) >= 2 and orientation(hull[-2], hull[-1], p) <= 0:\r\n            hull.pop()\r\n        hull.append(p)\r\n        draw(points, hull)\r\n\r\n    hull.append(points[0])\r\n    return hull\r\n\r\ndef Andrew():\r\n    global points \r\n    points.sort()\r\n    lower = []\r\n    for p in points:\r\n        while len(lower) >= 2 and orientation(lower[-2], lower[-1], p) <= 0:\r\n            lower.pop()\r\n        lower.append(p)\r\n        draw(points, lower)\r\n\r\n    higher = []\r\n    for p in points:\r\n        while len(higher) >= 2 and orientation(higher[-2], higher[-1], p) >= 0:\r\n            higher.pop()\r\n        higher.append(p)\r\n        draw(points, lower, higher)\r\n\r\n    return lower[::-1] + higher[1:-1] + [lower[-1]]\r\n\r\ndef main():\r\n    # hull = Andrew()\r\n    hull = Graham_Scan()\r\n\r\n    while True:\r\n        draw(points, hull)\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                exit(0)\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"Radu-Antonio/Advanced-Algorithms-Visualizations","sub_path":"convexhull_visualization.py","file_name":"convexhull_visualization.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10531282103","text":"import cv2 as cv #computer vison\nimport pickle # id extraction aid\n#\n#cap = cv.VideoCapture(0) #web cam stream\n#\nhaar_cascade=cv.CascadeClassifier('data/haarcascade_frontalface_alt2.xml') # haar cascade model\n\nrecognizer = cv.face.LBPHFaceRecognizer_create() # initiate face recognizer\nrecognizer.read('trainer.yml') # calls trained model\n\nlabels = {} # blank dictionary  to accomodate labels\n\nwith open(\"labels.pickle\",'rb') as f:\n    og_labels = pickle.load(f)\n    labels ={v:k for k,v in og_labels.items()} # invers order of label\n\n\nimg = cv.imread(\"input.jpg\")\n\ngray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)\nface_rect = haar_cascade.detectMultiScale(gray, 1.1, 10)\nfor(x,y,w,h) in face_rect:\n    box = cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),thickness=2)\n    roi_gray = gray[y:y+h,x:x+w]\n    id_, conf = recognizer.predict(roi_gray)\n    print(conf)\n    if conf >= 50:\n        print(labels[id_])\n        cv.putText(img,labels[id_],(x,y),cv.FONT_HERSHEY_TRIPLEX,1 , (0,255,0))\n        print(labels[id_])\n\n        cv.imshow(\"Output\",img)\ncv.waitKey(0)\n\n","repo_name":"roboticvedant/you_look_close_to","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36138497739","text":"import tensorflow as tf\r\n\r\n'''\r\n우선 이 소스에서는 feeding이 어떻게 이루어 지는 과정을 좀 살펴 볼 필요가 있다.\r\nTensorFlow는 Graph를 이용하여 이를 처리하고 있다고 하는데 이 의미는 무엇인가?\r\n'''\r\n\r\ntf.set_random_seed(777)\r\n\r\nW = tf.Variable(tf.random_normal([1]), name='weight')\r\nb = tf.Variable(tf.random_normal([1]), name='bias')\r\n\r\n'''\r\n위에서 X,Y에 대해 각각 float32로 placeholder를 선언 하였으니 이는 값을 가지고 있다가 Operation으로 보내는\r\nNode라고 보면 되는 건가?\r\nHere, we declare X, Y as a placeholder to hold a float32 value. So can we consider it as a node?\r\n'''\r\nX = tf.placeholder(tf.float32, shape=[None])\r\nY = tf.placeholder(tf.float32, shape=[None])\r\n\r\nhypothesis = X * W + b\r\n\r\ncost = tf.reduce_mean(tf.square(hypothesis - Y))\r\n\r\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)\r\ntrain = optimizer.minimize(cost)\r\n\r\nsess = tf.Session()\r\n\r\nsess.run(tf.global_variables_initializer())\r\n\r\nfor step in range(2001):\r\n    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X : [1, 2, 3], Y : [1, 2, 3]})\r\n\r\n    if step == 2000 :\r\n        print(step, cost_val, W_val, b_val)\r\n\r\nprint(sess.run(hypothesis, feed_dict={X: [5]}))\r\nprint(sess.run(hypothesis, feed_dict={X: [1.5, 1.1]}))\r\n\r\nfor step in range(2001):\r\n    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train],\r\n                                         feed_dict={X : [1, 2, 3, 4, 5], Y : [2.1, 3.1, 4.1, 5.2, 6.3]})\r\n\r\n    if step == 2000 :\r\n        print(step, cost_val, W_val, b_val)\r\n\r\nprint(sess.run(hypothesis, feed_dict={X: [5]})) #Upon the result of training, our hypotesis inform us the answer of '5' is....what...\r\nprint(sess.run(hypothesis, feed_dict={X: [1.5, 1.1]}))\r\n","repo_name":"tladus-git/Tensorflow-practice","sub_path":"Linear Regression/linear_regression_feed_roh.py","file_name":"linear_regression_feed_roh.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15886456133","text":"import argparse\nimport copy\nimport glob\nimport os\nfrom pathlib import Path\nimport sys\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport tqdm\n\n# define the global base device\nworld_size = torch.cuda.device_count()\nif world_size:\n    device = torch.device(\"cuda:0\")\n    for i in range(world_size):\n        print(f\"Device {i}: {torch.cuda.get_device_name(i)}\")\nelse:\n    device = \"cpu\"\n    print(\"Device: CPU\")\n\nimport numpy as np\n\nSEED = 12345\n_ = np.random.seed(SEED)\n_ = torch.manual_seed(SEED)\n\nimport h5py as h5\nfrom iftool.image_challenge import ParticleImage2D\nfrom iftool.image_challenge import collate\nfrom torch.utils.data import DataLoader\nfrom src.models.CNN_dropout import CNN_dropout\n\nproject_dir = Path(__file__).resolve().parents[2]\n\n\ndef main(args):\n    outdir = args.outdir\n    label = args.label\n    model_loc = f\"{outdir}/trained_models/\"\n    model_perf_loc = f\"{outdir}/model_performances/{label}\"\n    os.system(\n        f\"mkdir -p {model_loc} {model_perf_loc}\"\n    )  # -p: create parent dirs if needed, exist_ok\n\n    datapath = args.datapath\n    # Open a file in 'r'ead mode.\n    f = h5.File(datapath, mode=\"r\", swmr=True)\n\n    # List items in the file\n    for key in f.keys():\n        print(\"dataset\", key, \"... type\", f[key].dtype, \"... shape\", f[key].shape)\n\n    train_data = ParticleImage2D(data_files=[datapath])\n\n    train_data = ParticleImage2D(\n        data_files=[datapath],\n        start=0.0,  # start of the dataset fraction to use. 0.0 = use from 1st entry\n        end=args.train_size,  # end of the dataset fraction to use. 1.0 = use up the last entry\n    )\n    val_data = ParticleImage2D(\n        data_files=[datapath],\n        start=1.0\n        - args.val_size,  # start of the dataset fraction to use. 0.0 = use from 1st entry\n        end=1.0,  # end of the dataset fraction to use. 1.0 = use up the last entry\n    )\n\n    # We use a specifically designed \"collate\" function to create a batch data\n    f.close()\n    batch_size = args.batch_size\n    train_loader = DataLoader(\n        train_data,\n        collate_fn=collate,\n        shuffle=True,\n#                                   num_workers = 2,\n        batch_size=batch_size,\n    )\n    val_loader = DataLoader(\n        val_data,\n        collate_fn=collate,\n        shuffle=True,\n#                                   num_workers = 2,\n        batch_size=batch_size,\n    )\n    learning_rate = 0.001\n\n    model = CNN_dropout().to(device=device)\n    criterion = nn.CrossEntropyLoss()\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n    n_total_steps = len(train_loader)\n\n    train_its = int(len(train_data) / batch_size)\n    val_its = int(len(val_data) / batch_size)\n\n    train_losses = []\n    val_losses = []\n    val_accs = []\n    val_acc_best = 0\n    epochs = args.epoch\n\n    for epoch in range(epochs):\n        l_train_epoch = []\n        l_val_epoch = []\n        model.train()\n        pbar = tqdm.tqdm(train_loader, total=train_its)\n        for _, batch in enumerate(pbar):\n            images = batch[\"data\"]\n            labels = batch[\"label\"]\n            images = images.to(device)\n            labels = labels.to(device)\n\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            loss = loss.detach().cpu().item()\n            l_train_epoch.append(loss)\n            pbar.set_description(f\"Training loss: {loss:.4f}\")\n\n        l_train = np.mean(l_train_epoch)\n        train_losses.append(l_train)\n        print(f\"Epoch [{epoch+1}/{epochs}], Train Loss: {l_train:.4f} \")\n\n        model.eval()\n        with torch.no_grad():\n            correct = 0\n            total = 0\n            pbar = tqdm.tqdm(val_loader, total=val_its)\n            for i, batch in enumerate(pbar):\n                images = batch[\"data\"]\n                labels = batch[\"label\"]\n                images = images.to(device)\n                labels = labels.to(device)\n\n                outputs = model(images)\n                loss = criterion(outputs, labels)\n                loss = loss.detach().cpu().item()\n                l_val_epoch.append(loss)\n                pbar.set_description(f\"Validation loss: {loss:.4f}\")\n                _, predicted = torch.max(outputs.data, 1)\n                total += labels.size(0)\n                correct += (predicted == labels).sum().item()\n                del images, labels, outputs\n\n        val_acc = correct / total\n        print(f\"val accuracy: {val_acc}\")\n        val_accs.append(val_acc)\n\n        l_val = np.mean(l_val_epoch)\n        print(f\"Epoch [{epoch+1}/{epochs}], Val Loss: {l_val:.4f} \")\n        if val_acc > val_acc_best:\n            val_acc_best = val_acc\n            print(\"new best model\")\n            torch.save(model.state_dict(), f\"{model_loc}/CNN_dropout_{label}_best.pth\")\n        torch.save(model.state_dict(), f\"{model_loc}/CNN_dropout_{label}_last.pth\")\n        val_losses.append(l_val)\n\n    # after training, save the losses and accuracies\n    np.save(f\"{model_perf_loc}/train_losses.npy\", np.array(train_losses))\n    np.save(f\"{model_perf_loc}/val_losses.npy\", np.array(val_losses))\n    np.save(f\"{model_perf_loc}/val_accs.npy\", np.array(val_accs))\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"CNN\")\n    parser.add_argument(\n        \"--datapath\",\n        type=str,\n        default=f\"{project_dir}/data/if-image-train.h5\",\n        metavar=\"N\",\n        help=\"path to data\",\n    )\n    parser.add_argument(\n        \"--train-size\",\n        type=float,\n        default=0.9,\n        help=\"Percentage of data to use for training\",\n    )\n    parser.add_argument(\n        \"--val-size\",\n        type=float,\n        default=0.1,\n        help=\"Percentage of data to use for validation\",\n    )\n    parser.add_argument(\n        \"--epoch\",\n        type=int,\n        default=50,\n        help=\"Epochs to train for\",\n    )\n    parser.add_argument(\n        \"--batch-size\",\n        type=int,\n        default=128,\n        help=\"Batch size\",\n    )\n    parser.add_argument(\n        \"--outdir\",\n        type=str,\n        action=\"store\",\n        dest=\"outdir\",\n        default=f\"{project_dir}/models/\",\n        help=\"Output directory\",\n    )\n    parser.add_argument(\n        \"--label\",\n        type=str,\n        action=\"store\",\n        dest=\"label\",\n        default=\"baseline\",\n        help=\"label\",\n    )\n    args = parser.parse_args()\n    main(args)\n","repo_name":"JavierZhao/ssi_2023_if_1","sub_path":"src/models/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":6454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23916423446","text":"# def mul(a):\n#     t=[]\n#     for i in range(len(l):\n#         return t\n# tl=l.pop()\n#     m=mul(t1))\n#     o.append(m)\n# a=[1,2,3,4]\n# print(a)\n\nx = input('Write Text: ')\nz=[]\nfor character in x:\n    number = ord(character) - 87\n    # str1 = ''.join(z)\n    z.append(str(number))\nprint(''.join(z))\n# # ord() returns the integer ordinal of a character string. So ord('A') will return 97, the ASCII value for A.\n\n\n\n\n\n\n","repo_name":"revathigit98/revathi","sub_path":"hellopy/py1.py","file_name":"py1.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30346405826","text":"import pytest\nimport rastro\n\nif __name__ == '__main__':\n    ecc = 45.0 # Starting eccentric anomaly\n    e = 0.01  # Eccentricity\n\n    # Convert to mean anomaly\n    mean_anomaly = rastro.anomaly_eccentric_to_mean(ecc, e, True)\n\n    # Convert back from mean to eccentric anomaly\n    ecc_2 = rastro.anomaly_mean_to_eccentric(mean_anomaly, e, True)\n\n    # Confirm equality to within tolerance\n    assert ecc == pytest.approx(ecc_2, abs=1e-14)","repo_name":"duncaneddy/rastro","sub_path":"docs/examples/anomaly_eccentric_and_mean.py","file_name":"anomaly_eccentric_and_mean.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"13784678393","text":"from __future__ import annotations\n\nimport asyncio\nimport concurrent.futures\nimport threading\nfrom typing import Any, Coroutine, Optional, TypeVar\n\nTReturn = TypeVar(\"TReturn\")\nTSelf = TypeVar(\"TSelf\", bound=\"EventLoopThread\")\n\n\nclass EventLoopThread(threading.Thread):\n    \"\"\"An event loop thread that provides thread-safe utility functions.\n\n    .. note::\n        The thread cannot be started more than once because of the constraints\n        of :class:`threading.Thread`. The class raises :class:`RuntimeError` if\n        it is already terminated.\n\n    Example:\n        >>> async def _get_ident() -> int:\n        ...     return threading.get_ident()\n        ...\n        >>> thread = EventLoopThread()\n        >>> with thread:\n        ...     main, sub = await asyncio.gather(\n        ...         _get_ident(),\n        ...         thread.run_coroutine(_get_ident()),\n        ...     )\n        >>> main == sub\n        False\n\n    \"\"\"\n\n    def __init__(\n        self,\n        loop_policy: Optional[asyncio.AbstractEventLoopPolicy] = None,\n        daemon: bool = False,\n        start: bool = False,\n    ) -> None:\n        \"\"\"Creates a new event loop thread.\n\n        Args:\n            loop_policy:\n                A :obj:`asyncio.AbstractEventLoopPolicy` object to be used\n                for creating a new event loop. If :obj:`None` is specified,\n                ``asyncio.get_event_loop_policy()`` is used to get the policy.\n            daemon:\n                If ``True`` is specified, the thread is created with ``daemon=True``.\n                Refer to `threading.Thread.daemon`_ for further details.\n\n                .. _threading.Thread.daemon:\n                    https://docs.python.org/3/library/threading.html#threading.Thread.daemon\n            start:\n                If ``True`` is specified, the thread is started immediately.\n        \"\"\"\n        self._loop_policy = loop_policy\n\n        self._lock = threading.Lock()\n        self._future: concurrent.futures.Future[None] = concurrent.futures.Future()\n        self._loop: Optional[asyncio.AbstractEventLoop] = None\n\n        super().__init__(target=self._target_impl, daemon=daemon)\n\n        if start:\n            self.start()\n\n    def _target_impl(self) -> None:\n        future = self._future\n        loop: asyncio.AbstractEventLoop\n        try:\n            loop_policy = self._loop_policy\n            if loop_policy is None:\n                loop_policy = asyncio.get_event_loop_policy()\n\n            loop = loop_policy.new_event_loop()\n        except Exception as ex:\n            future.set_exception(ex)\n\n        try:\n            self._loop = loop\n            future.set_result(None)\n\n            asyncio.set_event_loop(loop)\n            loop.run_forever()\n        finally:\n            self._loop = None\n\n    def get_loop(self) -> asyncio.AbstractEventLoop:\n        \"\"\"Get an event loop of the running thread.\n\n        The thread must be running. The method will raise :class:`RuntimeError`\n        if the thread is not running.\n\n        See also:\n            Use :attr:`EventLoopThread.loop` if you need an accessor to the loop.\n        \"\"\"\n        loop = self._loop\n        if loop is None:\n            raise RuntimeError(\"Thread is not running\")\n\n        return loop\n\n    @property\n    def loop(self) -> asyncio.AbstractEventLoop:\n        \"\"\"Get an event loop of the running thread.\n\n        The property just returns the return value of :func:`EventLoopThread.get_loop`.\n        \"\"\"\n        return self.get_loop()\n\n    def start(self) -> None:\n        \"\"\"Start the thread and wait for a new event loop to be ready.\n\n        If the thread is already started, it returns immediately.\n\n        Raises:\n            RuntimeError:\n                If the thread is already terminated.\n        \"\"\"\n        with self._lock:\n            if self.is_alive():\n                return\n\n            if self._future.done():\n                raise RuntimeError(\"threads can only be started once\")\n\n            super().start()\n            # Wait until loop is created\n            self._future.result()\n\n    def shutdown(self, join: bool = True) -> None:\n        \"\"\"Shutdown the running event loop to terminate the thread.\n\n        If the event loop is not running, it returns immediately.\n\n        Args:\n            join:\n                If `True` is specified, the method waits for the thread to be terminated.\n\n        Raises:\n            RumtimeError:\n                The method raises a :class:`RuntimeError` if ``join = True`` and the method\n                is called by the same thread as ``self.loop`` in order to avoid a deadlock.\n                See `threading.Thread.join`_ for further details.\n\n        .. _threading.Thread.join:\n            https://docs.python.org/3/library/threading.html#threading.Thread.join\n        \"\"\"\n        loop = self._loop\n        if loop is None:\n            return\n\n        running_loop = loop\n        assert isinstance(running_loop, asyncio.AbstractEventLoop)\n        running_loop.call_soon_threadsafe(lambda: running_loop.stop())\n        if join:\n            self.join()\n\n    def __enter__(self: TSelf) -> TSelf:\n        \"\"\"Initialize the event loop if it is not started.\"\"\"\n        self.start()\n        return self\n\n    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:\n        \"\"\"Shutdown the event loop if it is running.\"\"\"\n        self.shutdown()\n\n    def run_coroutine_concurrent(\n        self, coro: Coroutine[Any, Any, TReturn]\n    ) -> concurrent.futures.Future[TReturn]:\n        \"\"\"Submit a coroutine in the event loop.\n\n        Args:\n            coro: A `Coroutine` object to run.\n\n        Returns:\n            A :class:`concurrent.future.Future` object that returns the execution result of\n            a given coroutine.\n        \"\"\"\n        loop = self.loop\n        return asyncio.run_coroutine_threadsafe(coro, loop)\n\n    def run_coroutine(\n        self,\n        coro: Coroutine[Any, Any, TReturn],\n        *,\n        loop: Optional[asyncio.AbstractEventLoop] = None,\n    ) -> asyncio.Future[TReturn]:\n        \"\"\"Submit a coroutine in a new thread and waits for its completion in a given ``loop``.\n\n        Args:\n            coro: A `Coroutine` object to run.\n            loop: An event loop to wait for the completion of ``coro``.\n\n        Returns:\n            A :class:`asyncio.Future` object that returns the execution result of a given\n            coroutine.\n        \"\"\"\n        future = self.run_coroutine_concurrent(coro)\n        return asyncio.wrap_future(future, loop=loop)\n\n\ndef run_coroutine_in_thread(\n    coro: Coroutine[Any, Any, TReturn],\n    *,\n    loop: Optional[asyncio.AbstractEventLoop] = None,\n) -> asyncio.Future[TReturn]:\n    \"\"\"Submit a coroutine in a new thread and waits for its completion in a given ``loop``.\n\n    Example:\n        >>> async def _get_ident() -> int:\n        ...     return threading.get_ident()\n        ...\n        >>> main, sub = await asyncio.gather(\n        ...     _get_ident(),\n        ...     asyncx.run_coroutine_in_thread(_get_ident()),\n        ... )\n        >>> main == sub\n        False\n\n    Args:\n        coro: A coroutine to run in a new thread.\n        loop: An event loop to wait for the completion of ``coro``.\n\n    Returns:\n        A :class:`asyncio.Future` object that returns the execution result of\n        a given coroutine.\n    \"\"\"\n\n    thread = EventLoopThread(start=True)\n\n    async def impl() -> TReturn:\n        try:\n            return await coro\n        finally:\n            thread.shutdown(join=False)\n\n    return thread.run_coroutine(impl(), loop=loop)\n","repo_name":"bonprosoft/asyncx","sub_path":"asyncx/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":7553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6127848872","text":"from pathlib import Path\nimport pandas as pd\nimport os\nfrom collections import Counter\n\ndatapath = Path(__file__).resolve().parents[2]\n\ndef gazetteer_allenNLP(data,data1):\n    count = 0\n    notgazetteer_location_list = []\n    notgazetteer_person_list = []\n    allenNLP_location_list = []\n    allenNLP_person_list = []\n\n    for index,row in data.iterrows():\n        print(index)\n        sentence = row['sentence']\n        person = row['Person']\n        location = row['Location']\n        gazetteer_person = row['Gazzeter Person']\n        gazetteer_location = row['Gazzeter Location']\n\n        person_list = []\n        location_list = []\n        gazetteer_person_list = []\n        gazetteer_location_list = []\n\n        if not isinstance(person,float):\n            person_list = person.split(\",\")\n        if not isinstance(location, float):\n            location_list = location.split(\",\")\n        if not isinstance(gazetteer_person, float):\n            gazetteer_person_list = gazetteer_person.split(\",\")\n        if not isinstance(gazetteer_location, float):\n            gazetteer_location_list = gazetteer_location.split(\",\")\n\n        person_list = [item.strip() for item in person_list]\n        location_list = [item.strip() for item in location_list]\n        gazetteer_person_list = [item.strip() for item in gazetteer_person_list]\n        gazetteer_location_list = [item.strip() for item in gazetteer_location_list]\n\n        common_person = sorted(list((Counter(person_list) & Counter(gazetteer_person_list)).elements()))\n        common_location = sorted(list((Counter(location_list) & Counter(gazetteer_location_list)).elements()))\n\n        notgazetteer_person_li = list((Counter(person_list) - Counter(common_person)).elements())\n        notgazetteer_location_li = list((Counter(location_list) - Counter(common_location)).elements())\n\n        notgazetteer_person = ','.join([str(elem) for elem in notgazetteer_person_li])\n        notgazetteer_location = ','.join([str(elem) for elem in notgazetteer_location_li])\n\n        allenNLP = data1.iloc[index]\n        allenNLP_person = allenNLP['allenNLP Person']\n        allenNLP_location= allenNLP['allenNLP Location']\n\n        notgazetteer_location_list.append(notgazetteer_location)\n        notgazetteer_person_list.append(notgazetteer_person)\n        allenNLP_location_list.append(allenNLP_location)\n        allenNLP_person_list.append(allenNLP_person)\n\n        count = count + 1\n        print(count)\n\n    data['allenNLP Location'] = allenNLP_location_list\n    data['allenNLP Person'] = allenNLP_person_list\n    data['notgazetteer Location'] = notgazetteer_location_list\n    data['notgazetteer Person'] = notgazetteer_person_list\n\n    return data\n\nbook = ['part1','part2','part3']\n#book = ['part1']\n\nfor part in book:\n\n    readfile = datapath / 'results/hugh-murray/{}/ner/gazetteer-murray-yule-special-index.csv'.format(part)\n    data = pd.read_csv(readfile, sep='\\t', encoding='latin1', error_bad_lines=False)\n\n    readfile = datapath / 'results/hugh-murray/{}/ner/allenNLP.csv'.format(part)\n    data1 = pd.read_csv(readfile, sep='\\t', encoding='latin1', error_bad_lines=False)\n\n    outdata = gazetteer_allenNLP(data,data1)\n\n    if not os.path.exists(datapath / 'results/hugh-murray/{}/ner'.format(part)):\n       os.makedirs(datapath / 'results/hugh-murray/{}/ner'.format(part))\n\n    writefile = datapath / 'results/hugh-murray/{}/ner/gazetteer-allenNLP.csv'.format(part)\n    if os.path.exists(writefile):\n        os.remove(writefile)\n\n    outdata.to_csv(writefile, sep='\\t', encoding='latin1',index=False)","repo_name":"rutabakhda/retracing-the-travelogue","sub_path":"src/ner-taggers/gazetteer-allenNLP.py","file_name":"gazetteer-allenNLP.py","file_ext":"py","file_size_in_byte":3557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13842314329","text":"class Solution:\n    def targetIndices(self, nums: List[int], target: int) -> List[int]:\n        nums.sort()\n        \n        if target not in nums:\n            return []\n        indexs = []\n        for i in range(len(nums)):\n            if nums[i] == target:\n                indexs.append(i)\n        \n        return indexs","repo_name":"natnael-meresa/Competitive-Programming","sub_path":"week1/day3/findTarget.py","file_name":"findTarget.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26260029514","text":"import os\nimport sys\n\nimport reframe as rfm\nfrom hpctestlib.microbenchmarks.gpu.kernel_latency import GpuKernelLatency\n\nsys.path.append(os.path.abspath(os.path.join(__file__, '../../../..')))\nimport microbenchmarks.gpu.hooks as hooks\n\n\n@rfm.simple_test\nclass gpu_kernel_latency_check(GpuKernelLatency):\n    valid_systems = [\n        'daint:gpu', 'dom:gpu', 'arolla:cn', 'tsa:cn', 'ault:amdv100',\n        'ault:intelv100', 'ault:amda100', 'ault:amdvega'\n    ]\n    num_tasks = 0\n    sys_reference = variable(dict, value={\n        'sync': {\n            'dom:gpu': {\n                'latency': (6.6, None, 0.10, 'us')\n            },\n            'daint:gpu': {\n                'latency': (6.6, None, 0.10, 'us')\n            },\n            'ault:intelv100': {\n                'latency': (7.15, None, 0.10, 'us')\n            },\n            'ault:amdv100': {\n                'latency': (7.15, None, 0.10, 'us')\n            },\n            'ault:amda100': {\n                'latency': (9.65, None, 0.10, 'us')\n            },\n            'ault:amdvega': {\n                'latency': (15.1, None, 0.10, 'us')\n            },\n        },\n        'async': {\n            'dom:gpu': {\n                'latency': (2.2, None, 0.10, 'us')\n            },\n            'daint:gpu': {\n                'latency': (2.2, None, 0.10, 'us')\n            },\n            'ault:intelv100': {\n                'latency': (1.83, None, 0.10, 'us')\n            },\n            'ault:amdv100': {\n                'latency': (1.83, None, 0.10, 'us')\n            },\n            'ault:amda100': {\n                'latency': (2.7, None, 0.10, 'us')\n            },\n            'ault:amdvega': {\n                'latency': (2.64, None, 0.10, 'us')\n            },\n        },\n    })\n    maintainers = ['@teojgo']\n    tags = {'benchmark', 'diagnostic', 'craype', 'health'}\n\n    @run_after('init')\n    def set_valid_prog_environs(self):\n        cs = self.current_system.name\n        if cs in {'dom', 'daint'}:\n            self.valid_prog_environs = ['PrgEnv-cray', 'PrgEnv-pgi',\n                                        'PrgEnv-gnu', 'PrgEnv-nvidia']\n        elif cs in {'arolla', 'tsa'}:\n            self.valid_prog_environs = ['PrgEnv-pgi']\n        elif cs in {'ault'}:\n            self.valid_prog_environs = ['PrgEnv-gnu']\n\n    # Inject external hooks\n    @run_after('setup')\n    def set_gpu_arch(self):\n        hooks.set_gpu_arch(self)\n\n    @run_before('run')\n    def set_num_gpus_per_node(self):\n        hooks.set_num_gpus_per_node(self)\n\n    @run_before('performance')\n    def set_references(self):\n        '''Set the refences based on the ``launch_mode`` parameter.'''\n        self.reference = self.sys_reference[self.launch_mode]\n","repo_name":"eth-cscs/cscs-reframe-tests","sub_path":"checks/microbenchmarks/gpu/kernel_latency/kernel_latency.py","file_name":"kernel_latency.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"24130063306","text":"from django.urls import path\nfrom . import views\n\n# declare app name to use for url routing dynamic\napp_name = \"accounts\"\n\nurlpatterns = [\n    path('register', views.register, name='register'),\n    path(\"login\", views.login_user, name=\"login_user\"),\n    path(\"logout\", views.logout_user, name=\"logout_user\"),\n]","repo_name":"onthir/hollandCode","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15489893642","text":"#!/usr/bin/env python\n\nimport os\nimport sys\nfrom PIL import Image\n\nfontconfig = sys.argv[1]\nbase_dir = os.path.split(fontconfig)[0]\n\nparams = {}\nfor l in open(fontconfig).readlines():\n    (k,v) = l.split(maxsplit=1)\n    params[k]=v.strip()\n    \nimg_path = params['source']\ncharacters = params['characters']\nname = params['name']\ncolsep = int(params.get('separator',0))\nnocase = bool(params.get('nocase',''))\n\n\n# \n#  The uppermost pixel of the font is the MSB. The bottom of the image is the LSB.\n#  All lines are zero padded to the left. For instance, the character:\n\n#    0 1 2 3 4\n#  0 . X X X .\n#  1 X . . . X\n#  2 X . . . X\n#  3 X . . . X\n#  4 X . . . X\n#  5 X . . . X\n#  6 X . . . X\n#  7 X . . . X\n#  8 X . . . X\n#  9 . X X X .\n# \n#  Would be encoded as 01fe, 0201, 0201, 0201, 01fe.\n\nimg = Image.open(img_path).convert(\"L\")\nprint (img.format, img.size, img.mode)\n\nht = img.size[1]\nprint (\"Height is \",ht)\ndef getCol(col):\n    v = 0\n    for i in range(ht):\n        p = img.getpixel((col,i))\n        v = v << 1\n        if p == 0:\n            v = v | 1\n    return v\n\n\nleft = 0\nfont = {}\nfor c in characters:\n    d = [];\n    while True:\n        l = getCol(left)\n        left = left + 1\n        if l == colsep:\n            break\n        d.append(l)\n    font[c] = d\n\nfont[' '] = [0,0]\n\nhpath = os.path.join(base_dir,name+'.h')\ncpath = os.path.join(base_dir,name+'.cpp')\n\nh=open(hpath,'w')\nh.write(f\"\"\"\n#ifndef __{name.upper()}_H__\n#define __{name.upper()}_H__\n\n#include \"font.h\"\n\nextern const Ctab_entry* const {name}_ctab;\nextern const uint8_t* const {name}_cdata;\n\nconst Font {name}_f({name}_ctab, {name}_cdata, {ht});\n\n#endif // __{name.upper()}_H__\n\"\"\");\nh.close()\n\n# Prepare ctab/cdata\nctab = \"\"\ncdata = []\n\nfor c in range(0x20,0x7f):\n    try:\n        if nocase:\n            dat = font[chr(c).upper()]\n        else:\n            dat = font[chr(c)]\n        ctab += f\"  {{{len(dat)},{len(cdata)}}},\\n\"\n        cdata += dat\n    except:\n        ctab += \"  {0,0},\\n\"\n\nif ht <= 8:\n    cdata = \", \".join(map(hex,cdata))\nelse:\n    cdata = \", \".join(map(lambda x : \"0x{:02x}, 0x{:02x}\".format(x>>8,x&0xff),cdata))\n    \n\nc=open(cpath,'w')\nc.write(f\"\"\"\n#include \"{name}.h\"\n\nconst Ctab_entry {name}_ctab_int[] = {{\n{ctab}\n}};\n\nconst uint8_t {name}_cdata_int[] = {{\n{cdata}\n}};\n\nconst Ctab_entry* const {name}_ctab = (const Ctab_entry* const){name}_ctab_int;\nconst uint8_t* const {name}_cdata = (const uint8_t* const){name}_cdata_int;\n\"\"\");\nc.close()\n\n","repo_name":"phooky/droid-head","sub_path":"font/prepare_font.py","file_name":"prepare_font.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14990487075","text":"# Class Photos\n# 🟢 Easy\n#\n# https://www.algoexpert.io/questions/class-photos\n#\n# Tags: Array - Greedy\n\nimport timeit\n\n\n# Sort both arrays, the taller student for each position should be in\n# the same array, either blue or red.\n#\n# Time complexity: O(n*log(n)) - Sorting has the highest time complexity.\n# Space complexity: O(n) - Sorting in python takes up to n/2.\nclass Solution:\n    def classPhotos(self, redShirtHeights, blueShirtHeights):\n        redShirtHeights.sort()\n        blueShirtHeights.sort()\n        if redShirtHeights[0] > blueShirtHeights[0]:\n            taller, shorter = redShirtHeights, blueShirtHeights\n        else:\n            taller, shorter = blueShirtHeights, redShirtHeights\n        return all([taller[i] - shorter[i] > 0 for i in range(len(taller))])\n        # Optionally, we could use a loop.\n        # for i in range(len(taller)):\n        #     if shorter[i] >= taller[i]:\n        #         return False\n        # return True\n\n\ndef test():\n    executors = [Solution]\n    tests = [\n        [[5, 8, 1, 3, 4], [6, 9, 2, 4, 5], True],\n        [[7, 8, 1, 3, 4], [6, 9, 2, 4, 5], False],\n    ]\n    for executor in executors:\n        start = timeit.default_timer()\n        for _ in range(1):\n            for col, t in enumerate(tests):\n                sol = executor()\n                result = sol.classPhotos(t[0], t[1])\n                exp = t[2]\n                assert result == exp, (\n                    f\"\\033[93m» {result} <> {exp}\\033[91m for\"\n                    + f\" test {col} using \\033[1m{executor.__name__}\"\n                )\n        stop = timeit.default_timer()\n        used = str(round(stop - start, 5))\n        cols = \"{0:20}{1:10}{2:10}\"\n        res = cols.format(executor.__name__, used, \"seconds\")\n        print(f\"\\033[92m» {res}\\033[0m\")\n\n\ntest()\n","repo_name":"raul-sauco/coding-challenges","sub_path":"algoexpert/class-photos.py","file_name":"class-photos.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21097432627","text":"def saddle_points(matrix):\n    for length in range(len(matrix) - 1):\n        if len(matrix[length]) != len(matrix[length + 1]):\n            raise ValueError(\"Matrix Error: rows have different lengths!\")\n\n    saddles = set()\n    for x, row in enumerate(matrix):\n        for y, element in enumerate(row):\n            column = [matrix[idx][y] for idx in range(len(matrix))]\n            if element >= max(row) and element <= min(column):\n                saddles.add((x, y))\n    return saddles\n","repo_name":"lagerfeuer/exercism-solutions","sub_path":"python/saddle-points/saddle_points.py","file_name":"saddle_points.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24545821572","text":"from ...core21_interaction.policy.error_follow_up import error_when_processing_array, EncounteredErrorArrayProcessing\nfrom ...core11_config.config import Config, register_config_default, config_dependencies\nfrom ...core30_context.context import Context\n\nfrom enum import Enum\n\n\nclass UnreadableConfigPolicy(Enum):\n    GO_NEXT_RAISE_AT_END = 1\n    VERBOSE_GO_NEXT_RAISE_AT_END = 2\n    VERBOSE_GO_NEXT_ASK_AT_END = 3\n    STOP_FIRST_FAILURE = 4  # this is just exception raising\n    ASK = 5\n    VERBOSE_ASK = 6\n\n\nregister_config_default('.config.unreadable_config', EncounteredErrorArrayProcessing,\n                        EncounteredErrorArrayProcessing.VERBOSE_GO_NEXT_ASK_AT_END)\n\n\ndef exit_or_specify_conf(ctxt: Context):\n    exit_or_ask = ctxt['interactor']['ask_boolean']({'e': True, 'p': False}) \\\n        ('No possible configuration file remains, exit (e) or provide other one (p)?')\n    if exit_or_ask:\n        exit(0)\n    return ctxt['interactor']['ask']('Please specify a valid location for an existing config file')\n\n\n@config_dependencies(('.config.unreadable_config', EncounteredErrorArrayProcessing))\ndef unreadable_config_policy(config: Config,\n                             failed_config: str, remaining_files_to_test: bool, raised_exception: Exception):\n    encountered_error_policy = config['config']['unreadable_config']\n    failed_attempt_message = f\"read config {failed_config}\"\n    no_more_items_message = 'No more configuration file available'\n    continue_message = f\"Unable to read {failed_config}, check next configuration?\"\n    exit_or_specify_manually = exit_or_specify_conf\n    return error_when_processing_array(encountered_error_policy, remaining_files_to_test, raised_exception,\n                                       failed_attempt_message, no_more_items_message, continue_message,\n                                       exit_or_specify_manually)\n","repo_name":"Alkanoor/core","sub_path":"core/core11_config/policy/unreadable_config.py","file_name":"unreadable_config.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"46609806884","text":"#-*- coding:utf-8 -*-\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os,sys\r\nimport matplotlib.pyplot as plt\r\nimport base\r\n\r\nvel_path = r\"D:\\Study\\vehicle\\data\"\r\nvel_path2 = r\"D:\\Study\\vehicle\\data\\period\"\r\ndetail_file = r\"vel_detail.csv\"\r\nvel_detail = pd.read_csv(os.path.join(vel_path, detail_file),low_memory=False)\r\n# print(len(vel_detail))\r\n\r\nemis_list = [\"国1\",\"国2\",\"国3\",\"国3带OBD\",\"国4\",\"国4(京5)\",\"国5\",\"国6\"]\r\npercentile = [0.01,0.05,0.10,0.50,0.90,0.95,0.99]\r\nindex_list = [\"count\",\"mean\",\"std\",\"min\",\"1%\",\"5%\",\"10%\",\"50%\",\"90%\",\"95%\",\"99%\",\"max\"]\r\ncols1 = [\"HC排放值\",\"CO排放值\",\"NOx排放值\",\"累积行驶里程\",\"排量（L）\"]\r\ncols2 = [\"综合工况油耗\",\"市区工况油耗\",\"市郊工况油耗\"]\r\n\r\n# print(len(vel_detail))\r\n##转换类型\r\nfor coli in cols2:\r\n    city_oil = vel_detail.loc[:,coli].tolist()\r\n    for i in range(len(city_oil)):\r\n    # city_oil[i]= map(eval,city_oil[i])\r\n        if \"/\" in str(city_oil[i]):\r\n            city_oil[i]=\" \"\r\n        elif \"-\" in str(city_oil[i]):\r\n            city_oil[i] = \" \"\r\n        elif \"L\" in str(city_oil[i]):\r\n            city_oil[i] = \" \"\r\n        elif \"a\" in str(city_oil[i]):\r\n            city_oil[i] = \" \"\r\n        else:\r\n            city_oil[i] = float(city_oil[i]) ## map(eval,city_oil[i])\r\n# city_oil = [map(eval,x) for x in city_oil ]\r\n    vel_detail.loc[:, coli]= pd.Series(city_oil)\r\n    # city_oil = pd.DataFrame(city_oil,index= None)\r\n    # print(city_oil)\r\n    # print(city_oil.dtypes)\r\nprint(vel_detail[\"综合工况油耗\"])","repo_name":"smallsmallcong/basic-data-analysis","sub_path":"test_convert.py","file_name":"test_convert.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2859372510","text":"from flask import Flask, redirect, url_for, request,Response\nfrom flask import render_template\nfrom threading import Condition\nimport cv2\nimport io\nimport vehicleControl\n\napp = Flask(__name__)\nvc = cv2.VideoCapture(0)\n\ndef gen():\n    \"\"\"Video streaming generator function.\"\"\"\n    while True:\n        rval, frame = vc.read()\n        cv2.imwrite('stream_frame.jpg', frame)\n        yield (b'--frame\\r\\n'\n               b'Content-Type: image/jpeg\\r\\n\\r\\n' + open('stream_frame.jpg', 'rb').read() + b'\\r\\n')\n\n@app.route('/video_feed')\ndef video_feed():\n    \"\"\"Video streaming route. Put this in the src attribute of an img tag.\"\"\"\n    return Response(gen(),\n                    mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\n@app.route('/')\ndef mainpage():\n    vehicleControl.steadyState()\n    return render_template('index.html')\n@app.route('/moveRequest',methods = ['POST'])\ndef success():\n    data = request.form\n    if data is not None:\n        if data.get('data_container') == \"up\":\n            vehicleControl.moveForward()\n            print(\"Go up\")\n        elif data.get('data_container') == \"down\":\n            vehicleControl.moveBackward()\n            print(\"Go down\")\n        elif data.get('data_container') == \"right\":\n            vehicleControl.moveRight()\n            print(\"Go right\")\n        elif data.get('data_container') == \"left\":\n            vehicleControl.moveLeft()\n            print(\"Go left\")\n        else:\n            print(\"Idle\")\n            vehicleControl.steadyState()\n    return 'received'\napp.run(debug = False)\nif __name__ == '__main__':\n   app.run(host='0.0.0.0', port=5500)","repo_name":"sritabh/VehicleControlSystem","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10034640894","text":"from . import views\nfrom django.urls import path\n\napp_name = 'users'\n\nurlpatterns = [\n    path('register/', views.register, name='register'),\n    path('register_handle/', views.register_handle, name='register_handle'),\n    path('login/', views.login, name='login'),\n    path('logout/', views.logout_view, name='logout'),\n    path('user/', views.user, name='user'),\n]","repo_name":"MuYikl1990/e-bookstore","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32848286103","text":"from datetime import datetime\n\nfrom django.db import models\nfrom django.utils import timezone\n\nfrom dashboard.models.utils import BaseModel\n\n\n# Just for permissions\nclass SetupPerms(models.Model):\n\n    class Meta:\n        managed = False\n        default_permissions = ()\n        permissions = [\n            ('manage_setup', 'Can manage system setup'),\n            ('manage_users', 'Can manage system users'),\n            ('view_dashboard', 'Can view dashboard'),\n            ('view_entity_statistics', 'Can view entity statistics'),\n            ('view_system_configuration', 'Can view system configuration'),\n            ('view_substatial_debt_holders', 'Can view substantial debt holders'),\n            (\"manage_tasks\", \"Can manage tasks\"),\n            (\"manage_other_report\", \"Can manage reports others\"),\n            (\"manage_roles\", \"Can manage roles\"),\n\n            # Action center\n            ('view_action_center', 'Can view action center'),\n            ('manage_action_center', 'Can manage action center'),\n\n            # Subject mapping\n            ('add_subject_mapping', 'Can add subject mapping'),\n            ('view_subject_mapping', 'Can view subject mapping'),\n\n            # Promotion\n            ('view_promotion', 'Can view promotion'),\n            ('add_promotion', 'Can add promotion'),\n            ('revert_promotion', 'Can revert promotion'),\n\n            # Class teacher\n            ('change_class_teacher_remark', 'Can change class teacher remark'),\n\n            # House master\n            ('change_house_master_remark', 'Can change house master remark'),\n\n            # Assisant head\n            ('append_signature', 'Can append signature to reports'),\n\n            # Academic records\n            ('change_academic_record', 'Can change academic records'),\n\n            ###### ENTITIES ######\n            # User\n            (\"view_user\", \"Can view user\"),\n            (\"add_user\", \"Can add user\"),\n            (\"change_user\", \"Can change user\"),\n            (\"delete_user\", \"Can delete user\"),\n\n            # Student\n            (\"view_student\", \"Can view student\"),\n            (\"add_student\", \"Can add student\"),\n            (\"change_student\", \"Can change student\"),\n            (\"delete_student\", \"Can delete student\"),\n\n            # Staff\n            (\"view_staff\", \"Can view staff\"),\n            (\"add_staff\", \"Can add staff\"),\n            (\"change_staff\", \"Can change staff\"),\n            (\"delete_staff\", \"Can delete staff\"),\n\n            # Class\n            (\"view_class\", \"Can view class\"),\n            (\"add_class\", \"Can add class\"),\n            (\"change_class\", \"Can change class\"),\n            (\"delete_class\", \"Can delete class\"),\n\n            # Subject\n            (\"view_subject\", \"Can view subject\"),\n            (\"add_subject\", \"Can add subject\"),\n            (\"change_subject\", \"Can change subject\"),\n            (\"delete_subject\", \"Can delete subject\"),\n\n            # Course\n            (\"view_course\", \"Can view course\"),\n            (\"add_course\", \"Can add course\"),\n            (\"change_course\", \"Can change course\"),\n            (\"delete_course\", \"Can delete course\"),\n\n            # Department\n            (\"view_department\", \"Can view department\"),\n            (\"add_department\", \"Can add department\"),\n            (\"change_department\", \"Can change department\"),\n            (\"delete_department\", \"Can delete department\"),\n\n            # House\n            (\"view_house\", \"Can view house\"),\n            (\"add_house\", \"Can add house\"),\n            (\"change_house\", \"Can change house\"),\n            (\"delete_house\", \"Can delete house\"),\n\n            # Attitude\n            (\"view_attitude\", \"Can view attitude\"),\n            (\"add_attitude\", \"Can add attitude\"),\n            (\"change_attitude\", \"Can change attitude\"),\n            (\"delete_attitude\", \"Can delete attitude\"),\n\n            # Conduct\n            (\"view_conduct\", \"Can view conduct\"),\n            (\"add_conduct\", \"Can add conduct\"),\n            (\"change_conduct\", \"Can change conduct\"),\n            (\"delete_conduct\", \"Can delete conduct\"),\n\n            # Interest\n            (\"view_interest\", \"Can view interest\"),\n            (\"add_interest\", \"Can add interest\"),\n            (\"change_interest\", \"Can change interest\"),\n            (\"delete_interest\", \"Can delete interest\"),\n\n            # Track\n            (\"view_track\", \"Can view track\"),\n            (\"add_track\", \"Can add track\"),\n            (\"change_track\", \"Can change track\"),\n            (\"delete_track\", \"Can delete track\"),\n\n            # Remark\n            (\"view_remark\", \"Can view remark\"),\n            (\"add_remark\", \"Can add remark\"),\n            (\"change_remark\", \"Can change remark\"),\n            (\"delete_remark\", \"Can delete remark\"),\n\n            # School session\n            (\"view_schooolsession\", \"Can view schooolsession\"),\n            (\"add_schooolsession\", \"Can add schooolsession\"),\n            (\"change_schooolsession\", \"Can change schooolsession\"),\n            (\"delete_schooolsession\", \"Can delete schooolsession\"),\n\n            # Grading system\n            (\"view_gradingsystem\", \"Can view gradingsystem\"),\n            (\"add_gradingsystem\", \"Can add gradingsystem\"),\n            (\"change_gradingsystem\", \"Can change gradingsystem\"),\n            (\"delete_gradingsystem\", \"Can delete schooolsession\"),\n\n            # ACCOUNTING\n            ('view_accounting', 'Can access accounting menu'),\n            # Invoices\n            (\"view_invoice\", \"Can view invoice\"),\n            (\"add_invoice\", \"Can add invoice\"),\n            (\"change_invoice\", \"Can change invoice\"),\n            (\"apply_invoice\", \"Can apply invoice\"),\n            (\"delete_invoice\", \"Can delete invoice\"),\n\n            # Invoices\n            (\"view_invoiceitem\", \"Can view invoice item\"),\n            (\"add_invoiceitem\", \"Can add invoice item\"),\n            (\"change_invoiceitem\", \"Can change invoice item\"),\n            (\"delete_invoiceitem\", \"Can delete invoice item\"),\n\n            # Staff\n            (\"view_task\", \"Can view task\"),\n            (\"add_task\", \"Can add task\"),\n            (\"change_task\", \"Can change task\"),\n            (\"delete_task\", \"Can delete task\"),\n\n            # Paymnet\n            (\"view_payment\", \"Can view payment item\"),\n            (\"add_payment\", \"Can add payment item\"),\n            (\"change_payment\", \"Can change payment item\"),\n            (\"delete_payment\", \"Can delete payment item\"),\n\n            # Alert\n            ('view_alert', 'Can access alert menu'),\n            ('compose_sms', 'Can compose sms'),\n            ('send_pin_notification', 'Can send pin notification'),\n            ('send_report_notification', 'Can send report notification'),\n            ('send_bill_notification', 'Can send bill notification'),\n            ('resend_notification', 'Can resend notifications'),\n            ('view_notification_history', 'Can view notification history'),\n\n            # Reporting\n            ('view_reporting', 'Can access reporting menu'),\n            ('generate_limited_report', 'Can access reporting menu'),\n\n            # Inventory\n            (\"view_inventory\", \"Can view inventory\"),\n            (\"add_inventory\", \"Can add inventory\"),\n            (\"change_inventory\", \"Can change inventory\"),\n            (\"delete_inventory\", \"Can delete inventory\"),\n\n            # Inventory\n            (\"view_issuance\", \"Can view issuance\"),\n            (\"add_issuance\", \"Can add issuance\"),\n            (\"change_issuance\", \"Can change issuance\"),\n            (\"delete_issuance\", \"Can delete issuance\"),\n\n            # Personal permissions\n            (\"view_personal_academic_record\",\n             \"Can view personal academic record\"),\n            (\"view_personal_invoice\", \"Can view personal invoice\"),\n            (\"view_personal_payment_history\",\n             \"Can view personal payment history\"),\n        ]\n\n\nclass SchoolSession(BaseModel):\n    name = models.CharField(max_length=50)\n    semester = models.IntegerField(verbose_name=\"Semester/Term\")\n    academic_year = models.CharField(max_length=10)\n    tracks = models.ManyToManyField(\"Track\", blank=True)\n    start_date = models.DateField(verbose_name=\"Start Date\",\n                                  null=True,\n                                  blank=True)\n    next_start_date = models.DateField(verbose_name=\"Next Date\",\n                                       null=True,\n                                       blank=True)\n    end_date = models.DateField(verbose_name=\"End Date\", null=True, blank=True)\n\n    class Meta:\n        db_table = \"school_sessions\"\n\n    def __str__(self):\n        return self.name\n\n    def active(self):\n        return self.start_date <= datetime.today().date() <= self.end_date\n\n\nclass Track(BaseModel):\n    name = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.name\n\n\nclass Attitude(BaseModel):\n    text = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.text\n\n\nclass Conduct(BaseModel):\n    text = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.text\n\n\nclass Interest(BaseModel):\n    text = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.text\n\n\nclass Remark(BaseModel):\n    text = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.text\n\n\nclass GradingSystem(BaseModel):\n    min_score = models.IntegerField(unique=True)\n    grade = models.CharField(max_length=2)\n    remark = models.CharField(max_length=50)\n\n    class Meta:\n        ordering = [\"-min_score\"]\n        db_table = \"grading_systems\"\n\n    def __str__(self):\n        return f\"{self.min_score} - {self.grade} - {self.remark}\"\n\n\nclass School(BaseModel):\n    code = models.CharField(max_length=255, blank=True, null=True)\n    name = models.CharField(max_length=255, blank=True, null=True)\n    address = models.CharField(max_length=255, blank=True, null=True)\n    email_address = models.EmailField(max_length=255, blank=True, null=True)\n    phone = models.CharField(max_length=255, blank=True, null=True)\n    logo = models.ImageField(upload_to=\"school_logos\", blank=True, null=True)\n    sms_balance = models.IntegerField(default=0)\n    head_teacher = models.OneToOneField(\"dashboard.Staff\",\n                                        null=True,\n                                        blank=True,\n                                        on_delete=models.SET_NULL)\n    assistant_head_teacher = models.OneToOneField(\n        \"dashboard.Staff\",\n        related_name=\"assistance_school\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL)\n    current_session = models.ForeignKey(SchoolSession,\n                                        on_delete=models.SET_NULL,\n                                        null=True,\n                                        blank=True)\n    current_track = models.ForeignKey(Track,\n                                      on_delete=models.SET_NULL,\n                                      null=True,\n                                      blank=True)\n    sms_sender_id = models.CharField(max_length=255, blank=True, null=True)\n    sms_active = models.BooleanField(default=True)\n    is_active = models.BooleanField(default=True)\n\n    class Meta:\n        db_table = \"schools\"\n\n    def __str__(self):\n        return self.name\n\n    def logo_url(self):\n        if self.logo:\n            return self.logo.url\n        return \"\"\n\n    def get_current_session(self):\n        if self.current_session:\n            return self.current_session\n        return SchoolSession.objects.filter(\n            date_start__lte=timezone.now()).order_by(\"-date_start\").first()\n\n    def get_logo_url(self):\n        if self.logo:\n            return self.logo.url\n        return \"/static/images/logo.png\"\n","repo_name":"dodziraynard/curie","sub_path":"app/setup/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11699,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29127257482","text":"from sklearn.metrics import accuracy_score, r2_score, mean_squared_error\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import OneHotEncoder\n\n# from lime.lime_tabular import LimeTabularExplainer\nfrom csv_logger import CSVLogger\nfrom lime_tabular import LimeTabularExplainer\nfrom models import NN_with_EntityEmbedding, NN\nfrom data_script import get_data\n\nrun_id = 0\nlog_file = '/content/drive/My Drive/myenv/log'+str(run_id)\nlogger = CSVLogger(name='mylogger', log_file=log_file, level='info')\nfiltered_tasks_ids = [3, 20, 24, 41, 45, 49, 3492, 3493, 3494, 3560, 34537, 34539, 146195]\nepochs = 50\nbatch_size = 128\n\nfor j in range(len(filtered_tasks_ids)):\n    print(\"++++++++++++++++++++++++++++++++++++++++++++++++\")\n    print(\"++++++++++ tasks: \"+str(j+1)+\"/\"+str(len(filtered_tasks_ids))+\"+++\")\n    print(\"++++++++++++++++++++++++++++++++++++++++++++++++\")\n    # within different runs, split traning/testing sets with different\n    # random_state.\n    taskid = filtered_tasks_ids[j]\n    X, X_train, X_test, y_train_str, y_test_str, y_train_int, y_test_int, feature_names, class_names, categorical_names, categorical_features = get_data(taskid, random_state=run_id)\n\n    # nn with embedding related\n    nn_with_embedding = NN_with_EntityEmbedding(X_train, y_train_int,\n                                                categorical_features,\n                                                categorical_names,\n                                                class_names,\n                                                epochs=epochs,\n                                                batch_size=batch_size,\n    )\n    nn_with_embedding_loss, nn_with_embedding_score = nn_with_embedding.evaluate(X_test, y_test_int)\n    print(\"nn_with_embedding prediction score: \", str(nn_with_embedding_score))\n    logger.log('nn_with_embedding', taskid, run_id, nn_with_embedding_score)\n\n    # nn related\n    nn = NN(X_train,\n            y_train_int,\n            categorical_features,\n            categorical_names,\n            class_names,\n            epochs=epochs,\n            batch_size=batch_size,)\n    nn_loss, nn_score = nn.evaluate(X_test, y_test_int)\n    print(\"nn prediction score: \", str(nn_score))\n    logger.log('nn', taskid, run_id, nn_score)\n\n    # machine learning model related\n    encoder_X = OneHotEncoder(handle_unknown='ignore', sparse=True)\n    encoder_X.fit(X)\n    encoded_X_train = encoder_X.transform(X_train)\n    encoded_X_test = encoder_X.transform(X_test)\n\n    rf = RandomForestClassifier(n_estimators=100)\n    rf.fit(encoded_X_train, y_train_str)\n    # predict_fn = lambda x: rf.predict_proba(encoder.transform(x))\n    rf_score = accuracy_score(rf.predict(encoded_X_test), y_test_str)\n    print(\"random forest score: \", str(rf_score))\n    logger.log('random_forest', taskid, run_id, rf_score)\n\n# predict_fn = lambda x: nn.predict_proba(x)\n# explainer = LimeTabularExplainer(X_train,\n#                                  class_names=class_names,\n#                                  feature_names=feature_names,\n#                                  categorical_features=categorical_features,\n#                                  categorical_names=categorical_names,\n#                                  kernel_width=3,\n#                                  verbose=False,\n#                                  sample_around_instance=True,\n#                                  # discretize_continuous=False,\n# )\n\n# # Experiment 1\n# # right means the prediction made by blackbox\n# right_prob = []\n# # fit means the prediction made by linear model\n# fit_prob = []\n# print(\"testing set size:  \", X_test.shape[0])\n# for i in range(X_test.shape[0]):\n#     print(str(i+1)+\"th test instance\")\n#     exp = explainer.explain_instance(X_test[i],\n#                                      predict_fn,\n#                                      # num_features=2,\n#                                      num_samples=50,\n#                                      top_labels=1)\n#     right_prob.append(exp.right)\n#     fit_prob.append(exp.local_pred)\n\n# # calculate results\n# score_r2 = r2_score(right_prob, fit_prob)\n# score_mse = mean_squared_error(right_prob, fit_prob)\n# print(\"r2 score: \", score_r2)\n# print(\"mse sore: \", score_mse)\n","repo_name":"Sylver-Chen/explanation","sub_path":"ml_comparision.py","file_name":"ml_comparision.py","file_ext":"py","file_size_in_byte":4233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38247312362","text":"import urllib.parse\nimport urllib.request\nimport json\nimport hashlib\nimport socket\nimport datetime\nimport os\n\n\ndef send_request(url, data=None, post=False, is_json=False):\n    form_data = urllib.parse.urlencode(data) if data else ''\n    if post:\n        response = urllib.request.urlopen(url, form_data.encode()).read().decode()\n    else:\n        response = urllib.request.urlopen(url + '?' + form_data).read().decode()\n\n    return response if not is_json else json.loads(response)\n\n\ndef convert_32to64(steamid):\n    # [U:1:61367470]\n    # STEAM_0:0:30683735\n    if steamid[1] == 'U':  # steam3\n        return 76561197960265728 + int(steamid[5:-1])\n    elif steamid[0] == 'S':  # steam 2\n        return 76561197960265728 + int(steamid[8]) + (int(steamid[10:])*2)\n    return 0\n\n\nclass TF2:\n    def __init__(self, dir=None):\n        self.key = 'E340348F2FAC64C68C8BE48CE1253A62'  # Get from: https://steamcommunity.com/dev/apikey\n        self.language = 'pl'\n        if dir is not None:\n            os.chdir(dir)\n\n    def save_items(self):\n        # This is broken :(\n        # Valve deprecate this endpoint.\n        # Ref: https://www.reddit.com/r/tf2/comments/8glw2m/website_operators_and_update_enthusiasts_that/\n\n        response = send_request('http://api.steampowered.com/IEconItems_440/GetSchema/v0001/', {\n            'key': self.key,\n            'language': self.language,\n            'format': 'json'\n        }, is_json=True)\n\n        if response['result']['status'] != 1:\n            return False\n\n        with open('hats.txt', 'wt', encoding='utf-8') as fp, \\\n                open('hats_info_pl.txt', 'wt', encoding='utf-8') as fp2, \\\n                open('paint_info.txt', 'wt', encoding='utf-8') as fp3:\n\n            fp_items = list()\n            fp2_items = list()\n            fp3_items = list()\n\n            for item in response['result']['items']:\n                if ('item_slot' in item and item['item_slot'] == 'head') or \\\n                        ('craft_material_type' in item and item['craft_material_type'] == 'hat') or \\\n                        ('item_type_name' in item and item['item_type_name'] == 'Nakrycie głowy'):\n\n                    hat_type = (1 << 1)\n                    if 'capabilities' in item and 'paintable' in item['capabilities'] and \\\n                            item['capabilities']['paintable']:\n                        hat_type |= (1 << 0)\n\n                    fp_items.append(str(item['defindex']))\n                    fp2_items.append('{0} ; {1} ; {2}'.format(item['defindex'], item['item_name'], hat_type))\n\n                elif 'tool' in item and 'type' in item['tool'] and item['tool']['type'] == 'paint_can':\n                    has_attrib = dict()\n                    for attrib in item['attributes']:\n                        if attrib['class'] == 'set_item_tint_rgb' or attrib['class'] == 'set_item_tint_rgb_2':\n                            has_attrib[attrib['class']] = attrib['value']\n\n                    if len(has_attrib) == 1:\n                        fp3_items.append('{0} ; {1} ; {1}'.format(item['item_name'], has_attrib['set_item_tint_rgb']))\n                    elif len(has_attrib) == 2:\n                        fp3_items.append('{0} ; {1} ; {2}'.format(item['item_name'], has_attrib['set_item_tint_rgb'],\n                                                                  has_attrib['set_item_tint_rgb_2']))\n\n            fp.write(','.join(fp_items))\n            fp2.write('\\n'.join(fp2_items))\n            fp3.write('\\n'.join(fp3_items))\n\n        effect_name = {\n            \"Attrib_KillStreakEffect2002\": \"Ogniste Rogi\",\n            \"Attrib_KillStreakEffect2003\": \"Mózgowe Wyładowanie\",\n            \"Attrib_KillStreakEffect2004\": \"Tornado\",\n            \"Attrib_KillStreakEffect2005\": \"Płomienie\",\n            \"Attrib_KillStreakEffect2006\": \"Osobliwość\",\n            \"Attrib_KillStreakEffect2007\": \"Spopielacz\",\n            \"Attrib_KillStreakEffect2008\": \"Hipno-Promień\",\n\n            \"Attrib_KillStreakIdleEffect1\": \"Blask Drużyny\",\n            \"Attrib_KillStreakIdleEffect2\": \"Nieludzki Narcyz\",\n            \"Attrib_KillStreakIdleEffect3\": \"Manndarynka\",\n            \"Attrib_KillStreakIdleEffect4\": \"Złośliwa Zieleń\",\n            \"Attrib_KillStreakIdleEffect5\": \"Bolesny Szmaragd\",\n            \"Attrib_KillStreakIdleEffect6\": \"Perfidna Purpura\",\n            \"Attrib_KillStreakIdleEffect7\": \"Hot Rod\",\n        }\n\n        with open('efekt_info.txt', 'wt', encoding='utf-8') as fp:\n            fp_items = list()\n\n            for effect in response['result']['attribute_controlled_attached_particles']:\n                if 'id' not in effect:\n                    continue\n\n                if effect['name'] == 'Attrib_Particle55':\n                    effect['name'] = 'Orbitujące Karty'\n\n                # unusual hats\n                if 2001 > effect['id'] >= 4:\n                    fp_items.append('{0} ; {1}'.format(effect['id'], effect['name']))\n\n                # unusual killstreak\n                elif 3001 > effect['id'] >= 2002:\n                    try:\n                        name = effect_name['Attrib_KillStreakEffect' + str(effect['id'])]\n                        fp_items.append('{0} ; {1}'.format(effect['id'], name))\n                    except:\n                        pass\n\n                # unusual taunt\n                elif 4001 > effect['id'] >= 3001:\n                    fp_items.append('{0} ; {1}'.format(effect['id'], effect['name']))\n\n                # unusual sheen\n                elif 23001 > effect['id'] >= 22002:\n                    try:\n                        name = effect_name['Attrib_KillStreakIdleEffect' + str(effect['id'] - 22001)]\n                        fp_items.append('{0} ; {1}'.format(effect['id'], name))\n                    except:\n                        pass\n\n            fp.write('\\n'.join(fp_items))\n\n        return True\n\n    def get_client(self, steamid):\n        response = send_request('http://api.steampowered.com/IEconItems_440/GetPlayerItems/v0001/', {\n            'key': self.key,\n            'steamid': steamid,\n            'format': 'json'\n        }, is_json=True)\n\n        if response['result']['status'] != 1:\n            return self.api_status(response['result']['status']) + ','\n\n        all_hats = [int(x) for x in open('hats.txt', 'rt', encoding='utf-8').read().split(',')]\n        all_hats_name = {int(x.split(' ; ')[0]): x.split(' ; ')[1] for x in open('hats_info_pl.txt', 'rt', encoding='utf-8').read().split('\\n')}\n\n        ret_items = dict()\n        for item in response['result']['items']:\n            item['defindex'] = int(item['defindex'])\n            if item['defindex'] in all_hats:\n                ret_items[all_hats_name[item['defindex']]] = str(item['defindex'])\n\n        return self.api_status(response['result']['status']) + ',' + ' '.join(ret_items.values())\n\n    @staticmethod\n    def api_status(status):\n        if status == 1:\n            return '1'\n        elif status == 8:\n            return '2'\n        elif status == 15:\n            return '3'\n        elif status == 18:\n            return '4'\n        else:\n            return '5'\n\n\nclass TF2Colors:\n    @staticmethod\n    def generate_hash(ip, port):\n        hash_pub = b'c17e7d02ef1bbd2f87d269143bfeef7983e33124'\n        hash_priv = TF2Colors.generate_priv_hash(ip, port).encode()\n        hash_new = list(hashlib.sha1(hash_pub + hash_priv).hexdigest())\n\n        hash_new[12], hash_new[5] = hash_new[5], hash_new[12]\n        hash_new = ''.join(hash_new)\n        return hash_new\n\n    @staticmethod\n    def generate_priv_hash(ip, port):\n        ret_hash = 'hj13abx{0}:{1}{2}6hasb14as'.format(ip, (int(port) - 25555), datetime.date.today().strftime('%Y-%m-%d'))\n        return hashlib.sha1(ret_hash.encode()).hexdigest()\n\n    @staticmethod\n    def send_socket(ip, port, dane):\n        try:\n            with socket.socket(socket.AF_INET, socket.SOCK_STREAM, socket.IPPROTO_TCP) as s:\n                s.connect((ip, int(port)))\n                s.settimeout(2)\n\n                s.send(b'open:' + TF2Colors.generate_hash(ip, port).encode())\n                s.send(dane.encode())\n                return s.recv(256)\n        except:\n            return None\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/justCTF/2021/pwn/PainterHell/web/utils/steam.py","file_name":"steam.py","file_ext":"py","file_size_in_byte":8158,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"29840801545","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport ftplib as ftp\nimport pandas as pd\nfrom termcolor import colored\nfrom imdb import IMDb\nimport re\nfrom socket import _GLOBAL_DEFAULT_TIMEOUT\nimport os\n\n\nclass Identifiants:\n    \n    def __init__(self, host, user, passwd):\n        self.host = host\n        self.user = user\n        self.passwd = passwd\n\n    def __str__(self):\n        return self.host + \" | \" + self.user\n\n\nclass Connexion(ftp.FTP):\n    \n    def __init__(self, identifiants, acct='', timeout=_GLOBAL_DEFAULT_TIMEOUT, source_address=None):\n        self.source_address = source_address\n        self.timeout = timeout\n        if identifiants.host:\n            self.connect(identifiants.host)\n            if identifiants.user:\n                self.login(identifiants.user, identifiants.passwd, acct)\n    \n    \n    def is_dir(self, filename):\n        try:\n            self.cwd(filename)\n        except:\n            return False\n        self.cwd(\"..\")\n        return True\n\n    \n    def liste_contenu(self, dossier):\n        dir_list = self.nlst(dossier)\n        return pd.DataFrame(dir_list, columns=[\"Dir\"])\n\n    \n    def suppr_fichier(self, fichier):\n        global flag\n        global film_supprime\n        x = fichier.split(\".\")\n        self.sendcmd(\"TYPE i\")\n        try:\n            size = self.size(fichier)\n        except:\n            print(colored(\"Fichier invalide :\", 'red'), fichier)\n        extension = x[-1]\n        if extension in [\"mkv\", \"mp4\"]:\n            titre = recuperer_titre_re(fichier)\n            annee = recuperer_annee_re(fichier)\n            if len(titre) < 35 and is_serie(fichier) == False:\n                rating = info_film(titre, annee)\n                if rating and rating >= 6.5:\n                    couleur = \"green\"\n                else:\n                    couleur = \"red\"\n                    try:\n                        self.delete(fichier)\n                        print(colored(\"Supprimé : {0} (note : {1})\".format(fichier, rating), 'green'))\n                        flag += 1\n                        film_supprime = 1\n                    except:\n                        print(colored(\"Suppression du fichier impossible :\", 'red'), fichier)\n                #print(indent + titre + \": \" + colored(str(rating), couleur))\n        elif extension in [\"jpg\", \"jpeg\"]:\n            try:\n                self.delete(fichier)\n                print(colored(\"Supprimé : {0} (jpeg)\".format(fichier), 'green'))\n                flag += 1\n            except:\n                print(colored(\"Suppression du fichier impossible.\", 'red'), fichier)\n        elif extension == \"srt\" and film_supprime == 1:\n            try:\n                self.delete(fichier)\n                print(colored(\"Supprimé : {0} (film supprimé)\".format(fichier), 'green'))\n                flag += 1\n            except:\n                print(colored(\"Suppression du fichier impossible.\", 'red'), fichier)\n        elif size < 1000000 and extension != \"srt\":\n            try:\n                self.delete(fichier)\n                print(colored(\"Supprimé : {0} (taille : {1})\".format(fichier, size), 'green'))\n                flag += 1\n            except:\n                print(colored(\"Suppression du fichier impossible.\", 'red'), fichier)\n        return flag\n\n    \n    def parcours_liste(self, liste):\n        global flag\n        global indent\n        global film_supprime\n        film_supprime= 0\n        for contenu in liste[\"Dir\"]:\n            #print(indent, contenu)\n            if self.is_dir(contenu):\n                sous_contenu = self.liste_contenu(contenu)\n                if sous_contenu.empty:\n                    try:\n                        self.rmd(contenu)\n                        print(colored(\"Suppression du dossier :\", 'green'), contenu)\n                        flag += 1\n                    except:\n                        print(colored(\"Suppression du dossier impossible :\", 'red'), contenu)\n                    #print(\"\")\n                else:\n                    indent = indent + \"    \"\n                    try:\n                        self.cwd(contenu)\n                    except:\n                        print(colored(\"Parcours du dossier impossible :\", 'red'), contenu)\n                    self.parcours_liste(sous_contenu)\n                    self.cwd(\"..\")\n                    indent = indent[:len(indent)-4]\n            else:\n                flag = self.suppr_fichier(contenu)\n        #print(\"\")\n\n\ndef recuperer_titre(fichier):\n    film = fichier.replace('2018', '2019')\n    film = film.split(\"2019\")\n    titre = film[0].replace(\".\", \" \")\n    return titre\n\n\ndef recuperer_titre_re(fichier):\n    p = re.compile(r\"([\\w\\d \\.]*).([\\d]{4}).([\\dp]{4,5})(.*)\")\n    m = p.search(fichier)\n    if m:\n        return m.group(1).replace(\".\", \" \")\n    else:\n        return \"\"\n\n    \ndef recuperer_annee_re(fichier):\n    p = re.compile(r\"([\\w\\d \\.]*).([\\d]{4}).([\\dp]{4,5})(.*)\")\n    m = p.search(fichier)\n    if m:\n        return m.group(2)\n    else:\n        return \"\"\n\n    \ndef recuperer_annee_imdb(fichier):\n    p = re.compile(r\"([\\d]{4})\")\n    m = p.search(fichier)\n    if m:\n        return m.group(1)\n    else:\n        return \"\"\n\n\ndef is_serie(fichier):\n    p = re.compile('[sS][0-9]+[eE][0-9]+')\n    m = p.search(fichier)\n    if m:\n        return True\n    else:\n        return False\n\n\ndef info_film(titre, annee):\n    s = i.search_movie(titre, results=5)\n    for j in range(len(s)):\n        m = i.get_movie(s[j].movieID)\n        i.update(m, 'release dates')\n        try:\n            if recuperer_annee_imdb(m[\"release dates\"][0]) == annee:\n                try:\n                    rating = m.get('rating')\n                    return rating\n                except:\n                    print(indent, colored(\"Note introuvable :\", 'red'), titre)\n                    return 10\n        except:\n            continue\n\n\ndef main():\n\n    HOST = os.getenv('HOST')\n    USER = os.getenv('USER')\n    PASSWORD = os.getenv('PASSWORD')\n\n    print(\"---- Cleaning started ----\")\n    print(\"Host => \" + HOST)\n    print(\"User => \" + USER)\n\n    identifiants = Identifiants(HOST, USER, PASSWORD)\n    print(colored(identifiants, 'blue'))\n    \n    connect = Connexion(identifiants)\n\n    liste = connect.liste_contenu('/')\n    connect.parcours_liste(liste)\n    \n    if flag == 0:\n        print(\"Terminé : pas de fichier supprimé.\")\n    else:\n        print(\"Terminé : {0} fichier(s) ou dossier(s) supprimé(s).\".format(flag))\n\n\nif __name__ == '__main__':\n    indent = \"\"\n    flag = 0\n    i = IMDb()\n    film_supprime = 0\n    main()","repo_name":"lbouriez/hassio-addons","sub_path":"putio/putio.py","file_name":"putio.py","file_ext":"py","file_size_in_byte":6530,"program_lang":"python","lang":"fr","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"20657604226","text":"#!/usr/bin/env python3\n'''\n\n   Sylvain BERTRAND, 2023\n   (all variables in SI unit)\n   \n   Publish markers for simulation of Pong game\n   \n'''\n\nimport rospy\nfrom visualization_msgs.msg import Marker\n\n\n\n# node init\n# --------------\nrospy.init_node('arena_marker', anonymous=False)\n\n\n# node frequency\n# -----------------\nfrequency = 10.0\nTs = 1.0/frequency\nmarkersPubRate = rospy.Rate(frequency)\n\n\n\nmarker = Marker()\nmarker.header.frame_id = \"world\" #frame_id\nmarker.ns = \"arena\"\nmarker.header.stamp = rospy.Time.now()\nmarker.id = 0\nmarker.type = Marker.CUBE\nmarker.action = 0  #ADD\nmarker.pose.orientation.x = 0.0\nmarker.pose.orientation.y = 0.0\nmarker.pose.orientation.z = 0.0\nmarker.pose.orientation.w = 1.0\nmarker.pose.position.x = 0\nmarker.pose.position.y = 0\nmarker.pose.position.z = 0\nmarker.color.r = 0\nmarker.color.g = 0\nmarker.color.b = 1\nmarker.color.a = 0.1\nmarker.scale.x = 2\nmarker.scale.y = 4\nmarker.scale.z = 0.02\n\npubArenaMarker = rospy.Publisher('/arena_marker', Marker, queue_size=10)\n\n\n\n# main node loop\n# ---------------\n\n# -----------------------------------------------------------------------------\nif __name__ == '__main__':\n# -----------------------------------------------------------------------------\n\twhile not rospy.is_shutdown():\n\t\tmarker.header.stamp = rospy.Time.now()\n\t\t# msg publication\n\t\tpubArenaMarker.publish(marker)\n\t\tmarkersPubRate.sleep()\n# -----------------------------------------------------------------------------\n\n","repo_name":"L2S-lab/dropong","sub_path":"scripts/arena_marker.py","file_name":"arena_marker.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38334705194","text":"#!/usr/local/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\nel_mundo = [\n    (1971, 'Tostão', 'José Omar Pastoriza', 'Luis Artime'),\n    (1972, 'Teófilo Cubillas', 'Pelé', 'Jairzinho'),\n    (1973, 'Pelé', 'Miguel Ángel Brindisi', 'Rivelino'),\n    (1974, 'Elías Figueroa', 'Marinho Chagas', 'Carlos Babington'),\n    (1975, 'Elías Figueroa', 'Norberto Alonso', 'Fernando Morena'),\n    (1976, 'Elías Figueroa', 'Zico', 'Rivelino'),\n    (1977, 'Zico', 'Rivelino', 'Elías Figueroa'), \n    (1978, 'Mario Kempes', 'Ubaldo Fillol', 'Dirceu'),\n    (1979, 'Diego Maradona', 'Julio César Romero', 'Falcão'),\n    (1980, 'Diego Maradona', 'Zico', 'Waldemar Victorino'),\n    (1981, 'Zico', 'Diego Maradona', 'Júnior'),\n    (1982, 'Zico', 'Falcão', 'Diego Maradona'),\n    (1983, 'Sócrates', 'Ubaldo Fillol', 'Éder'),\n    (1984, 'Enzo Francescoli', 'Ubaldo Fillol', 'Ricardo Bochini'),\n    (1985, 'Romerito', 'Enzo Francescoli', 'Claudio Borghi'),\n    ]\n\nel_pais = [\n    (1986, 'Antonio Alzamendi', 'Careca', 'Julio César Romero'),\n    (1987, 'Carlos Valderrama', 'Obdulio Trasante', 'José Perdomo'),\n    (1988, 'Rubén Paz', 'Hugo de León', 'José Pintos Saldanha'),\n    (1989, ('Bebeto', 74), ('Mazinho', 42), ('René Higuita', 34)),\n    (1990, ('Raúl Amarilla', 57), ('Rubén da Silva', 32), ('Leonel Álvarez', 25), ('René Higuita', 25)),\n    (1991, ('Oscar Ruggeri', 44), ('Ramón Díaz', 28), ('Patricio Toledo', 23)),\n    (1992, ('Raí', 55), ('Sergio Goycochea', 24), ('Alberto Acosta', 20), ('Fernando Gamboa', 20)),\n    (1993, ('Carlos Valderrama',  46), ('Marco Etcheverry', 30), ('Cafu', 28), ('Freddy Rincón', 28)),\n    (1994, ('Cafu',  36), ('José Luis Chilavert', 35), ('Gustavo López', 22)),\n    (1995, ('Enzo Francescoli', 34), ('Diego Maradona', 28), ('Edmundo', 24)),\n    (1996, ('José Luis Chilavert', 80), ('Enzo Francescoli', 69), ('Ariel Ortega', 41), ('Carlos Valderrama', 41)),\n    (1997, ('Marcelo Salas', 87), ('Nolberto Solano', 39), ('José Luis Chilavert', 37)),\n    (1998, ('Martín Palermo', 73), ('Carlos Gamarra', 70), ('José Luis Chilavert', 63)),\n    (1999, ('Javier Saviola', 55), ('Francisco Arce', 45), ('Juan Román Riquelme', 42)),\n    (2000, ('Romario', 67), ('Juan Román Riquelme', 64), ('Óscar Córdoba', 53), ('Martín Palermo', 53)),\n    (2001, ('Juan Román Riquelme', 88), ('Óscar Córdoba', 59), ('Romario', 41)),\n    (2002, ('José Cardozo', 39), ('Sergio Órteman', 32), ('Alejandro Lembo', 30)),\n    (2003, ('Carlos Tévez', 73), ('José Cardozo', 39), ('Diego', 33)),\n    (2004, ('Carlos Tévez', 76), ('Javier Mascherano', 56), ('Lucho González', 37), ('Robinho', 37)),\n    (2005, ('Carlos Tévez', 77), ('Diego Lugano', 54), ('Cicinho', 37)),\n    (2006, ('Matías Fernández', 62), ('Rodrigo Palacio', 53), ('Fernando Gago', 50)),\n    (2007, ('Salvador Cabañas', 67), ('Claudio Morel Rodríguez', 61), ('Hugo Ibarra', 57)),\n    (2008, ('Juan Sebastián Verón',  66), ('Juan Román Riquelme', 63), ('Salvador Cabañas', 47)),\n    (2009, ('Juan Sebastián Verón', 109), ('Édison Méndez', 64), ('Humberto Suazo', 64), ('Leandro Desábato', 52)),\n    (2010, ('Andrés D\\'Alessandro',  61), ('Juan Sebastián Verón', 51), ('Neymar', 47)),\n    ]\n","repo_name":"SoccerStatsUS/metadata","sub_path":"data/lists/awards/south_america.py","file_name":"south_america.py","file_ext":"py","file_size_in_byte":3210,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"16607454036","text":"import requests\nimport json \n\nbase_path = 'http://localhost:1026/v2'\n\npayload = {\n    \"open\": {\n    \"type\":\"command\",\n    \"value\": \"\"\n    }\n        \n    }\nheaders = {\n    \"Content-Type\":\"application/json\",\n    \"fiware-service\":\"openiot\",\n    \"fiware-servicepath\":\"/\"\n}\nfor i in range(4):\n    base_request = base_path + f'/entities/Door:00{i+1}/attrs'\n    r = requests.patch(base_request, data=json.dumps(payload), headers=headers)\n    print(r.status_code)\n    print(base_request)\n    print(f\"Door:00{i+1} abierta\")\n\nprint(\"\\n\")\npayload = {\n  \"on\": {\n      \"type\" : \"command\",\n      \"value\" : \"\"\n  }\n}\n\nfor i in range(4):\n    base_request = base_path + f'/entities/Lamp:00{i+1}/attrs'\n    r = requests.patch(base_request, data=json.dumps(payload), headers=headers)\n    print(f\"Lamp:00{i+1} encendida\")","repo_name":"luistdp/P1_XDEI","sub_path":"start_commands.py","file_name":"start_commands.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38905359061","text":"import numpy as np\nfrom parameters import k, norm_factor, recover, a_dist, bld_dist, contagious_risk_day, quarantine, diagnosis, scenario_code, hospital_recover, name\n\n# a script containing all the necessary functions for the epidemiological model\n\n# function 1: update the buildings' status in the network - close/open\n\ndef update_buildings_status(bld_visits_by_agents,agents,build):\n        # get buildings status - open/close == 1/0 and multiply bld_visits_by_agents by it\n        bld_visits = bld_visits_by_agents * build[\n            np.argmax(build[:, 0][None, :] == bld_visits_by_agents[:, :, None], axis=2), 10]\n        # check if agents are in isolation and if yes - all activities but first (home) become zero\n        bld_visits[:, 1:] = bld_visits[:, 1:] * ((agents[:, 13] != 3) & (agents[:, 13] != 4)\n                                                  & (agents[:, 13] != 3.5)).reshape((len(agents), 1))\n        # check if agents are admitted or dead and if yes - all activities become zero\n        bld_visits[:, 0:] = bld_visits[:, 0:] * ((agents[:, 13] != 5) & (agents[:, 13] != 7)).reshape((len(agents), 1))\n        # set None values for slots with no activity\n        bld_visits[bld_visits==0] = np.nan\n        return bld_visits\n    \n# function 2: update counts of days since infection/quarantine for each agent in the network\n\ndef update_counts(agents,tick,bld_visits):\n    # update number of days since infecton for carriers\n    agents[(agents[:, 13] == 2) | \n           (agents[:, 13] == 4) | \n           (agents[:, 13] == 3.5)| \n           (agents[:, 13] == 5), 17] = tick - agents[(agents[:, 13] == 2) | \n                  (agents[:, 13] == 4) | \n                  (agents[:, 13] == 3.5) | \n                  (agents[:, 13] == 5), 16]\n    # update number of days since quarantine for carriers\n    agents[(agents[:, 13] == 3) |\n           (agents[:, 13] == 3.5) |\n           (agents[:, 13] == 4), 20] = tick - agents[(agents[:, 13] == 3)  |\n                    (agents[:, 13] == 3.5) |\n                    (agents[:, 13] == 4), 19] \n    # document all active infected agents\n    infected = np.where((agents[:,13]==2) | (agents[:,13]==4) | (agents[:, 13]==3.5))[0]\n    # document all uninfected agents\n    uninfected = np.where((agents[:,13]<2) | (agents[:,13]==3))[0]\n    # document all admitted agents\n    admitted = np.where(agents[:,13]==5)[0]\n    # document all unadmitted agents\n    unadmitted = np.where(agents[:,13]!=5)[0]\n    # document all  infected and in quarantine agents\n    infected_quar = len(np.where(agents[:,13]==4)[0]) # total diagnosed agents in quarantine    \n    # building visits by infected agents\n    infected_blds = bld_visits[infected] \n    # building visits by uninfected agents\n    uninfected_blds = bld_visits[uninfected]                                                 \n    return agents, admitted, unadmitted, infected_quar, infected_blds, uninfected_blds\n\n\n    \n# function 3: hospitaliztion of agents calculations\n\ndef calculate_hospitalizations(agents,uninfected,tick):\n        # agents infected less than 4 days or more than two weeks\n        no_admission_slot = np.where((agents[:,16] < 4) |\n                                     (agents[:,16] > 14) |\n                                     (agents[:, 13] >= 5) )[0] \n        # array of all agents' admission probability\n        admission_prob = agents[:,23] \n        # in order to prevent changes on original array 'agents', a copy of admissions prob array is required\n        admission_prob = admission_prob.copy() \n        # set probability 0 for all susceptible agents\n        admission_prob[uninfected] = 0 \n        # set probability 0 for all agents infected less than 4 days or more than 2 weeks\n        admission_prob[no_admission_slot] = 0 \n        # caclulate random factor for admission\n        rand_admission = np.random.random(admission_prob.shape)\n        # calculate hospitalized & unhospitalized agents\n        admissions = admission_prob > rand_admission \n        # calculate new admissions\n        new_admissions = np.where(admissions == True)\n        # set agent's status as hospitalized\n        agents[admissions, 13] = 5 \n        # record admission day \n        agents[admissions, 24] = tick \n        return agents, new_admissions\n    \n# function 4: mortality of agents calculations\n    \ndef calculate_mortality(agents,unadmitted):\n        # agents admitted less than 3 days\n        no_death_slot = np.where(agents[:,24] < 3)[0] \n        # array of all agents' death probability\n        death_prob = agents[:,26] \n        # in order to prevent changes on original array 'agents', a copy of death probs array is required\n        death_prob = death_prob.copy() \n        # set death prob for all unadmitted agents as zero\n        death_prob[unadmitted] = 0 \n        # set death prob for all agents admitted 2 days or less as zero\n        death_prob[no_death_slot] = 0 \n        # caclulate random factor for death\n        rand_death = np.random.random(death_prob.shape) \n        # calculate deaths amongst hospitalized agents\n        deaths = death_prob > rand_death \n        # calculate new deaths\n        new_deaths = np.where(deaths == True) \n        # set agent's status as deceased\n        agents[deaths, 13] = 7 \n        return agents, new_deaths\n\n# function 5: infections calculations\n\ndef calculate_infections(agents,tick,infected_blds,uninfected_blds,interaction_prob,infected,uninfected):\n        # compare the two arrays by flattening them and reshaping infected_blds\n        exposure = 1*np.array([np.isin(infected_blds, uninfected_blds[i]).any(axis=1) \n                                for i in range(len(uninfected_blds))])\n        # calculate infection probability for current iteration for each agent in the network based on interactions\n        infection_prob = interaction_prob[uninfected][:, infected] * agents[\n            uninfected, 14].reshape((len(uninfected), 1)) \n        # multiply it by the probability to infect as a dependency of illness day\n        infection_prob *= contagious_risk_day.pdf(agents[infected, 17])\n        # multiply it by agents' exposure in current iteration\n        infection_prob *= exposure \n        # normalize results\n        infection_prob *= norm_factor\n        # calculate random factor\n        rand_cont = np.random.random(infection_prob.shape)\n        # calculate infections wth random factor\n        infections = infection_prob > rand_cont\n        # document new infections\n        new_infected = np.where(infections.any(axis=1) & (agents[uninfected, 13]<2))\n        # document new infected and in quarantine agents\n        new_quarantined_infected = np.where(infections.any(axis=1) & (agents[uninfected, 13]==3))\n        # record new status and infection day count for infected undiagnosed agents\n        agents[uninfected[new_infected], 13] = 2\n        agents[uninfected[new_infected], 16] = tick\n        # record new status and infection day count for infected, quarantined undiagnosed agents\n        agents[uninfected[new_quarantined_infected], 13] = 3.5\n        agents[uninfected[new_quarantined_infected], 16] = tick\n        # this allows tracing infection chains - who infected whom and when\n        agents[uninfected[np.where(infections)[0]], 21] = agents[infected[np.where(infections)[1]], 0]\n        return agents\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    ","repo_name":"yair-grinb/DySTUrbD","sub_path":"epidemiological model.py","file_name":"epidemiological model.py","file_ext":"py","file_size_in_byte":7350,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"34388730703","text":"from json import loads\nfrom markdown import markdown\nfrom urllib3 import PoolManager\nfrom datetime import datetime as dt\nfrom db_connection import db, cursor\n\nLIMIT = 100\nSUBREDDIT = \"kopyamakarna\"\nIGNORE_FLAIRS = [\"META\", \"DUYURU\"]\nAPI_BASE_URL = \"https://api.pushshift.io/reddit/search/submission\"\n\ncursor.execute(\"SELECT created_utc FROM floods ORDER BY id DESC LIMIT 1\")\nafter = cursor.fetchone()[\"created_utc\"] or 1540846800  # Subreddit created_utc\nhttp = PoolManager()\n\nwhile True:\n    response = http.request(\n        \"GET\", API_BASE_URL, fields={\n            \"subreddit\": SUBREDDIT, \"after\": after, \"limit\": LIMIT\n        }\n    )\n\n    if response.status != 200:\n        break\n    submissions = loads(response.data.decode(\"utf-8\"))[\"data\"]\n    if not len(submissions):\n        print(\"Up to date, no new data in API...\")\n        break\n    after = submissions[-1][\"created_utc\"]\n\n    for submission in submissions:\n        if submission.get(\"link_flair_text\", \"\") in IGNORE_FLAIRS:\n            continue\n\n        selftext = markdown(\n            submission.get(\"selftext\", \"\")\n        )\n        if selftext.strip() in \"<p>[removed]</p>\":\n            continue\n\n        sql = \"INSERT INTO floods (title, selftext, full_link, created, created_utc) VALUES (%s, %s, %s, %s, %s)\"\n        val = (\n            submission.get(\"title\"),\n            selftext,\n            submission.get(\"full_link\"),\n            dt.fromtimestamp(submission.get(\"created_utc\")).ctime(),\n            submission.get(\"created_utc\")\n        )\n\n        cursor.execute(sql, val)\n        db.commit()\n        print(\"Data successfully inserted into MySql database...\")\n","repo_name":"floodarchive/floodarchive-rw","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73795663288","text":"from __future__ import absolute_import\n\nfrom sqlparse import engine\nfrom sqlparse import filters\nfrom sqlparse import tokens as T\n\n\nclass ValueFilter(filters.TokenFilter):\n    include = (T.String, T.Number)\n    exclude = (T.String.Symbol,)\n\n    def process(self, stack, stream):\n        for ttype, value in stream:\n            parent = ttype\n            while parent:\n                if parent in self.exclude:\n                    break\n                if parent in self.include:\n                    value = '?'\n                    break\n                if ttype.parent == parent:\n                    parent = None\n                else:\n                    parent = ttype.parent\n            yield ttype, value\n\n\ndef parse(query):\n    stack = engine.FilterStack()\n    stack.preprocess.append(ValueFilter())\n    stack.postprocess.append(filters.SerializerUnicode())\n    return ''.join(stack.run(query))\n","repo_name":"NetEaseGame/Sentry","sub_path":"src/sentry/utils/sqlparser.py","file_name":"sqlparser.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"7759371597","text":"\n# coding: utf-8\n\n# # Applications of Data Science\n# # Lab 3-Py\n# # Preparing Text Data with Python\n# \n# ## Overview\n# \n# This lab introduces you to the basics of text mining and text data preparation. In this lab you will work with a set of 160,000 tweets, which include sentiment labels. \n# \n# Social media sentiment is an important indicator of public opinion.  Determining sentiment can be valuable in a number of applications including brand awareness, product launches, and detecting political trends. \n# \n# Raw text is inherently messy. Machine understanding and analysis is inhibited by the presence of extraneous symbols and words that clutter the text. The exact nature of the required text cleaning depends on the application.  In this case, you will focus on text cleaning to facilitate sentiment classification. The presence of certain words determine the sentiment of the tweet. Words and symbols which are extraneous to this purpose are distractions at best, and a likely source of noise in the analysis. You will follow these steps to prepare the tweet text for analysis: \n# \n# - Remove punctuation symbols and numerals, leaving only alphabetic characters.\n# - Convert all remaining characters to lower case. \n# - Remove stopwords like \"the\", \"and\" and \"this\". Since these words are relatively common, yet communicate no particular sentiment, they can bias analytics. \n# - Stem all remaining words to their root stem. \n# \n# \n# ## What you will need\n# To complete this lab, you will need the following:\n# - A web browser and Internet connection\n# - An Azure ML workspace\n# - The lab files for this lab\n# \n# \n# ## Load and transform the tweet data\n# \n# As a first step, ensure that you have uploaded the **tweets.csv** and **stopwords.csv** files as new datasets in your Azure Machine Learning workspace. Then use the following code to load the tweets data set and set the column names to convenient values.\n\n# In[1]:\n\n\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport pandas as pd\nfrom azureml import Workspace\nws = Workspace()\nds = ws.datasets['tweets.csv']\ndataset = ds.to_dataframe()\ndataset.columns = ['sentiment', 'tweets']\ndataset.head()\n\n\n# Examine the head of the data frame shown above, noticing the content of the two columns.\n# - The Sentiment column contains a sentiment score {0,4} for negative of positive sentiment of the tweet.\n# - The Tweets column contains the actual text of the tweet.\n# \n# \n# ## Normalize the text\n# \n# You will now normalize the tweet text. The code in the cell below performs the following operations:\n# - Remove numbers.\n# - Remove punctuation.\n# - Convert to lower case.\n\n# In[2]:\n\n\nimport string\nsp = string.punctuation\ntweets = dataset['tweets'].tolist()\ntweets = list(map(lambda t: ''.join([\"\" if c.isdigit() else c for c in t]), tweets))\ntweets = list(map(lambda t: ''.join([\"\" if c in sp else c for c in t]), tweets))\ntweets = list(map(str.lower, tweets))\ntweets[:5]\n\n\n# Examine the tweet text. All of the characters are lower case and there are no punctuation characters, or numbers. \n# \n# Next, you will compute the counts and cumulative frequencies of the words in the tweets. The **nltk** package contains two tools to help:\n# \n# The ** regexp_tokenize** functuon tokenizes the text. Tokenization is the process of dividing the text into its component tokens. In this case, the tokens are all words, since you are working with normalized text.\n# \n# The ** FreqDist** function computes the frequency distribution of words in a text corpus. A Pandas data frame is then computed from the word frequency array. \n# \n# Execute the code in the cell below to compute the word frequency and examine the head of the data frame.\n# \n\n# In[3]:\n\n\ndef to_TF(tweets):\n    import pandas as pd\n    import nltk\n    tweets = list(map(lambda t: nltk.regexp_tokenize(t, r'\\S+'), tweets))\n    tweets = [w for l in tweets for w in l]\n\n    ## Compute the frequency distribution of the words as a dictionary\n    fdist = nltk.FreqDist(tweets) \n    ## Convert the dictionary to a dataframe contaning the words and\n    ## counts indexed by the words, and then take the transpose.\n    count_frame = pd.DataFrame(fdist, index =[0]).T\n    count_frame.columns = ['Count']\n    return(count_frame.sort_values('Count', ascending = False))\n \nwf = to_TF(tweets)\nwf.head(n = 20)\n\n\n# Notice that the most frequent words are in the head of this data frame. Of these 20 most frequent words none are likely to convey much information on sentiment.  \n# \n# The code in the cell below, creates a bar plot of word frequency for the most common 60 words. \n\n# In[4]:\n\n\ndef wf_bar(wf):\n    import matplotlib.pyplot as plt\n    ## Barplot of the most fequent words.   \n    fig = plt.figure(figsize=(12, 9))\n    ax = fig.gca()    \n    wf['Count'][:60].plot(kind = 'bar', ax = ax)\n    ax.set_title('Frequency of the most common words')\n    ax.set_ylabel('Frequency of word')\n    ax.set_xlabel('Word')\n    plt.show()\n    return 'Done'\nwf_bar(wf)\n\n\n# Examine this plot and notice the most frequent words. Many of the most frequent words are stop words, such as \"˜the\", \"and\", and \"you\", which are not likely to be helpful in determining sentiment. Also, the frequency of the words drops off fairly quickly to less than 500 out of the 160,000 tweets.\n# \n# Another tool for examining the frequency of words in a corpus of documents is the cumulative distribution frequency (CDF) plot. Execute the code in the cell below to compute and display a bar plot of the cumulative frequencies of the words.\n\n# In[5]:\n\n\ndef plot_cfd(wf):\n    import matplotlib.pyplot as plt\n    ## Compute the relative cumulative frequency of the words in \n    ## descending order of frequency and add the dataframe.   \n    word_count = float(wf['Count'].sum(axis = 0))   \n    wf['Cum'] = wf['Count'].cumsum(axis = 0)\n    wf['Cum'] = wf['Cum'].divide(word_count)\n    \n    ## Barplot the cumulative frequency for the most frequent words.   \n    fig = plt.figure(figsize=(12, 9))\n    ax = fig.gca()    \n    wf['Cum'][:60].plot(kind = 'bar', ax = ax)\n    ax.set_title('Cumulative fraction of total words vs. words')\n    ax.set_ylabel('Cumulative fraction')\n    ax.set_xlabel('Word')\n    plt.show()\n    return 'Done'\n\n\nplot_cfd(wf)\n\n\n# The conclusions one can draw from the second chart are largely the same as the first. The most frequent words are stop words and the frequency of words drops off rather quickly. Also notice, that the frequency of the words becomes uniform fairly quickly. \n# \n# ****\n# \n# You will now examine the head of the resulting word frequency data frame to determine the following:\n# - What is the percentage of all words for these first 20 words?\n# - Of these 20 words, how many are likely to contibute sentiment information?\n# - Are these 20 words different from the words seen for the raw text?\n# \n# To perform this exercise, apply the Pandas **head** method, with **n = 20**, to the wf data frame.\n# \n# \n\n# In[6]:\n\n\nwf.head(n = 20)\n\n\n# ## Remove stop words\n# \n# In the previous section you removed extraneous characters and whitespace from the tweet text. The results show that the most frequent words do not communicate much sentiment information. These frequent words, which are largely extraneous, are known as stop words and should be removed from the text before further analysis. In this exercise you will use custom Python code to remove stop words from the tweet text.\n# \n# As a first step you will load the list of stop words, and examine the first 100 by executing the code in the cell below. \n\n# In[7]:\n\n\nsw = ws.datasets['stopwords.csv']\nstop_words = sw.to_dataframe()\nstop_words = [w for w in stop_words.words if w in stop_words.words.unique() ]\nstop_words[:20]\n\n\n# Execute the code in the cell below to remove the stop words from each tweet using nested list comprehensions. \n\n# In[8]:\n\n\ntemp = [tweet.split() for tweet in tweets] ## Split tweets into tokens\ntweets = [' '.join([word for word in tweet if word not in set(stop_words)]) for tweet in temp]\n\n\n# Execute the code in the cell below to visualize the word frequency.\n\n# In[9]:\n\n\nwf = to_TF(tweets)\nwf_bar(wf)\n\n\n# The distribution of word frequency is not quite different. Note that many of the most frequent words are now likely to convey some sentiment, such as \"good\", \"like\", and \"love\". Evidently, removing stop words has had the desired effect.\n# \n# Next, execute the code in the cell below to display the CDF of the tweets with the stop words removed. \n\n# In[10]:\n\n\nplot_cfd(wf)\n\n\n# As before, this chart shows a number of frequent words which are likely to convey sentiment. However, note that these 60 most frequent words only make up about 17% or the total.\n# \n# ****\n# \n# You will now examine the head of the resulting word frequency data frame to determine the following:\n# \n# - What is the percentage of all words for these first 20 words? \n# - Of these 20 words, how many are likely to contribute sentiment information? \n# - Are these 20 words different from the words seen for the normalized text? \n# \n# To perform this exercise, apply the Pandas **head** method, with **n = 20**, to the wf data frame.\n# ****\n\n# In[11]:\n\n\nwf.head(n = 20)\n\n\n# ## Stem the Words\n# \n# You have cleaned the tweet text and removed stop words. There is one last data preparation step required, stemming the words. Stemming is a process of reducing words to their stems or roots. For example, conjugated verbs such as \"goes\", \"going\", and \"gone\" are stemmed to the word \"go\".  Both Python and R offer a choice of stemmers. Depending on this choice, the results can be more or less suitable for the application. In this case, you will use the popular Porter stemmer. \n# \n# The Porter stemmer used by the **PorterStemmer** function in the **nltk.stem.porter** library. Execute the code in the cell below to load and apply the Porter stemmer to the tweet text and display the first few tweets with stemmed words.\n\n# In[12]:\n\n\nfrom nltk.stem.porter import PorterStemmer\nporter_stemmer = PorterStemmer()\ntemp = [tweet.split() for tweet in tweets] ## Split tweets into tokens\ntemp = map(lambda t: [porter_stemmer.stem(w) for w in t], temp)\ntweets = [' '.join(tweet) for tweet in temp] ## Join the words of the tweet string\ntweets[:10]\n\n\n# Compare the text in the tweets to the text for the normalized text. Notice that there are fewer words in these tweets following the removal of stop words. Also, words like 'happy' have been stemmed to 'happi'.  This text is now ready for analysis!\n# \n# To display the bar plot of the word frequency, execute the code in the cell below.\n\n# In[14]:\n\n\nwf = to_TF(tweets)\nwf_bar(wf)\n\n\n# To display the CDF plot of the word frequency, execute the code in the cell below.\n\n# In[15]:\n\n\nplot_cfd(wf)\n\n\n# Compare these two charts using the stemmed words to the charts created with just stop word filtering and notice the differences. These differences are quite noticeable. For example, some words like \"good\" and \"like\" have moved higher in the order of most frequent words, while some other words like \"going\" have moved down. \n\n# ****\n# \n# You will now examine the head of the resulting word frequency data frame to determine the following:\n# \n# - Have any of the words in the list been stemmed? \n# - Has the stemming changed the frequency of these words?\n# \n# To perform this exercise, apply the Pandas **head** method, with **n = 20**, to the wf data frame.\n# ****\n\n# In[16]:\n\n\nwf.head(n = 20)\n\n","repo_name":"prakobpo/Text-Analytic","sub_path":"TextPrep_Py.py","file_name":"TextPrep_Py.py","file_ext":"py","file_size_in_byte":11412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71359793208","text":"#imports\nfrom flask import Flask, render_template, request\nfrom chatterbot import ChatBot\nfrom chatterbot.trainers import ChatterBotCorpusTrainer\nfrom chatterbot.trainers import ListTrainer\nimport spacy\n\n#python -m spacy link en_core_web_lg en\n\nspacy.load(\"en\")\napp = Flask(__name__)\n\n#create chatbot\nenglishBot = ChatBot(\"mqbot\", storage_adapter=\"chatterbot.storage.SQLStorageAdapter\")\n\n\ntrainer = ListTrainer(englishBot)\n\ntraining_data_quesans = open('training_data/ques_ans.txt').read().splitlines()\ntraining_data_personal = open('training_data/personal_ques.txt').read().splitlines()\n\ntraining_data = training_data_quesans + training_data_personal\n\n#trainer = ListTrainer(chatbot)\ntrainer.train(training_data) \n\ntrainer = ChatterBotCorpusTrainer(englishBot)\ntrainer.train(\"chatterbot.corpus.english\") #train the chatter bot for english\n\n\n#define app routes\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/get\")\n#function for the bot response\ndef get_bot_response():\n    userText = request.args.get('msg')\n    return str(englishBot.get_response(userText))\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"damanpreet26/chatbot","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"2825785105","text":"from multiprocessing import connection\nfrom games import Game\nfrom games import Additional\nfrom games import Genre\nfrom games import Requirements\nfrom games import Review\nfrom games import Author\nfrom games import Price_Info\nfrom games import Game_Tags\nimport psycopg2 as dbapi2\n\nclass Database:\n    def __init__(self, dbfile):\n        self.connection = dbfile\n        \n    def get_game(self, game_id):\n       \n        cursor = self.connection.cursor()\n        query = \"SELECT game_id, response_id, query_name, release_date, required_age, metacritic, about_text FROM Main_Table WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchall()[0]\n        game_ = Game(results[0], results[2])\n        return game_\n\n    def get_games(self):\n        games = []\n        \n        cursor = self.connection.cursor()\n        query = \"SELECT game_id, query_name, release_date, required_age, metacritic, about_text FROM Main_Table LIMIT 50\"\n        cursor.execute(query)\n        for game_id, query_name, release_date, required_age, metacritic, about_text in cursor:\n            games.append(Game(game_id, query_name, release_date, required_age, metacritic, about_text))\n        return games\n\n    def get_additional(self):\n        additional = []\n        cursor = self.connection.cursor()\n        query = \"SELECT game_id, background, headerimage FROM Additional_game_info \"\n        cursor.execute(query)\n        for game_id, background, headerimage in cursor:\n            additional.append(Additional(game_id, background, headerimage))\n        return additional\n\n    def get_adds(self, game_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT gameinfo_Id, game_id, background, headerimage, supporturl, website, recomendationcount,steamspyowners,steamspyplayersestimate FROM Additional_game_info WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchone()\n        try:\n            adds = Additional(results[0], results[1], results[2], results[3], results[4], results[5], results[6], results[7], results[8])    \n        except:\n            adds = Additional(-1, game_id)\n        return adds\n\n    def get_requirements(self, game_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT platform_Id, game_id, response_id, platformwindows, platformlinux, platformmac, pcminreqtext,linuxminreqtext,macminreqtext FROM Platform_Requirements WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchone()\n        try:\n            requirements = Requirements(results[0], results[1], results[2], results[3], results[4], results[5], results[6], results[7], results[8])    \n        except:\n            requirements = Requirements(-1, game_id)\n        return requirements    \n\n    def get_genre(self, game_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT genre_Id, game_id, GenreIsNonGame, GenreIsIndie, GenreIsAction, GenreIsAdventure, GenreIsCasual,GenreIsStrategy,GenreIsRPG,GenreIsSimulation,GenreIsEarlyAccess,GenreIsFreeToPlay,GenreIsSports,GenreIsRacing,GenreIsMassivelyMultiplayer FROM Genre WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchone()\n        try:\n            genre = Genre(results[0], results[1], results[2], results[3], results[4], results[5], results[6], results[7], results[8], results[9], results[10], results[11], results[12], results[13])    \n        except:\n            genre = Genre(-1, game_id)\n        return genre\n\n    def get_reviews(self, game_id):\n        reviews = []\n        \n        cursor = self.connection.cursor()\n        query = \"SELECT game_id, review_id, language, review, timestamp_created, votes_helpful, votes_funny, author_steam_id FROM Reviews WHERE (game_id = %s)LIMIT 20\"\n        cursor.execute(query, (game_id,))\n        for game_id, review_id, language, review, timestamp_created, votes_helpful, votes_funny, author_steam_id in cursor:\n            reviews.append((Review(game_id,review_id, language, review, timestamp_created, votes_helpful, votes_funny, author_steam_id)))\n        return reviews\n    \n    def get_author(self, steam_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT steam_id, num_games_owned, num_reviews, playtime_forever, playtime_last_two_weeks, last_played FROM Author WHERE (steam_id = %s)\"\n        cursor.execute(query, (steam_id,))\n        results = cursor.fetchall()[0]\n        author_ = Author(results[0],results[1], results[2],  results[3], results[4], results[5])\n        return author_\n\n    def get_price_info(self, game_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT price_Id, game_id, isFree, freeveravail, pricecurrency, priceinitial, pricefinal, purchassesavail, subscriptionavail FROM Price_Info WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchone()\n        price_info = Price_Info(results[0],results[1], results[2],  results[3], results[4], results[5], results[6], results[7], results[8])\n        return price_info\n\n    def get_game_tags(self, game_id):\n        cursor = self.connection.cursor()\n        query = \"SELECT tags_Id, game_id, addictive, adventure, co_op, comedy, crime, drama, dystopian_, education, emotional, epic, family_friendly, farming, fighting, flight, football, funny, gambling, hacking, horror, indie, magic, mythology, platformer, rpg, shooter FROM Game_Tags WHERE (game_id = %s)\"\n        cursor.execute(query, (game_id,))\n        results = cursor.fetchone()\n        game_tags = Game_Tags(results[0],results[1], results[2],  results[3], results[4], results[5], results[6], results[7], results[8], results[9], results[10],results[11], results[12],  results[13], results[14], results[15], results[16], results[17], results[18], results[19], results[20],results[21], results[22],  results[23], results[24], results[25], results[26])\n        return game_tags\n\n    def add_info(self, info): #TODO: game_id ?????????? \n        cursor = self.connection.cursor()\n        i=0\n        j=0\n        query = \"SELECT MAX(gameinfo_id) FROM Additional_game_info\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i+=1\n        query = \"SELECT MAX(game_id) FROM Additional_game_info\"\n        cursor.execute(query)\n        j = cursor.fetchone()[0]\n        j = j+100\n        query = \"INSERT INTO Main_Table (game_id,response_id ) VALUES ('{}', '{}')\".format(j,j)\n        cursor.execute(query)\n        query = \"INSERT INTO Additional_game_info (gameinfo_id, game_id, background, headerimage, supporturl, website, recomendationcount,steamspyowners,steamspyplayersestimate) VALUES ('{}','{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(i, j, info.background, info.headerimage, info.supporturl, info.website, info.recomendationcount,info.steamspyowners,info.steamspyplayersestimate)\n        cursor.execute(query)\n        self.connection.commit()\n        return i\n\n    def add_reqirements(self, requirements): #TODO: game_id ?????????? response_id????????????\n        cursor = self.connection.cursor()\n        i=0\n        j=0\n        query = \"SELECT MAX(platform_id) FROM Platform_Requirements\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i+=1\n        query = \"SELECT MAX(game_id) FROM Platform_Requirements\"\n        cursor.execute(query)\n        j = cursor.fetchone()[0]\n        j = j+160\n        query = \"INSERT INTO Main_Table (game_id,response_id ) VALUES ('{}', '{}')\".format(j,j)\n        cursor.execute(query)\n        query = \"INSERT INTO Platform_Requirements (platform_id, game_id, response_id, platformwindows, platformlinux, platformmac, pcminreqtext,linuxminreqtext,macminreqtext) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(i, j, j, requirements.platformwindows, requirements.platformlinux, requirements.platformmac, requirements.pcminreqtext, requirements.linuxminreqtext, requirements.macminreqtext)\n        cursor.execute(query)\n        self.connection.commit()\n        return i\n\n    def add_genre(self, genre): #TODO: game_id ?????????? \n        cursor = self.connection.cursor()\n        i=0\n        j=0\n        query = \"SELECT MAX(genre_id) FROM Genre\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i = i+1\n        print(i)\n        query = \"SELECT MAX(game_id) FROM Genre\"\n        cursor.execute(query)\n        j = cursor.fetchone()[0]\n        j = j+130\n        query = \"INSERT INTO Main_Table (game_id,response_id ) VALUES ('{}', '{}')\".format(j,j)\n        cursor.execute(query)\n        query = \"INSERT INTO Genre (genre_id, game_id, GenreIsNonGame, GenreIsIndie, GenreIsAction, GenreIsAdventure, GenreIsCasual,GenreIsStrategy,GenreIsRPG,GenreIsSimulation,GenreIsEarlyAccess,GenreIsFreeToPlay,GenreIsSports,GenreIsRacing,GenreIsMassivelyMultiplayer) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(i, j, genre.GenreIsNonGame, genre.GenreIsIndie, genre.GenreIsAction, genre.GenreIsAdventure, genre.GenreIsCasual, genre.GenreIsStrategy,genre.GenreIsRPG,genre.GenreIsSimulation,genre.GenreIsEarlyAccess,genre.GenreIsFreeToPlay,genre.GenreIsSports,genre.GenreIsRacing,genre.GenreIsMassivelyMultiplayer)\n        cursor.execute(query)\n        self.connection.commit()\n        return i\n\n    def delete_genre(self, game_id):\n            cursor = self.connection.cursor()\n            query = \"DELETE FROM Genre WHERE game_id = '{}'\".format(game_id)\n            cursor.execute(query)\n            self.connection.commit()\n            #connection.commit()\n\n    def delete_info(self, game_id):\n            cursor = self.connection.cursor()\n            query = \"DELETE FROM Additional_game_info WHERE game_id = '{}'\".format(game_id)\n            cursor.execute(query)\n            self.connection.commit()\n            #connection.commit()\n\n    def delete_requirements(self, game_id):\n            cursor = self.connection.cursor()\n            query = \"DELETE FROM Platform_Requirements WHERE game_id = '{}'\".format(game_id)\n            cursor.execute(query)\n            self.connection.commit()\n            #connection.commit()\n\n\n\n    def update_genre(self, game_id, GenreIsNonGame, GenreIsIndie,GenreIsAction, GenreIsAdventure, GenreIsCasual,GenreIsStrategy,GenreIsRPG,GenreIsSimulation,GenreIsEarlyAccess,GenreIsFreeToPlay,GenreIsSports,GenreIsRacing,GenreIsMassivelyMultiplayer):\n\n            cursor = self.connection.cursor()\n            query = \"UPDATE Genre SET GenreIsNonGame = '{}' , GenreIsIndie = '{}' , GenreIsAction = '{}' , GenreIsAdventure = '{}' , GenreIsCasual = '{}' , GenreIsStrategy = '{}' , GenreIsRPG = '{}' , GenreIsSimulation = '{}' , GenreIsEarlyAccess = '{}' , GenreIsFreeToPlay = '{}' , GenreIsSports = '{}' , GenreIsRacing = '{}'  , GenreIsMassivelyMultiplayer = '{}' WHERE  game_id = '{}'\".format(GenreIsNonGame, GenreIsIndie,GenreIsAction, GenreIsAdventure, GenreIsCasual,GenreIsStrategy,GenreIsRPG,GenreIsSimulation,GenreIsEarlyAccess,GenreIsFreeToPlay,GenreIsSports,GenreIsRacing,GenreIsMassivelyMultiplayer, game_id)\n            cursor.execute(query)\n            self.connection.commit()\n\n    def update_info(self, game_id, background, headerimage, supporturl, website, recomendationcount, steamspyowners, steamspyplayersestimate):\n\n            cursor = self.connection.cursor()\n            query = \"UPDATE Additional_game_info SET background = '{}' , headerimage = '{}' , supporturl = '{}' , website = '{}' , recomendationcount = '{}' , steamspyowners = '{}' , steamspyplayersestimate = '{}' WHERE  game_id = '{}'\".format(background, headerimage, supporturl, website, recomendationcount, steamspyowners, steamspyplayersestimate, game_id)\n            cursor.execute(query)\n            self.connection.commit()\n\n    def update_requirements(self, game_id, platformwindows, platformlinux, platformmac, pcminreqtext, linuxminreqtext, macminreqtext):\n\n            cursor = self.connection.cursor()\n            query = \"UPDATE Platform_Requirements SET platformwindows = {} , platformlinux = {} , platformmac = {} , pcminreqtext = '{}' , linuxminreqtext = '{}' , macminreqtext = '{}' WHERE  game_id = '{}'\".format(platformwindows, platformlinux, platformmac, pcminreqtext, linuxminreqtext, macminreqtext,game_id)\n            cursor.execute(query)\n            self.connection.commit()\n\n\n    def price_info_create(self, price_info):\n        cursor = self.conencion.cursor()\n        i=0\n        while(True):\n            query = \"SELECT price_Id FROM Price_Info WHERE price_Id = '{}'\".format(i)\n            cursor.execute(query)\n            row = cursor.fetchone()\n            if row is None:\n                break\n            else:\n                i+=1\n            query = \"INSERT INTO Price_Info (price_id, game_id, isFree, freeveravail, pricecurrency, priceinitial, pricefinal, purchaseavail, subscriptionavail) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(i, i, price_info.isFree, price_info.freeveravail, price_info.pricecurrency, price_info.priceinitial, price_info.pricefinal, price_info.purchaseavail, price_info.subscriptionavail)\n            cursor.execute(query)\n            return i\n\n    def price_info_delete(self, price_Id):\n        with dbapi2.connect(self.dbfile) as connection:\n            cursor = self.connection.cursor()\n            query = \"DELETE FROM Price_Info WHERE price_Id = '{}'\".format(price_Id)\n            cursor.execute(query)\n            self.connection.commit()\n\n    def price_info_update(self, price_id, game_id, isFree, freeveravail, pricecurrency, priceinitial, pricefinal, purchaseavail, subscriptionavail):\n        with dbapi2.connect(self.dbfile) as connection:\n            cursor = connection.cursor()\n            query = \"UPDATE Price_Info SET isFree={} AND freeveravail={} AND pricecurrency={} AND priceinitial={} AND pricefinal={} AND purchassesavail={} AND subscriptionavail={} WHERE game_id='{}'\".format(isFree, freeveravail, pricecurrency, priceinitial, pricefinal, purchaseavail, subscriptionavail)\n            cursor.execute(query)\n            self.connection.commit()\n\n    def game_tags_create(self, tags):\n        cursor = self.conencion.cursor()\n        i=0\n        while(True):\n            query = \"SELECT tags_Id FROM Game_Tags WHERE tags_Id = '{}'\".format(i)\n            cursor.execute(query)\n            row = cursor.fetchone()\n            if row is None:\n                break\n            else:\n                i+=1\n            query = \"INSERT INTO Game_Tags (tags_Id, game_id, addictive, adventure, co_op, comedy, crime, drama, dystopian_, education, emotional, epic, family_friendly, farming, fighting, flight, football, funny, gambling, hacking, horror, indie, magic, mythology, platformer, rpg, shooter) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(i, i, tags.addictive, tags.adventure, tags.co_op, tags.comedy, tags.crime, tags.drama, tags.dystopian_, tags.education, tags.emotional, tags.epic, tags.family_friendly, tags.arming, tags.fighting, tags.flight, tags.football, tags.funny, tags.gambling, tags.hacking, tags.horror, tags.indie, tags.magic, tags.mythology, tags.platformer, tags.rpg, tags.shooter)\n            cursor.execute(query)\n            return i\n\n    def game_tags_delete(self, tags_Id):\n        with dbapi2.connect(self.dbfile) as connection:\n            cursor = self.connection.cursor()\n            query = \"DELETE FROM Game_Tags WHERE tags_Id = '{} '\".format(tags_Id)\n            cursor.execute(query)\n            connection.commit()\n\n    def game_tags_update(self, tags_Id, game_id, addictive, adventure, co_op, comedy, crime, drama, dystopian_, education, emotional, epic, family_friendly, farming, fighting, flight, football, funny, gambling, hacking, horror, indie, magic, mythology, platformer, rpg, shooter):\n        with dbapi2.connect(self.dbfile) as connection:\n            cursor = connection.cursor()\n            query = \"UPDATE Game_Tags SET addictive={} AND adventure={} AND co_op={} AND comedy={} AND crime={} AND drama={} AND dystopian_={} AND education={} AND emotional={} AND epic={} AND family_friendly={} AND farming={} AND fighting={} AND flight={} AND football={} AND funny={} AND gambling={} AND hacking={} AND horror={} AND indie={} AND magic={} AND mythology={} AND platformer={} AND rpg={} AND shooter={} AND WHERE game_id='{}'\".format(addictive, adventure, co_op, comedy, crime, drama, dystopian_, education, emotional, epic, family_friendly, farming, fighting, flight, football, funny, gambling, hacking, horror, indie, magic, mythology, platformer, rpg, shooter)\n            cursor.execute(query)\n            connection.commit()\n            \n            \n    def add_game(self, game): # INSERT method for Main_Table\n        cursor = self.connection.cursor()\n        i=0\n        j=0\n        query = \"SELECT MAX(game_id) FROM Main_Table\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i = i+1\n        query = \"SELECT MAX(response_id) FROM Main_Table\"\n        cursor.execute(query)\n        j = cursor.fetchone()[0]\n        j = j+1\n        query = \"INSERT INTO Main_table (game_id, response_id,query_name, release_date ,required_age, metacritic, about_text) VALUES ('{}', '{}','{}', '{}','{}', '{}','{}')\".format(i, j, game.query_name, game.release_year, game.required_age, game.metacritic, game.about_text)\n        cursor.execute(query)\n        return i\n    \n    \n    def add_author(self, author): # INSERT method for Author table\n        cursor = self.connection.cursor()\n        i=0\n        query = \"SELECT MAX(steam_id) FROM Author\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i = i+1\n        query = \"INSERT INTO Author (steam_id, num_games_owned,num_reviews,playtime_forever,playtime_last_two_weeks,last_played) VALUES ('{}', '{}','{}', '{}', '{}', '{}')\".format(i,author.num_games_owned, author.num_reviews, author.playtime_forever, author.playtime_last_two_weeks, author.last_played)\n        cursor.execute(query)\n        return i\n    \n    def add_review(self, review): # INSERT method for Reviews table\n        cursor = self.connection.cursor()\n        i=0\n        query = \"SELECT MAX(steam_id) FROM Author\"\n        cursor.execute(query)\n        i = cursor.fetchone()[0]\n        i = i+1\n        # add author id to author table first\n        query = \"INSERT INTO Author (steam_id) VALUES ('{}')\".format(i)\n        cursor.execute(query) \n        # game id should be valid for this operation\n        query = \"INSERT INTO Reviews (review_id, game_id, language, review, timestamp_created, votes_helpful, votes_funny,recommended,author_steam_id) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')\".format(review.review_id, review.game_id, review.language, review.review, review.timestamp_created, 0, 0,0,i)\n        cursor.execute(query)\n        return i","repo_name":"itudb2215/itudb2215","sub_path":"project_flask/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":18933,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"20339126943","text":"\"\"\"\n    url路径配置\n\"\"\"\nBASE_URL = \"http://ihrm-test.itheima.net\"\n# 登录\nLOGIN_URL = BASE_URL + \"/api/sys/login\"\n# 员工添加\nEMPLOYEE_ADD = BASE_URL + \"/api/sys/user\"\n# 员工修改、查询、删除\nEMPLOYEE_ELSE = BASE_URL + \"/api/sys/user/{}\"\n\n\n# token\nTOKEN = None\n\n# 请求头数据 dict\n# \"Authorization\": \"Bearer bcfc9d2e-a575-4240-9569-f5f860619f97\"\nheaders_data = {\n    \"Content-Type\": \"Application/json\"\n}\n","repo_name":"turbid-wine/IHRMTest","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12759648556","text":"#!/usr/bin/env python3\n\n'''101-safely_get_value module'''\n\nfrom typing import Union, Any, Mapping, TypeVar\n\nT = TypeVar('T', bound=Any)\n\n\ndef safely_get_value(dct: Mapping, key: Any,\n                     default: Union[T, None] = None\n                     ) -> Union[Any, T]:\n    '''\n    Gets the value of dictionary key if found otherwise the default value\n    Arg:\n        dict: a mapping object\n        key: a mapping key of any type\n        default: a T or NoneType object\n    Return:\n        The value of at the key dictionary if found, or the default value\n        passed in\n    '''\n    if key in dct:\n        return dct[key]\n    else:\n        return default\n","repo_name":"BLACKMouha/alx-backend-python","sub_path":"0x00-python_variable_annotations/101-safely_get_value.py","file_name":"101-safely_get_value.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14590914359","text":"from typing import Type\n\nimport pytest\n\nfrom jeepito.domain.model import Event, Metadata\nfrom jeepito.service._async.unit_of_work import (\n    AsyncUnitOfWorkTransaction,\n    TransactionError,\n    TransactionStatus,\n)\nfrom tests._async.conftest import AsyncDummyUnitOfWork, DummyModel\n\n\nclass FooCreated(Event):\n    id: str\n    metadata: Metadata = Metadata(name=\"foo_created\", schema_version=1, published=True)\n\n\nclass BarCreated(Event):\n    metadata: Metadata = Metadata(name=\"bar_created\", schema_version=1, published=True)\n\n\nasync def test_collect_new_events(\n    uow: AsyncDummyUnitOfWork, foo_factory: Type[DummyModel]\n):\n    foo = foo_factory(id=\"1\", counter=0)\n    foo.messages.append(FooCreated(id=\"1\"))\n    bar = foo_factory(id=\"1\", counter=0)\n    bar.messages.append(BarCreated())\n    foo2 = foo_factory(id=\"2\", counter=0)\n    foo2.messages.append(FooCreated(id=\"2\"))\n\n    async with uow as tuow:\n        await tuow.foos.add(foo)\n        await tuow.bars.add(bar)\n        await tuow.foos.add(foo2)\n        await tuow.commit()\n\n    iter = uow.collect_new_events()\n    assert next(iter) == FooCreated(id=\"1\")\n    assert next(iter) == FooCreated(id=\"2\")\n    assert next(iter) == BarCreated()\n    with pytest.raises(StopIteration):\n        next(iter)\n\n\nasync def test_transaction_rollback_on_error(uow: AsyncDummyUnitOfWork):\n    tuow = None\n    try:\n        async with uow as tuow:\n            raise ValueError(\"Boom\")\n    except Exception:\n        ...\n    assert tuow is not None\n    assert tuow.status == TransactionStatus.rolledback\n\n\nasync def test_transaction_rollback_explicit_commit(uow: AsyncDummyUnitOfWork):\n    with pytest.raises(TransactionError) as ctx:\n        async with uow as tuow:\n            tuow.foos\n\n    assert str(ctx.value).endswith(\n        \"Transaction must be explicitly close. Missing commit/rollback call.\"\n    )\n\n\nasync def test_transaction_invalid_state(uow: AsyncDummyUnitOfWork):\n    with pytest.raises(TransactionError) as ctx:\n        async with uow as tuow:\n            tuow.status = TransactionStatus.closed\n\n    assert str(ctx.value).endswith(\"Transaction is closed.\")\n\n\nasync def test_transaction_invalid_usage(uow: AsyncDummyUnitOfWork):\n    with pytest.raises(TransactionError) as ctx:\n        transaction = AsyncUnitOfWorkTransaction(uow)\n        transaction.status = TransactionStatus.committed\n        async with transaction:\n            ...\n\n    assert str(ctx.value).endswith(\"Invalid transaction status.\")\n\n\nasync def test_transaction_commit_after_rollback(uow: AsyncDummyUnitOfWork):\n    with pytest.raises(TransactionError) as ctx:\n        async with uow as tuow:\n            await tuow.rollback()\n            await tuow.commit()\n\n    assert str(ctx.value).endswith(\"Transaction already closed (rolledback).\")\n\n\nasync def test_transaction_commit_twice(uow: AsyncDummyUnitOfWork):\n    with pytest.raises(TransactionError) as ctx:\n        async with uow as tuow:\n            await tuow.commit()\n            await tuow.commit()\n\n    assert str(ctx.value).endswith(\"Transaction already closed (committed).\")\n","repo_name":"mardiros/jeepito","sub_path":"tests/_async/test_unit_of_work.py","file_name":"test_unit_of_work.py","file_ext":"py","file_size_in_byte":3060,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"17091237196","text":"'''\r\nDemonstrating Levenshtein edit distance(dp method)\r\non given word and finding closest word found in dictionary of words\r\nstored in an array\r\n'''\r\nimport datetime\r\nimport sys\r\n# sys.stdin = open('sample.txt', 'r')\r\n# sys.stdout = open('output.txt', 'w')\r\nwords=[]\r\n\r\ndef words_append():\r\n    sys.stdin = open('sample.txt', 'r')\r\n    for t in range(int(input())):\r\n        words.append(input())\r\n    print(\"sm2\")\r\n\r\n\r\nclass Levenshtein:\r\n    def __init__(self):\r\n        self.dp=[]\r\n    def initialise_dp(self,n,m):\r\n        temp=[]\r\n        for i in range(n + 1):\r\n            x = []\r\n            for j in range(m + 1):\r\n                x.append(None)\r\n            temp.append(x)\r\n        self.dp=temp\r\n    def levenshtein_dp(self,a, b, i, j):\r\n        if b == \"\":\r\n            self.dp[i][0] = len(a)\r\n            return len(a)\r\n        if a == \"\":\r\n            self.dp[0][j] = len(b)\r\n            return len(b)\r\n        if self.dp[i][j] != None:\r\n            return self.dp[i][j]\r\n        c1 = self.levenshtein_dp(a[:i - 1], b[:j - 1], i - 1, j - 1) + (a[i - 1] != b[j - 1])  # replace\r\n        c2 = self.levenshtein_dp(a[:i - 1], b, i - 1, j) + 1  # remove\r\n        c3 = self.levenshtein_dp(a, b[:j - 1], i, j - 1) + 1  # insert\r\n        self.dp[i][j] = min(c1, c2, c3)\r\n        return self.dp[i][j]\r\n    def solve_dp(self,b):\r\n        start_time = datetime.datetime.now()\r\n        nearest=[]\r\n        minimum=float('inf')\r\n        for a in words:\r\n            self.initialise_dp(len(a),len(b))\r\n            x=self.levenshtein_dp(a,b,len(a),len(b))\r\n            if x<minimum:\r\n                nearest=[a]\r\n                minimum=x\r\n            elif x==minimum:\r\n                nearest.append(a)\r\n        end_time = datetime.datetime.now()\r\n        print(*nearest)\r\n        print(end_time - start_time)\r\n        gap = end_time - start_time\r\n        return nearest, gap.seconds + gap.microseconds / 1000000\r\n\r\n\r\nl=Levenshtein()\r\n# l.solve_dp(\"clock\")","repo_name":"djdheeraj5701/seminar-spellcheck","sub_path":"seminar_code_2.py","file_name":"seminar_code_2.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35160747292","text":"# -*- coding: utf-8 -*-\n\"\"\"\nauthor: zengbin93\nemail: zeng_bin8888@163.com\ncreate_dt: 2022/2/16 20:31\ndescribe: czsc.utils 单元测试\n\"\"\"\nimport pytest\nimport pandas as pd\nfrom czsc import utils\n\n\ndef test_x_round():\n    assert utils.x_round(100, 3) == 100\n    assert utils.x_round(1.000342, 3) == 1.0\n    assert utils.x_round(1.000342, 4) == 1.0003\n    assert utils.x_round(1.000342, 5) == 1.00034\n\n\ndef test_subtract_fee():\n    from czsc.utils.stats import subtract_fee\n\n    # 构造测试数据\n    data = {\n        'dt': pd.date_range('2022-01-01', periods=20, freq='D'),\n        'pos': [0, 1, 1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 0, 0, -1, -1, 0, 1, 1],\n        'price': [10, 11, 12, 13, 14, 10, 11, 12, 13, 14, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n    }\n    df = pd.DataFrame(data)\n\n    # 执行函数\n    df = subtract_fee(df, fee=100)\n\n    # 验证结果\n    assert int(df['edge_pre_fee'].sum()) == 2748\n    assert int(df['edge_post_fee'].sum()) == 1848\n\n    # 构造测试数据\n    data = {\n        'dt': pd.date_range('2022-01-01', periods=5, freq='D'),\n        'pos': [0, 1, 1, -1, 0],\n    }\n    df = pd.DataFrame(data)\n\n    # 执行函数并捕获异常\n    with pytest.raises(AssertionError):\n        subtract_fee(df, fee=1)\n\n    # 构造测试数据\n    data = {\n        'dt': pd.date_range('2022-01-01', periods=5, freq='D'),\n        'pos': [0, 1, 2, -1, 0],\n        'price': [10, 11, 12, 13, 14]\n    }\n    df = pd.DataFrame(data)\n\n    # 执行函数并捕获异常\n    with pytest.raises(AssertionError):\n        subtract_fee(df, fee=1)","repo_name":"leonlazuli/czsc","sub_path":"test/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"6974679814","text":"# -*- coding:utf-8 -*-\n\"\"\"\nauthor: zhou zhen\ndate:   2020/10/28\n\n\"\"\"\n\nimport requests  # 导入网络请求库\nimport os  # 导入文件操作库\nfrom bs4 import BeautifulSoup  # 导入html处理库\nimport re  # 正则库\nimport pdfkit  # 用于html转pdf\nimport PyPDF4\n\npic_path = 'https://learnku.com/docs/pymotw'  # 爬网页地址\n\nres = requests.get(pic_path)\nsoup = BeautifulSoup(res.text, 'html.parser')\nall_link = soup.findAll('a')\nwith open('out.txt', 'w') as f:  # 因为html编码原因,正则无法匹配,先将html转成txt匹配\n    for href in all_link:\n        f.write(str(href))\nwith open('out.txt', 'r') as f:\n    lines = f.readlines()\n    out_list = []\n    for line in lines:\n        find_one = re.compile('<a\\sclass=\"\"\\shref=\"(.*)\">')  # 获取要爬的网页地址\n        out = re.findall(find_one, line)\n        if out != []:\n            out_list.append(out)\nos.remove('out.txt')\noptions = {\n    'page-size': 'A4',\n    'margin-top': '0.1in',\n    'margin-right': '0.1in',\n    'margin-bottom': '0.1in',\n    'margin-left': '0.1in',\n    'encoding': \"utf-8\",\n    'custom-header': [\n        ('Accept-Encoding', 'gzip')\n    ],\n    'cookie': [\n        ('cookie-name1', 'cookie-value1'),\n        ('cookie-name2', 'cookie-value2'),\n    ],\n    'outline-depth': 30,\n    \"dpi\": 196\n}\n\nhtml_template = \"\"\" \n<!DOCTYPE html> \n<html lang=\"en\"> \n<head> \n    <meta charset=\"UTF-8\"> \n</head> \n<body> \n{content} \n</body> \n</html> \n\n\"\"\"\n\n\ndef create_path():\n    if not os.path.exists('pdf'):\n        os.makedirs('pdf')  # 创建pdf文件夹\n\ncreate_path()\ni = 0  # 生成文件名\nfor out in out_list:\n    res = requests.get(out[0])\n    new_soup = BeautifulSoup(res.text, 'html.parser')\n    find_out = new_soup.find_all('div', 'extra-padding')  # 获取要转成pdf的内容\n    find_out = str(find_out[0])\n    html = find_out\n    html = html_template.format(content=find_out)\n    html = html.encode(\"utf-8\")\n    with open('out.html', 'wb') as f:\n        f.write(html)\n    pdfkit.from_file('out.html', 'pdf/' + 'out' + str(i) + '.pdf',options=options)  # html转pdf\n    i += 1\n    os.remove('out.html')\n\ndef sort_key(name):#按名称排序文件合并\n    return int(re.findall(r'(\\d+)',name)[0])\nfiles=os.listdir('pdf')\nfiles.sort(key=lambda x:sort_key(x))\npdf_all=PyPDF4.PdfFileMerger()\nfor file in files:#合并文件\n    pdf_all.append('pdf/'+file)\npdf_all.write('pdf_all.pdf')\npdf_all.close()\n","repo_name":"ZZXV/get_pdf_and_merge","sub_path":"get_pdf.py","file_name":"get_pdf.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1846482534","text":"\"\"\"O(nlogn)时间复杂度,O(1)空间复杂度\"\"\"\n# 思路：快慢指针+归并排序\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\n        if not l1: return l2  # 终止条件，直到两个链表都空\n        if not l2: return l1\n        if l1.val <= l2.val:  # 递归调用\n            l1.next = self.mergeTwoLists(l1.next, l2)\n            return l1\n        else:\n            l2.next = self.mergeTwoLists(l1, l2.next)\n            return l2\n\n    def sortList(self, head: ListNode) -> ListNode:\n        if not head or not head.next:\n            return head\n\n        slow, fast = head, head\n        while slow != None and fast != None and fast.next != None:\n            pre = slow\n            slow = slow.next\n            fast = fast.next.next\n\n        head2 = slow\n        pre.next = None\n\n        return self.mergeTwoLists(self.sortList(head), self.sortList(head2))","repo_name":"zhengsizuo/leetcode-zhs","sub_path":"排序/148-排序链表.py","file_name":"148-排序链表.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23104156425","text":"from django.shortcuts import render\nfrom .models import *\nfrom django.core.mail import send_mail, BadHeaderError\nfrom .decorators import check_recaptcha\n\n\n# Create your views here.\n\ndef send_contact(request):\n    fname = request.POST.get('first_name', '')\n    lname = request.POST.get('last_name', '')\n    subject = 'Message from website'\n    message = request.POST.get('message', '')\n    from_email = request.POST.get('email', '')\n    whatsapp = request.POST.get('whatsapp', '')\n    messages = 'Name and Surname: {} {} \\nWhatsApp: {}\\nFrom: {}\\nMessage: \\n{}\\n\\n\\n\\nSent From dark-render.com'.format(\n        fname, lname, whatsapp, from_email, message)\n    send_mail(subject, messages, 'noreply@dark-render.com', ['info@dark-render.com'], fail_silently=False)\n\n\ndef index(request):\n    partners = Partner.objects.all()\n    slider = Slider.objects.all()\n    projects = Project.objects.all().order_by('-id')[:6]\n\n    ctx = {'slider': slider,\n           'partners': partners,\n           'projects' : projects}\n\n    return render(request, 'index.html', ctx)\n\n\ndef about(request):\n    partners = Partner.objects.all()\n\n    ctx = {'partners': partners}\n    return render(request, 'about.html', ctx)\n\n\n@check_recaptcha\ndef contact(request):\n    ctx = {'success': False,\n           'fail': False}\n    if request.method == 'POST':\n        if request.recaptcha_is_valid:\n            send_contact(request)\n            ctx['success'] = True\n        else:\n            ctx['fail'] = True\n    return render(request, 'contact.html', ctx)\n\n\ndef projects(request):\n    projects = Project.objects.all().order_by('-id')\n\n    filter = None\n    try:\n        filter = request.GET['filter']\n    except:\n        ...\n\n    if not filter:\n        filter = '*'\n\n    ctx = {'projects': projects,\n           'filter': filter}\n\n    return render(request, 'projects.html', ctx)\n\n\ndef vr_tours(request):\n    vrs = VR.objects.all().order_by('-id')\n\n    ctx = {'vr': vrs}\n\n    return render(request, 'vr_tours.html', ctx)\n\ndef project_page(request, id):\n    project = Project.objects.get(pk=id)\n    gallery = Photo.objects.filter(project=project)\n\n    ctx = {\n        'project': project,\n        'gallery': gallery,\n    }\n\n    return render(request, 'project_page.html', ctx)\n","repo_name":"nss-agency/Dark_Render","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17582907213","text":"import utils as U\r\nimport pandas as pd\r\nfrom dataclasses import dataclass\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\r\nfrom sklearn.model_selection import train_test_split, GridSearchCV\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\n@dataclass\r\nclass ModelLogger:\r\n    name: str\r\n    test_accuracy: float = .0\r\n    test_recall: float = .0\r\n    test_precision: float = .0\r\n    test_f1: float = .0\r\n\r\n    def update_accuracy(self, y_pred, y_test):\r\n        self.test_accuracy = accuracy_score(y_test, y_pred)\r\n\r\n    def update_recall(self, y_pred, y_test):\r\n        self.test_recall = recall_score(y_test, y_pred, average=\"macro\")\r\n\r\n    def update_precision(self, y_pred, y_test):\r\n        self.test_precision = precision_score(y_test, y_pred, average=\"macro\")\r\n\r\n    def update_f1(self, y_pred, y_test):\r\n        self.test_f1 = f1_score(y_test, y_pred, average=\"macro\")\r\n\r\n    def update_metrics(self, y_pred, y_test):\r\n        self.update_f1(y_pred, y_test)\r\n        self.update_precision(y_pred, y_test)\r\n        self.update_accuracy(y_pred, y_test)\r\n        self.update_recall(y_pred, y_test)\r\n\r\n\r\ndef train_and_predict() -> tuple[pd.DataFrame, ModelLogger]:\r\n    features = pd.read_csv(\"pipeline/features.csv\")\r\n    labels = pd.read_csv(\"pipeline/labels.csv\")\r\n\r\n    le = LabelEncoder()\r\n    labels = le.fit_transform(labels)\r\n\r\n    scaler = StandardScaler()\r\n\r\n    _, features = U.split_features(features)\r\n    features = scaler.fit_transform(features)\r\n\r\n    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.02, random_state=0, shuffle=False)\r\n\r\n    print(f\"shape of training data {X_train.shape}\")\r\n    print(f\"shape of testing data {X_test.shape}\")\r\n\r\n    lr = LogisticRegression()\r\n    param_grid = [{ \"penalty\": [\"L1\", \"L2\", \"elasticnet\", \"none\"],\r\n                    \"C\": [100, 10, 1.0, 0.1, 0.01],\r\n                    \"solver\": [\"lbfgs\", \"newton-cg\", \"liblinear\", \"sag\", \"saga\"],\r\n                    \"max_iter\": [100, 1000, 2500, 5000]}]\r\n\r\n    grid_search = GridSearchCV(lr, param_grid=param_grid,\r\n                                   cv=5, n_jobs=-1, verbose=True)\r\n\r\n    grid_search.fit(X_train, y_train)\r\n    tuned_model = grid_search.best_estimator_\r\n\r\n    tuned_model.fit(X_train, y_train)\r\n    y_pred = tuned_model.predict(X_test)\r\n\r\n    model_info = ModelLogger(lr.__class__.__name__)\r\n    model_info.update_metrics(y_pred, y_test)\r\n\r\n    df_pred = pd.read_csv(\"pipeline/to_predict.csv\")\r\n    _, df_pred = U.split_features(df_pred)\r\n    df_pred = scaler.transform(df_pred)\r\n    home, away, draw = [], [], []\r\n    pred = tuned_model.predict_proba(df_pred)\r\n    for prob in pred:\r\n        home.append(f'{int(round(prob[2]*100, 0))}')\r\n        draw.append(f'{int(round(prob[1]*100, 0))}')\r\n        away.append(f'{int(round(prob[0]*100, 0))}')\r\n    df_fixtures = pd.read_csv(\"fixtures.csv\", encoding=\"cp1252\")\r\n    model_predictions = df_fixtures[df_fixtures[\"Div\"] == \"E0\"][[\"Date\", \"Time\", \"HomeTeam\", \"AwayTeam\"]]\r\n    model_predictions[\"Home %\"] = home\r\n    model_predictions[\"Draw %\"] = draw\r\n    model_predictions[\"Away %\"] = away\r\n\r\n    return model_predictions, model_info\r\n\r\n","repo_name":"Chinedu-E/premier-league-main","sub_path":"pipeline/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27462688170","text":"import os\n\n# Funcion para leer el archivo de texto con la matriz de adyacencia\n# Entrada: La ruta del archivo de texto\n# Salida: Retorna la matriz de adyacencia, la lista de adyacencia y los nombres de los nodos\ndef read_graph_from_file(file_path):\n    file_path = os.path.dirname(__file__) + '/' + file_path\n\n    # Una cadena con los nombres de los nodos alfabético\n    node_names = 'abcdefghijklmnopqrstuvwxyz'\n\n    with open(file_path, 'r') as file:\n        lines = file.readlines()\n    \n    # Generar el adjacency matrix\n    adjacency_matrix = []\n    for line in lines:\n        adjacency_matrix.append([int(x) for x in line.split()])\n    \n    # Generar el adjacency list\n    adjacency_list = []\n    for line in lines:\n        node_list = []\n        for i, node in enumerate(line.split()):\n            if int(node) > 0:\n                node_list.append((i, int(node)))\n        adjacency_list.append(node_list)\n\n    return adjacency_matrix, adjacency_list, node_names[0:len(adjacency_matrix)]\n\n# Funcion para imprimir el camino mas corto desde un nodo inicial hasta un nodo final\n# Entrada: El arreglo de distancias, el arreglo de desde donde se llega a cada nodo, el arreglo de nombres de nodos y el nodo final\n# Salida: Imprime el camino mas corto desde el nodo inicial hasta el nodo final\ndef print_path(distances, fromNode, node_names, end):\n    curr_node = end\n    total_distance = 0\n    path = []\n\n    # Recorrer el arreglo de nodos desde donde se llega a cada nodo hasta llegar al nodo inicial\n    while curr_node is not None:\n        total_distance += distances[curr_node] # Incrementar la distancia total\n        path.append((curr_node, distances[curr_node]))\n        curr_node = fromNode[curr_node]\n\n    path.reverse()\n\n    # Generamos la cadena de texto con el camino de manera mas legible\n    path_string = ''\n    for i in path:\n        path_string += f'{node_names[i[0]]}({i[1]})'\n\n        if i[0] == end:\n            path_string += f': {total_distance}'\n        else:\n            path_string += ', '\n\n    print(path_string)\n\n# Funcion para encontrar el nodo con menor distancia que no haya sido procesado\ndef find_min_processed_node(distances, processed):\n    min = float('inf')\n    min_index = None\n    # Encontrar el indice del nodo con menor distancia pero que no haya sido procesado\n    for i, v in enumerate(distances):\n        if v < min and not processed[i]:\n            min = v\n            min_index = i\n\n    return min_index\n\n# Implementacion del algoritmo de Dijkstra\n# Entrada: La lista de adyacencia, el nodo inicial y el nodo final\n# Salida: Retorna las distancias desde el nodo inicial hasta cada nodo y el arreglo de desde donde se llega a cada nodo\n# Complejidad: O(V^2)\ndef dijkstra(adjacency_list, start, end):\n    # Arreglo de distancias desde un nodo al otro\n    distances = [float('inf') for i in range(len(adjacency_list))]\n    distances[start] = 0\n\n    # Arreglos de nodos procesados y desde donde se llega a cada nodo\n    visited = [False for i in range(len(adjacency_list))]\n    fromSource = [None for i in range(len(adjacency_list))]\n    \n    for _ in range(len(adjacency_list)):\n        # Obtenemos un nodo no procesado con la menor distancia\n        u = find_min_processed_node(distances, visited)\n        visited[u] = True\n\n        # Recorrer cada vecino de el nodo u\n        for neighbor, weight in adjacency_list[u]:\n            new_distance = distances[u] + weight\n            if new_distance < distances[neighbor]:\n                distances[neighbor] = new_distance\n                fromSource[neighbor] = u\n    \n    # Retornar las distancias y el arreglo desde donde se llega a cada nodo\n    return distances, fromSource\n\n# Implementacion del algoritmo de Prim\n# Entrada: La lista de adyacencia\n# Salida: Retorna las distancias desde el nodo inicial hasta cada nodo y el arreglo de desde donde se llega a cada nodo para el arbol\n# Complejidad: O(V^2)\ndef prim(adjacency_list):\n    distances = [float('inf') for i in range(len(adjacency_list))]\n    distances[0] = 0\n\n    visited = [False for i in range(len(adjacency_list))]\n    fromSource = [None for i in range(len(adjacency_list))]\n\n    # Aqui en vez de recorrer todos los nodos, recorremos todos los nodos que no han sido procesados\n    while False in visited:\n        # Aqui de igual manera obtenemos el nodo con la menor distancia\n        u = find_min_processed_node(distances, visited)\n        visited[u] = True\n\n        for neighbor, weight in adjacency_list[u]:\n            if not visited[neighbor] and weight < distances[neighbor]:\n                distances[neighbor] = weight\n                fromSource[neighbor] = u\n    \n    return distances, fromSource\n\n# Implementacion del algoritmo de Warshall\n# Entrada: La matriz de adyacencia\n# Salida: Retorna la matriz de adyacencia con los caminos entre cada nodo\n# Complejidad: O(V^3)\ndef warshall(adjacency_matrix):\n    for k in range(len(adjacency_matrix)):\n        for i in range(len(adjacency_matrix)):\n            for j in range(len(adjacency_matrix)):\n                adjacency_matrix[i][j] = adjacency_matrix[i][j] or (adjacency_matrix[i][k] and adjacency_matrix[k][j])\n\n    return adjacency_matrix\n\n# Implementacion del algoritmo de Floyd\n# Entrada: La matriz de adyacencia\n# Salida: Retorna la matriz de adyacencia con los caminos entre cada nodo\n# Complejidad: O(V^3)\ndef floyd(adjacency_matrix):\n    # Inicializar la diagonal de la matriz de adyacencia en 0 y los 0 en infinito\n    for i in range(len(adjacency_matrix)):\n        for j in range(len(adjacency_matrix)):\n            # Si no hay conexion entre los nodos, se pone infinito siempre y cuando no sea en la diagonal\n            if adjacency_matrix[i][j] == 0 and i != j:\n                adjacency_matrix[i][j] = float('inf')\n            elif i == j:\n                adjacency_matrix[i][j] = 0 # Poner la diagonal en 0\n    \n    for k in range(len(adjacency_matrix)):\n        for i in range(len(adjacency_matrix)):\n            for j in range(len(adjacency_matrix)):\n                adjacency_matrix[i][j] = min(adjacency_matrix[i][j], adjacency_matrix[i][k] + adjacency_matrix[k][j])\n    \n    return adjacency_matrix\n\ndef main():\n    _, adjacency_list, node_names = read_graph_from_file('graph1.txt')\n\n    print('--------------- Prueba de Prim ---------------')\n    distances, fromSource = prim(adjacency_list)\n    print(\"Costo total del arbol:\", sum(distances))\n    for i in range(1, len(fromSource)):\n        if fromSource[i] is not None:\n            print(f\"{node_names[fromSource[i]]} -> {node_names[i]}: {distances[i]}\")\n\n    _, adjacency_list, node_names = read_graph_from_file('graph2.txt')\n    print('\\n------------- Prueba de Dijkstra -------------')\n    \n    for i in range(1, len(adjacency_list)):\n        distances, fromSource = dijkstra(adjacency_list, 0, i)\n        print('Camino mas corto desde', node_names[0], 'hasta', node_names[i], end=': ')\n        print_path(distances, fromSource, node_names, i)\n\n    adjancency_matrix, _, node_names = read_graph_from_file('graph3.txt')\n    print('\\n------------- Prueba de Warshall -------------')\n    adjacency_matrix = warshall(adjancency_matrix)\n    for i in range(len(adjacency_matrix)):\n        string = f\"{node_names[i]} tiene un camino hacia: \"\n        for j in range(len(adjacency_matrix)):\n            if adjacency_matrix[i][j] >= 1:\n                string += f\"{node_names[j]}\"\n                if j != len(adjacency_matrix) - 1:\n                    string += ', '\n        print(string)\n    \n    adjancency_matrix, _, node_names = read_graph_from_file('graph4.txt')\n    print('\\n-------------- Prueba de Floyd ---------------')\n    adjacency_matrix = floyd(adjancency_matrix)\n    for i in range(len(adjacency_matrix)):\n        for j in range(len(adjacency_matrix)):\n            if i != j:\n                if adjacency_matrix[i][j] == float('inf'):\n                    print(f\"No hay camino de {node_names[i]} hacia {node_names[j]}\")\n                else:\n                    print(f\"Desde '{node_names[i]}' hacia '{node_names[j]}' el costo es de {adjacency_matrix[i][j]}\")\n    \n    # ¿Cómo harías para reconstruir el camino? \n    # Para poder reconstruir el camino, se puede hacer otra matriz que guarde el nodo anterior a cada nodo, de esta manera se puede reconstruir el camino\n    # de manera similar a como se hace en Dijkstra para cada nodo. Simplemente habria que guardar el nodo anterior a cada nodo en la matriz y luego recorrer\n    # la matriz para reconstruir el camino segun el nodo incial y el nodo final elegidos.\n\nif __name__ == \"__main__\":\n    main()","repo_name":"CDavidSV/Algorithms","sub_path":"Python/Graphs.py","file_name":"Graphs.py","file_ext":"py","file_size_in_byte":8556,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6620608517","text":"import json\nimport re\nimport sys\nimport time\nfrom random import randrange\n\nimport requests\nfrom requests.exceptions import ConnectionError, RetryError, \\\n    SSLError, HTTPError, ConnectTimeout, TooManyRedirects, InvalidHeader\nfrom bs4 import BeautifulSoup\nfrom pymongo import MongoClient\nfrom requests.models import Response\n\nfrom tweepy.streaming import StreamListener\nimport socket\nfrom socket import gaierror\n\n\nIS_PY3 = sys.version_info >= (3, 0)\n\nif not IS_PY3:\n    print(\"Sorry, requires Python 3.\")\n    sys.exit(1)\n\n# Use or create a twitterDB\nMONGO_HOST = 'mongodb://localhost/twitterSentiment'\nmongoClient = MongoClient(MONGO_HOST)\ntwitterDB = mongoClient.twitterSentiment\nTweetCollection = twitterDB.tweetSentiment\n\n\n# Override the stream class\nclass TwitterStreamListener(StreamListener):\n    blacklist = ['[document]', 'noscript', 'header', 'html', 'meta', 'head', 'input', 'script', 'style', '/n']\n\n    def __init__(self):\n        self.count = 0\n        self.max_count = 150  # max 300 tweets\n\n    def on_data(self, raw_data):\n        try:\n            # if TweetCollection.find({}, {\"text\": 1}):\n            #     return False\n            # else:\n            raw_data\n        except TypeError:\n            print(\"Completed:\")\n\n        else:\n            try:\n                twitter_data_dict = json.loads(raw_data)\n\n                if twitter_data_dict['retweeted'] or 'RT @' in twitter_data_dict['text']:\n                    return\n                twitter_data_text = twitter_data_dict[\"text\"]\n                TweetCollection.insert_one(twitter_data_dict)\n                self.count += 1\n\n                if twitter_data_text is None:\n                    return True  # continue with next data stream\n\n                if self.count == self.max_count:\n                    print(self.count, \": tweets reached\", \"expected maximum tweets:\", self.max_count)\n                    return False  # disconnect stream ?\n\n                else:\n                    tweet_urls = re.findall(\n                        r'(http[s]?://(?!twitter)(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)'\n                        , twitter_data_text)\n\n                    for url in tweet_urls:\n                        try:\n                            time.sleep(randrange(2, 10))\n                            response: Response = requests.get(url)\n                            print('tweet No:', self.count, 'url',  response.url)\n\n                        except socket.error:\n                            print(\"connection refused:\", requests.get(url, verify=False).url)\n                            continue\n\n                        except requests.packages.urllib3.exceptions.MaxRetryError as e:\n                            print(str(e))\n                            continue\n\n                        except requests.ConnectionError as e:\n                            print(\n                                \"OOPS!! Connection Error. Make sure you are connected to Internet. Technical Details \"\n                                \"given below.\\n\")\n                            print(str(e))\n                            continue\n\n                        except requests.Timeout as e:\n                            print(\"OOPS!! Timeout Error\")\n                            print(str(e))\n                            continue\n\n                        except ConnectionError as e:\n                            print(\"OOPS!! connection Error\")\n                            print(str(e))\n                            continue\n\n                        except SSLError as e:\n                            print(\"OOPS!! SSL Error\")\n                            print(str(e))\n                            continue\n\n                        except RetryError as e:\n                            print(\"OOPS!! retry Error\")\n                            print(str(e))\n                            continue\n\n                        except HTTPError as e:\n                            print(\"OOPS!! http Error\")\n                            print(str(e))\n                            continue\n\n                        except gaierror as e:\n                            print(\"OOPS!! gai Error\")\n                            print(str(e))\n                            continue\n\n                        except OSError as e:\n                            print(\"os error, connection refused\")\n                            print(str(e))\n                            continue\n\n                        except ConnectTimeout as e:\n                            print(\"os error, connection refused\")\n                            print(str(e))\n                            continue\n\n                        except TooManyRedirects as e:\n                            print(\"os error, connection refused\")\n                            print(str(e))\n                            continue\n\n                        except InvalidHeader as e:\n                            print(\"os error, connection refused\")\n                            print(str(e))\n                            continue\n\n                        else:\n                            output = ''\n                            if 'https://twitter.com' in response.url:\n                                pass\n                            else:\n                                html_page = response.content\n                                soup = BeautifulSoup(html_page, 'html.parser')\n                                text = soup.find_all(text=True)\n                                for t in text:\n                                    if t.parent.name not in TwitterStreamListener.blacklist:\n                                        output += '{} '.format(t)\n                                Query = {\"text\": twitter_data_text}\n                                values = {\"$set\": {\"text\": twitter_data_text + output}}\n                                TweetCollection.update_one(Query, values)\n\n            except KeyError:\n                return True  # continue if there is no text\n\n    # on failure\n    def on_error(self, status_code):\n        time.sleep(randrange(2, 30))\n        return True\n\n    # on timeout\n    def on_timeout(self):\n        time.sleep(randrange(2, 30))\n        return True\n\n\n\n","repo_name":"hraja101/SentimentAnalysis","sub_path":"src/TwitterDataStream.py","file_name":"TwitterDataStream.py","file_ext":"py","file_size_in_byte":6180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17580507797","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('accounts', '0005_customer_flag_count_notification'),\n    ]\n\n    operations = [\n        migrations.RenameField(\n            model_name='customer',\n            old_name='replyto_email',\n            new_name='_replyto_email',\n        ),\n    ]\n","repo_name":"redblame315/brabbl-backend","sub_path":"brabbl/accounts/migrations/0006_auto_20151110_1520.py","file_name":"0006_auto_20151110_1520.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"184815731","text":"import re\nimport requests\nfrom requests_html import HTML\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\n\nurl = \"https://www.spoonflower.com/en/shop?on=fabric\"\n\ndef scraper(url):\n\toptions = Options()\n\toptions.add_argument(\"--headless\")\n\tdriver = webdriver.Chrome(options=options)\n\tdriver.get(url)\n\treturn driver.page_source\n\ndef extract_id_slug(url_path):\n\tregex = r\"^[^\\s]+(?P<id>\\d+)-(?P<slug>[\\w-]+)$\"\n\tgroup = re.match(regex, url_path)\n\tif not group:\n\t\treturn None, None\n\treturn group['id'], group['slug']\n\ncontent = scraper(url)\n\nhtml_r = HTML(html=content)\nprint(html_r)\n\nfabric_links = [x for x in list(html_r.links) if x.startswith(\"/en/fabric\")]\ndatas = []\nimport pandas as pd\n\nfor path in fabric_links:\n\tid_, slug_ = extract_id_slug(path)\n\tprint(id_, slug_)\n\tdata = {\n\t\t\"id\":id_,\n\t\t\"slug\":slug_,\n\t\t\"path\":path,\n\t\t\"scraped:\": 0 # True / False -> 1 / 0\n\t}\n\tdatas.append(data)\n\ndf = pd.DataFrame(datas)\ndf.head()\n\ndf.to_csv(\"local.csv\")","repo_name":"jaydutt31/MyNotes","sub_path":"Web Scraping with Asyncio/selen.py","file_name":"selen.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73756364728","text":"from implib import *\n\n\ndef translate_day(ukr_day):\n    if ukr_day == c.week_days[0]:\n        return 'Monday.png'\n    elif ukr_day == c.week_days[1]:\n        return 'Tuesday.png'\n    elif ukr_day == c.week_days[2]:\n        return 'Wednesday.png'\n    elif ukr_day == c.week_days[3]:\n        return 'Thursday.png'\n    elif ukr_day == c.week_days[4]:\n        return 'Friday.png'\n    elif ukr_day == c.week_days[5]:\n        return ['Monday.png', 'Tuesday.png', 'Wednesday.png', 'Thursday.png', 'Friday.png']\n\n\ndef get_sch_folder(msg):\n    if msg == '1 курс':\n        return 'B1'\n    elif msg == '2 курс':\n        return 'B2'\n    elif msg == '3 курс':\n        return 'B3'\n    elif msg == '4 курс':\n        return 'B4'\n    elif msg == '1 курс м.':\n        return 'M1'\n    elif msg == '2 курс м.':\n        return 'M2'\n    elif msg == 'Вчителі фізики (Туркменістан)':\n        return 'T'\n    else:\n        return None\n\n\ndef capitalize_n(s, n):\n    return s[:n] + s[n].capitalize() + s[n + 1:]\n\n\ndef get_sport_files():\n    return [file[:-4] for file in os.listdir(sport_sch_path)]\n\n\ndef get_fullname(message):\n    fullname = message.from_user.first_name\n    try:\n        fullname += ' '\n        fullname += message.from_user.last_name\n    except TypeError:\n        pass\n    return (fullname)\n\n\ndef log_to_dialog(message, function):\n    if c.log_to_dialog:\n        if message.chat.id != 394701484:\n            fullname = help.get_fullname(message)\n            bot.send_message(394701484, function + \"\\n\" + fullname)\n\n\ndef check_line_length(line):\n    if len(line) > 25:\n        parts = line.split(' ')\n        res = ''\n        line = ''\n        for i in range(len(parts)):\n            line += ' ' + parts[i]\n            if i == 0:\n                line = line[1:]\n\n            if len(line) > 16 or i == len(parts) - 1:\n                res += line\n                line = ''\n                if not i == len(parts) - 1:\n                    res += '\\n \\t\\t\\t\\t\\t\\t\\t\\t'\n\n        return res\n    else:\n        return line\n\n\ndef create_sch_message(df):\n    join = '├ '\n    end = '└ '\n    dash = '┊ '\n    msg = '*' + df['day'][0] + '*\\n'\n\n    #         \"year\",\n    #         \"day\",\n    #         \"groupname\",\n    #         \"leshead\",\n    #         \"lesnum\",\n    #         \"timestart\",\n    #         \"timeend\",\n    #         \"lesname\",\n    #         \"aud\",\n    #         \"teach\",\n    #         \"sg\",\n    #         \"half\",\n\n    for i in range(1, 5):\n        lesnum = df.loc[df['lesnum'] == i]\n        mod = ''\n\n        if len(df.loc[df['lesnum'] == i]) == 0:\n            continue\n\n        if i == 4 or len(df.loc[df['lesnum'] == i + 1]) == 0:\n            lesmsg = end + '*' + lesnum['leshead'].values[0] + '*\\n'\n        else:\n            lesmsg = join + '*' + lesnum['leshead'].values[0] + '*\\n'\n\n        if not end in lesmsg:\n            mod += dash + '\\t'\n        else:\n            mod += '\\t\\t\\t'\n\n        time = \"_{:02d}:{:02d}-{:02d}:{:02d}_\".format(\n            lesnum['timestart'].values[0].hour,\n            lesnum['timestart'].values[0].minute,\n            lesnum['timeend'].values[0].hour,\n            lesnum['timeend'].values[0].minute,\n        )\n\n        lesmsg += mod + join + time + '\\n'\n\n        h1 = lesnum.loc[(df['half'] == 1) & (df.isna()['sg'] == True)]\n        h2 = lesnum.loc[(df['half'] == 2) & (df.isna()['sg'] == True)]\n\n        s1 = lesnum.loc[(df.isna()['half'] == True) & (df['sg'] == 1)]\n        s2 = lesnum.loc[(df.isna()['half'] == True) & (df['sg'] == 2)]\n\n        h1s1 = lesnum.loc[(df['half'] == 1) & (df['sg'] == 1)]\n        h1s2 = lesnum.loc[(df['half'] == 1) & (df['sg'] == 2)]\n        h2s1 = lesnum.loc[(df['half'] == 2) & (df['sg'] == 1)]\n        h2s2 = lesnum.loc[(df['half'] == 2) & (df['sg'] == 2)]\n\n        if not False in [x.empty for x in [h1, h2, s1, s2, h1s1, h1s2, h2s1, h2s2]]:\n            if lesnum['aud'].values[0] != None:\n                lesmsg += check_line_length(mod + join + lesnum['aud'].values[0] + '\\n')\n            if lesnum['teach'].values[0] == None:\n                lesmsg += check_line_length(mod + end + lesnum['lesname'].values[0] + '\\n')\n            else:\n                lesmsg += check_line_length(mod + join + lesnum['lesname'].values[0] + '\\n')\n                lesmsg += mod + end + lesnum['teach'].values[0] + '\\n'\n\n        else:\n            # ЧИСЕЛЬНИК\n            if not h1.empty:\n                if h2.empty and h2s1.empty and h2s2.empty:\n                    lesmsg += mod + end + '*Чисельник*' + '\\n'\n                    mod += '\\t\\t'\n                else:\n                    lesmsg += mod + join + '*Чисельник*' + '\\n'\n                    mod += dash + '\\t'\n\n                if h1['aud'].values[0] != None:\n                    lesmsg += check_line_length(mod + join + h1['aud'].values[0] + '\\n')\n                if h1['teach'].values[0] == None:\n                    lesmsg += check_line_length(mod + end + h1['lesname'].values[0] + '\\n')\n                else:\n                    lesmsg += check_line_length(mod + join + h1['lesname'].values[0] + '\\n')\n                    lesmsg += mod + end + h1['teach'].values[0] + '\\n'\n\n                mod = mod[:-3]\n\n            if not s1.empty:\n                if s2.empty and h1s2.empty and h2s2.empty:\n                    lesmsg += mod + end + '*1-а підгрупа*' + '\\n'\n                    mod += '\\t\\t'\n                else:\n                    lesmsg += mod + join + '*1-а підгрупа*' + '\\n'\n                    mod += dash + '\\t'\n\n                if s1['aud'].values[0] != None:\n                    lesmsg += check_line_length(mod + join + s1['aud'].values[0] + '\\n')\n                if s1['teach'].values[0] == None:\n                    lesmsg += check_line_length(mod + end + s1['lesname'].values[0] + '\\n')\n                else:\n                    lesmsg += check_line_length(mod + join + s1['lesname'].values[0] + '\\n')\n                    lesmsg += mod + end + s1['teach'].values[0] + '\\n'\n\n                mod = mod[:-3]\n\n            if not h1s1.empty or not h1s2.empty:\n                if h2.empty and h2s1.empty and h2s2.empty:\n                    lesmsg += mod + end + '*Чисельник*' + '\\n'\n                    mod += '\\t\\t'\n                else:\n                    lesmsg += mod + join + '*Чисельник*' + '\\n'\n                    mod += dash + '\\t'\n\n                if not h1s1.empty:\n                    if h1s2.empty:\n                        lesmsg += mod + end + '*1-а підгрупа*' + '\\n'\n                        mod += '\\t\\t'\n                    else:\n                        lesmsg += mod + join + '*1-а підгрупа*' + '\\n'\n                        mod += dash + '\\t'\n\n                    if h1s1['aud'].values[0] != None:\n                        lesmsg += check_line_length(mod + join + h1s1['aud'].values[0] + '\\n')\n                    if h1s1['teach'].values[0] == None:\n                        lesmsg += check_line_length(mod + end + h1s1['lesname'].values[0] + '\\n')\n                    else:\n                        lesmsg += check_line_length(mod + join + h1s1['lesname'].values[0] + '\\n')\n                        lesmsg += mod + end + h1s1['teach'].values[0] + '\\n'\n\n                        mod = mod[:-2]\n\n                if not h1s2.empty:\n                    lesmsg += mod + end + '*2-а підгрупа*' + '\\n'\n                    mod += '\\t\\t'\n\n                    if h1s2['aud'].values[0] != None:\n                        lesmsg += check_line_length(mod + join + h1s2['aud'].values[0] + '\\n')\n                    if h1s2['teach'].values[0] == None:\n                        lesmsg += check_line_length(mod + end + h1s2['lesname'].values[0] + '\\n')\n                    else:\n                        lesmsg += check_line_length(mod + join + h1s2['lesname'].values[0] + '\\n')\n                        lesmsg += mod + end + h1s2['teach'].values[0] + '\\n'\n\n                    mod = mod[:-2]\n\n                mod = mod[:-2]\n\n            if not s2.empty:\n                lesmsg += mod + end + '*2-а підгрупа*' + '\\n'\n                mod += '\\t\\t'\n\n                if s2['aud'].values[0] != None:\n                    lesmsg += check_line_length(mod + join + s2['aud'].values[0] + '\\n')\n                if s2['teach'].values[0] == None:\n                    lesmsg += check_line_length(mod + end + s2['lesname'].values[0] + '\\n')\n                else:\n                    lesmsg += check_line_length(mod + join + s2['lesname'].values[0] + '\\n')\n                    lesmsg += mod + end + s2['teach'].values[0] + '\\n'\n\n                mod = mod[:-2]\n\n            # ЗНАМЕННИК\n            if not h2.empty:\n                lesmsg += mod + end + '*Знаменник*' + '\\n'\n                mod += '\\t\\t'\n\n                if h2['aud'].values[0] != None:\n                    lesmsg += (mod + join + h2['aud'].values[0] + '\\n')\n                if h2['teach'].values[0] == None:\n                    lesmsg += check_line_length(mod + end + h2['lesname'].values[0] + '\\n')\n                else:\n                    lesmsg += check_line_length(mod + join + h2['lesname'].values[0] + '\\n')\n                    lesmsg += mod + end + h2['teach'].values[0] + '\\n'\n\n                mod = mod[:-2]\n\n            if not h2s1.empty or not h2s2.empty:\n                lesmsg += mod + join + '*Знаменник*' + '\\n'\n                mod += '\\t\\t'\n\n                if not h2s1.empty:\n                    if h2s2.empty:\n                        lesmsg += mod + end + '*1-а підгрупа*' + '\\n'\n                        mod += '\\t\\t'\n                    else:\n                        lesmsg += mod + join + '*1-а підгрупа*' + '\\n'\n                        mod += dash + '\\t'\n\n                    if h2s1['aud'].values[0] != None:\n                        lesmsg += check_line_length(mod + join + h2s1['aud'].values[0] + '\\n')\n                    if h2s1['teach'].values[0] == None:\n                        lesmsg += check_line_length(mod + end + h2s1['lesname'].values[0] + '\\n')\n                    else:\n                        lesmsg += check_line_length(mod + join + h2s1['lesname'].values[0] + '\\n')\n                        lesmsg += mod + end + h2s1['teach'].values[0] + '\\n'\n\n                    mod = mod[:-3]\n\n                if not h2s2.empty:\n                    lesmsg += mod + end + '*2-а підгрупа*' + '\\n'\n                    mod += '\\t\\t'\n\n                    if h2s2['aud'].values[0] != None:\n                        lesmsg += check_line_length(mod + join + h2s2['aud'].values[0] + '\\n')\n                    if h2s2['teach'].values[0] == None:\n                        lesmsg += check_line_length(mod + end + h2s2['lesname'].values[0] + '\\n')\n                    else:\n                        lesmsg += check_line_length(mod + join + h2s2['lesname'].values[0] + '\\n')\n                        lesmsg += mod + end + h2s2['teach'].values[0] + '\\n'\n\n                    mod = mod[:-2]\n\n                mod = mod[:-2]\n\n        if not (i == 4 or len(df.loc[df['lesnum'] == i + 1]) == 0):\n            lesmsg += dash + '\\n'\n        msg += lesmsg\n    return (msg)\n\ndef tuple_from_string(string):\n    string = [i[0].split(sep=',') for i in string]\n    string = [(i[0][1:], i[1][:-1]) for i in string]\n    return string\n\ndef check_time_diff(time1, time2, intsec):\n    sec1 = time1.hour*3600 + time1.minute*60 + time1.second\n    sec2 = time2.hour*3600 + time2.minute*60 + time2.second\n    return sec1 - sec2 <= intsec and sec1 - sec2 > 0\n\n","repo_name":"lap1dem/physfac-bot-stable","sub_path":"modules/help_functions.py","file_name":"help_functions.py","file_ext":"py","file_size_in_byte":11512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69797317688","text":"from absl.testing import absltest\nfrom xmanager.contrib import addressing\n\n\nclass AddressingTest(absltest.TestCase):\n\n  def test_k8s_pod_domain(self):\n    address = addressing.k8s_pod_domain(\n        job_name='cifar10',\n        experiment_id=123,\n        work_unit_id=4,\n        service='best_service',\n        namespace='best_namespace',\n    )\n\n    self.assertEqual(\n        address,\n        '123-4-cifar10.best_service.best_namespace.svc.cluster.local:2222',\n    )\n\n\nif __name__ == '__main__':\n  absltest.main()\n","repo_name":"deepmind/xmanager","sub_path":"xmanager/contrib/addressing_test.py","file_name":"addressing_test.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":708,"dataset":"github-code","pt":"77"}
+{"seq_id":"3693893946","text":"\"\"\"\n------------Problem Statement---------\nYou are given a non-negative number in the form of list elements. For example, the number 123 would be provided as arr = [1, 2, 3]. Add one to the number and return the output in the form of a new list.\n\nExample 1:\n\ninput = [1, 2, 3]\noutput = [1, 2, 4]\nExample 2:\n\ninput = [9, 9, 9]\noutput = [1, 0, 0, 0]\n\nSolution-1:\nOne way to solve this problem is to convert the input array into a number and then add one to it.\nFor example, if we have input = [1, 2, 3], you could solve this problem by creating the number\n123 and then separating the digits of the output number 124.\n\nSolution-2, Efficient:\nJust add 1 the way we humans add 1 to any value. Start from the end value, add 1 to it and then keep carry/borrow\nif needed and keep adding the carry to remaining values\n\"\"\"\n\n\ndef convert_array_to_decimal(array):\n    # Given input ['1', '2', '3'], the output will be\n    # decimal = 1*10^2 + 2*10^1 + 3*10^0 = 123\n    decimal = 0\n    power = len(array) - 1\n    for value in array:\n        decimal += value * pow(10, power)\n        power -= 1\n\n    return decimal\n\n\ndef test_convert_array_to_decimal():\n    assert(convert_array_to_decimal([1, 2, 3]) == 123)\n\n\ntest_convert_array_to_decimal()\n\n\ndef convert_decimal_to_array(decimal):\n    array = []\n    while decimal > 0:\n        result = decimal % 10\n        decimal = int(decimal / 10)\n        array.insert(0, result)\n\n    return array\n\n\ndef test_convert_decimal_to_array():\n    assert(convert_decimal_to_array(123) == [1, 2, 3])\n\n\ntest_convert_decimal_to_array()\n\n\ndef add_one(array):\n    \"\"\"\n    One way to solve this problem is to convert the input array into a number and then add one to it.\n    For example, if we have input = [1, 2, 3], you could solve this problem by creating the number\n    123 and then separating the digits of the output number 124.\n    \"\"\"\n    if len(array) == 0:\n        return ['1']\n\n    decimal = convert_array_to_decimal(array)\n    decimal += 1\n    output = convert_decimal_to_array(decimal)\n    return output\n\ndef add_one_efficient(array):\n    \"\"\"\n    Just add 1 the way we humans add 1 to any value. Start from the end value, add 1 to it and then keep carry/borrow\n    if needed and keep adding the carry to remaining values\n    \"\"\"\n\n    # at start the carry will be 1 as we want to add 1\n    carry = 1\n    # run loop in reverse range [start = n-1, end = -1 (exclusive), step = -1)\n    for i in range(len(array)-1, -1, -1):\n        output = carry + array[i]\n        carry = output // 10\n        array[i] = output % 10\n\n    # at the end if there is still carry then prepend it, example: 99 + 1 = 100, in this carry goes to start\n    if carry > 0:\n        array = [carry] + array\n\n    return array\n\ndef test_add_one(test_case, add_one):\n    arr = test_case[0]\n    solution = test_case[1]\n\n    output = add_one(arr)\n    assert output == solution, f\"output: {output}\"\n\n\ndef run_test_cases(add_one):\n    # Test case-1\n    arr = [0]\n    solution = [1]\n    test_case = [arr, solution]\n    test_add_one(test_case, add_one)\n\n    # Test case-2\n    arr = [1, 2, 3]\n    solution = [1, 2, 4]\n    test_case = [arr, solution]\n    test_add_one(test_case, add_one)\n\n    # Test case-3\n    arr = [9, 9, 9]\n    solution = [1, 0, 0, 0]\n    test_case = [arr, solution]\n    test_add_one(test_case, add_one)\n\n\n# run test cases on normal `add_one()`\nrun_test_cases(add_one)\n# run test cases on `add_one_efficient()`\nrun_test_cases(add_one_efficient)","repo_name":"informramiz/data-structures-and-algorithms","sub_path":"lists/problems/add_one.py","file_name":"add_one.py","file_ext":"py","file_size_in_byte":3446,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21240914584","text":"from odoo import api, fields, models, _\nfrom odoo.exceptions import ValidationError, UserError\nfrom odoo.tools import float_round\nfrom datetime import datetime\nfrom functools import partial\nfrom itertools import groupby\nfrom odoo.tools.float_utils import float_compare, float_is_zero, float_round\nfrom . import constantes\nimport logging\n\n_logging = logging.getLogger(__name__)\n\nclass PurchaseOrder(models.Model):\n\t_inherit = \"purchase.order\"\n\n\tdef _prepare_invoice(self):\n\t\tself.ensure_one()\n\t\tres = super(PurchaseOrder, self)._prepare_invoice()\n\n\t\tmove_type = 'in_invoice'\n\t\tcompany_id = (self.company_id or self.env.company).id\n\t\tdomain = [('company_id', '=', company_id), ('type', 'in', ['purchase'])]\n\n\t\tjournal = None\n\t\tcurrency_id = self.currency_id.id or self._context.get('default_currency_id')\n\t\tif currency_id and currency_id != self.company_id.currency_id.id:\n\t\t\tcurrency_domain = domain + [('currency_id', '=', currency_id)]\n\t\t\tjournal = self.env['account.journal'].search(currency_domain, limit=1)\n\n\t\tif not journal:\n\t\t\tjournal = self.env['account.journal'].search(domain, limit=1)\n\t\t\t\n\t\tif not journal:\n\t\t\traise UserError(_('Please define an accounting purchase journal for the company %s (%s).') % (self.company_id.name, self.company_id.id))\n\n\t\tpartner_invoice_id = self.partner_id.address_get(['invoice'])['invoice']\n\t\tpartner_invoice = self.env['res.partner'].browse(self.partner_id.address_get(['invoice'])['invoice'])\n\t\t#journal_id = self.env['account.journal'].search([('type', '=', 'purchase')], limit=1)\n\t\ttipo_documento = self.env['l10n_latam.document.type']\n\t\ttipo_doc_id = tipo_documento.search([('code', '=', '01'), ('sub_type', '=', 'purchase')], limit=1)\n\t\tif not tipo_doc_id:\n\t\t\traise UserError('No se encontro un tipo de documento para facturas de compra')\n\t\t\n\t\treg = self\n\t\ttipo_transaccion = 'contado'\n\t\tif reg.payment_term_id:\n\t\t\ttipo_transaccion = reg.payment_term_id.tipo_transaccion or 'contado'\n\n\t\tinvoice_vals = {\n\t\t\t'ref': self.partner_ref or '',\n\t\t\t'move_type': move_type,\n\t\t\t'purchase_id': self.id,\n\t\t\t'journal_id': journal.id,\n\t\t\t'l10n_latam_document_type_id': tipo_doc_id.id,\n\t\t\t'narration': self.notes,\n\t\t\t'currency_id': self.currency_id.id,\n\t\t\t'invoice_user_id': self.user_id and self.user_id.id or self.env.user.id,\n\t\t\t'partner_id': partner_invoice.id,\n\t\t\t'fiscal_position_id': (self.fiscal_position_id or self.fiscal_position_id._get_fiscal_position(partner_invoice)).id,\n\t\t\t'payment_reference': self.partner_ref or '',\n\t\t\t'partner_bank_id': self.partner_id.bank_ids[:1].id,\n\t\t\t'invoice_origin': self.name,\n\t\t\t'invoice_payment_term_id': self.payment_term_id.id,\n\t\t\t'invoice_line_ids': [],\n\t\t\t'company_id': self.company_id.id,\n\t\t\t'tipo_transaccion': tipo_transaccion,\n\t\t}\n\t\tres.update(invoice_vals)\n\t\treturn res\n\n\tdef action_create_invoice(self):\n\t\t\"\"\"Create the invoice associated to the PO.\n\t\t\"\"\"\n\t\tprecision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n\n\t\t# 1) Prepare invoice vals and clean-up the section lines\n\t\tinvoice_vals_list = []\n\t\tfor order in self:\n\t\t\tif order.invoice_status != 'to invoice':\n\t\t\t\tcontinue\n\n\t\t\torder = order.with_company(order.company_id)\n\t\t\tpending_section = None\n\t\t\t# Invoice values.\n\t\t\tinvoice_vals = order._prepare_invoice()\n\t\t\t# Invoice line values (keep only necessary sections).\n\t\t\tfor line in order.order_line:\n\t\t\t\tif line.display_type == 'line_section':\n\t\t\t\t\tpending_section = line\n\t\t\t\t\tcontinue\n\t\t\t\tif not float_is_zero(line.qty_to_invoice, precision_digits=precision):\n\t\t\t\t\tif pending_section:\n\t\t\t\t\t\tinvoice_vals['invoice_line_ids'].append((0, 0, pending_section._prepare_account_move_line()))\n\t\t\t\t\t\tpending_section = None\n\t\t\t\t\tinvoice_vals['invoice_line_ids'].append((0, 0, line._prepare_account_move_line()))\n\t\t\tinvoice_vals_list.append(invoice_vals)\n\n\t\tif not invoice_vals_list:\n\t\t\traise UserError(_('There is no invoiceable line. If a product has a control policy based on received quantity, please make sure that a quantity has been received.'))\n\n\t\t# 2) group by (company_id, partner_id, currency_id) for batch creation\n\t\tnew_invoice_vals_list = []\n\t\tfor grouping_keys, invoices in groupby(invoice_vals_list, key=lambda x: (x.get('company_id'), x.get('partner_id'), x.get('currency_id'))):\n\t\t\torigins = set()\n\t\t\tpayment_refs = set()\n\t\t\trefs = set()\n\t\t\tref_invoice_vals = None\n\t\t\tfor invoice_vals in invoices:\n\t\t\t\tif not ref_invoice_vals:\n\t\t\t\t\tref_invoice_vals = invoice_vals\n\t\t\t\telse:\n\t\t\t\t\tref_invoice_vals['invoice_line_ids'] += invoice_vals['invoice_line_ids']\n\t\t\t\torigins.add(invoice_vals['invoice_origin'])\n\t\t\t\tpayment_refs.add(invoice_vals['payment_reference'])\n\t\t\t\trefs.add(invoice_vals['ref'])\n\t\t\tref_invoice_vals.update({\n\t\t\t\t'ref': ', '.join(refs)[:2000],\n\t\t\t\t'invoice_origin': ', '.join(origins),\n\t\t\t\t'payment_reference': len(payment_refs) == 1 and payment_refs.pop() or False,\n\t\t\t})\n\t\t\tnew_invoice_vals_list.append(ref_invoice_vals)\n\t\tinvoice_vals_list = new_invoice_vals_list\n\n\t\t# 3) Create invoices.\n\t\tmoves = self.env['account.move']\n\t\tAccountMove = self.env['account.move'].with_context(default_move_type='in_invoice')\n\t\tfor vals in invoice_vals_list:\n\t\t\tmoves |= AccountMove.with_company(vals['company_id']).create(vals)\n\n\t\t# 4) Some moves might actually be refunds: convert them if the total amount is negative\n\t\t# We do this after the moves have been created since we need taxes, etc. to know if the total\n\t\t# is actually negative or not\n\t\tmoves.filtered(lambda m: m.currency_id.round(m.amount_total) < 0).action_switch_invoice_into_refund_credit_note()\n\n\t\treturn self.action_view_invoice(moves)\n\nclass PurchaseOrderLine(models.Model):\n\t_inherit = \"purchase.order.line\"\n\n\tnro_oc = fields.Char(\"Nro OC\", related=\"order_id.name\", store=True)\n\tref_proveedor_n2 = fields.Char(\"Nro. Factura Proveedor\", related=\"invoice_lines.move_id.ref\", store=True)\n\tfecha_pedido = fields.Datetime(\"Fecha Pedido\", related=\"order_id.date_order\", store=True)\n\tproveedor = fields.Many2one('res.partner', related='order_id.partner_id', store=True)\n\tuom_po_id = fields.Many2one('uom.uom', related=\"product_id.uom_po_id\")","repo_name":"Lobonick/cens-test","sub_path":"solse_pe_cpe_purchase/models/purchase_order.py","file_name":"purchase_order.py","file_ext":"py","file_size_in_byte":6033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"19312687497","text":"# 本脚本参考来自https://www.jianshu.com/p/ae3fe782d7c8，在此声明\n\n# 1  RAP_ID →  MSU_ID\n\nrelation={}\nfor i in open(\"RAP-MSU_2018-03-29.txt\"): #同目录下可找到该文件\n    rap=str(i.split()[0])\n    msu=str(i.split()[1])\n    if msu!=\"None\":\n        relation[rap]=msu\n\nfor j in open(\"Os01g.txt\"):\n    id = j.strip()\n    if id in relation.keys():\n        if \",\" in relation[id]:\n            s=relation[id].split(\",\")\n            for a in s:\n                print(id,a,sep=\"\\t\")\n        else:\n            print(id,relation[id],sep=\"\\t\")\n    else:\n        print(id,\"None\",sep=\"\\t\")\n        \n# 2 MUS_ID → RAP_ID\n\nrelation = {}\nfor i in open(\"RAP-MSU_2018-03-29.txt\"):\n    rap=str(i.split()[0])\n    msu=str(i.split()[1])\n    if msu!=\"None\":\n        if \",\" in msu:\n            for a in msu.split(\",\"):\n                relation[a[0:-2]]=rap\n        else:\n            relation[msu[0:-2]]=rap\nfor j in open(\"MSU_IDtest.txt\"):\n    id=j.strip()\n    if id[0:-2] in relation.keys():\n        print(id,relation[id[0:-2]],sep=\"\\t\")\n    else:\n        print(id,\"None\",sep=\"\\t\")\n       \n# 3 RAP_ID 内部ID的转换  如 Os01g0102800 → Os01t0102800-01 并不是如此简单直接在后面加上-01，因为有些基因后面是-02，所以还是得找到一个ID匹配文档\n\nrelation={}\nfor i in open(\"RAP_Locus2ID.txt\"):\n    locus=str(i.split(\"\\t\")[0])\n    id=str(i.split(\"\\t\")[1]).strip()\n    relation[locus]=id\nfor j in open(\"Os01g.txt\"):\n    id = j.strip()\n    if id in relation.keys():\n       print(id,relation[id],sep=\"\\t\")\n    else:\n       print(id,\"None\",sep=\"\\t\")\n\n# 4 再加一个 RAP_ID → Uniprot\nrelation={}\nfor i in open(\"Uniprot_id.txt\"):\n    locus=str(i.split()[0])\n    id=str(i.split()[1])\n    relation[locus]=id\nfor j in open(\"Os01g.txt\"):\n    id = j.strip()\n    if id in relation.keys():\n       print(id,relation[id],sep=\"\\t\")\n    else:\n       print(id,\"None\",sep=\"\\t\")\n\n \n # 这里有2个问题，就是 \n#  1：locus=str(i.split(\"\\t\")[0]) 必须加\"\\t\",因为不加就会出现“IndexError: list index out of range” 说索引越界了；\n#  2：id=str(i.split(\"\\t\")[1].strip() 必须加strip()否则结果就是一行之后都有一个空行\n#  关键的是并没有找到原因。\n  \n          \n          \n \n","repo_name":"taotao0501/Python_scripts","sub_path":"Rice MSU_RAP_ID convert.py","file_name":"Rice MSU_RAP_ID convert.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"21036170503","text":"import pandas as pd\nfrom utils import  get_path_raw_data\n\n\ndef followup_to_pickle(kind='train'):\n    \"\"\"\n    :param kind:\n    :return:\n    \"\"\"\n    # 读取数据\n    followup = pd.read_csv(get_path_raw_data() + '{}_followup.csv'.format(kind))\n    followup['FOLLOWUP_DATE'] = pd.to_datetime(followup['FOLLOWUP_DATE'])\n    # 按时间排序\n    followup = followup.sort_values(by=['FOLLOWUP_DATE'])\n    # 保存结果\n    followup.to_pickle(get_path_raw_data() + '{}_followup.pkl'.format(kind))\n\n\ndef personinfo_to_pickle(kind='train'):\n    \"\"\"\n    :param kind:\n    :return:\n    \"\"\"\n    # 读取数据\n    personinfo = pd.read_csv(get_path_raw_data() + '{}_personinfo.csv'.format(kind))\n    # 时间处理\n    personinfo['DATE_OF_BIRTH'] = pd.to_datetime(personinfo['DATE_OF_BIRTH'])\n    personinfo['CONFIRM_DATE'] = pd.to_datetime(personinfo['CONFIRM_DATE'])\n    personinfo['CHD_DATE'] = pd.to_datetime(personinfo['CHD_DATE'])\n    # 保存数据\n    personinfo.to_pickle(get_path_raw_data() + '{}_personinfo.pkl')\n","repo_name":"datamininger/cardiovascularriskprediction","sub_path":"Code/Feat/_0_0_to_pickle.py","file_name":"_0_0_to_pickle.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21148903017","text":"# Imports\n# Python STL\nimport logging\nfrom pathlib import Path\nimport sys\n# Plotting\nimport matplotlib.pyplot as plt\n# PyTorch\nimport torch\nimport torch.backends.cudnn as cudnn\n# Advanced configurations\nimport hydra\n\n# Get root logger\nlogger = logging.getLogger('root')\n\n\n@hydra.main(config_path=\"config/config.yaml\")\ndef train(cfg):\n    # Show working directory\n    logger.info(f\"Running experiment in {Path.cwd()}\")\n    # Validate configuration\n    cfg = validate_config(cfg)\n\n    # Set constants based on config\n    # Faster convolutions at the expense of memory\n    cudnn.benchmark = cfg.training.cudnn_benchmark\n\n    # Get trainer from config\n    trainer = hydra.utils.get_class(cfg.trainer)(cfg)\n\n    # `try-except` to save model before exiting if ^C was pressed\n    try:\n        # Start training + validation\n        trainer.start()\n    except KeyboardInterrupt or SystemExit:\n        logger.info(\"Exit requested during train-val\")\n        # Don't save state in debugging mode\n        if cfg.debugging:\n            logger.warning(\"NOT saving state in DEBUG mode\")\n            sys.exit(0)\n        # Collect state\n        state = {\n            \"epoch\": trainer.current_epoch,\n            \"best_loss\": trainer.best_loss,\n            \"state_dict\": trainer.net.state_dict(),\n            \"optimizer\": trainer.optimizer.state_dict(),\n        }\n        logger.info(\"**** Saving state before exiting ****\")\n        # Create file with parent directories if it doesn't exist\n        Path(cfg.final_weights_path).parent.mkdir(parents=True, exist_ok=True)\n        # Save state\n        try:\n            torch.save(state, cfg.final_weights_path)\n        except IOError:\n            logger.warning(f\"Could not save in {cfg.final_weights_path}\", exc_info=True)\n        else:\n            logger.info(\"Saved 🎉\")\n        # Exit\n        sys.exit(0)\n\n    # TODO Fix metric and loss plots for multiclass\n    # for metric_name, metric_values in trainer.meter.store.items():\n    #     metric_plot(cfg, metric_values, metric_name)\n\n\n# TODO Validate everything else in config\ndef validate_config(cfg):\n    # dataset\n    # dataset.root\n    if not Path(cfg.dataset.root).is_dir():\n        raise NotADirectoryError(f\"{cfg.dataset.root} is not a directory or it doesn't exist.\")\n\n    # dataset.root/imgs/[train, val]/[imgs, masks]\n    _path_to_imgs = Path(cfg.dataset.root) / \"train\" / cfg.dataset.image_dir\n    if not Path(_path_to_imgs).is_dir():\n        raise FileNotFoundError(f\"{_path_to_imgs} doesn't exist.\")\n    _path_to_imgs = Path(cfg.dataset.root) / \"val\" / cfg.dataset.image_dir\n    if not Path(_path_to_imgs).is_dir():\n        raise FileNotFoundError(f\"{_path_to_imgs} doesn't exist.\")\n    _path_to_imgs = Path(cfg.dataset.root) / \"train\" / cfg.dataset.mask_dir\n    if not Path(_path_to_imgs).is_dir():\n        raise FileNotFoundError(f\"{_path_to_imgs} doesn't exist.\")\n    _path_to_imgs = Path(cfg.dataset.root) / \"val\" / cfg.dataset.mask_dir\n    if not Path(_path_to_imgs).is_dir():\n        raise FileNotFoundError(f\"{_path_to_imgs} doesn't exist.\")\n\n    # dataset.num_classes\n    if not cfg.dataset.num_classes >= 2:\n        raise ValueError(f\"Number of classes must be >= 2. 2 => Binary, >2 => Multi-class\")\n\n    # dataset.class_dict\n    if not len(cfg.dataset.class_dict) == cfg.dataset.num_classes:\n        raise ValueError(f\"Length of class dict must be same as number of classes.\")\n\n    # device\n    if torch.cuda.is_available() and cfg.device == 'cpu':\n        logger.warning(\"Using device: 'cpu' when device: 'cuda' is available\")\n    elif not torch.cuda.is_available() and cfg.device != 'cpu':\n        logger.warning(\"Setting device to 'cpu' as device: 'cuda' is not available\")\n        cfg.device = 'cpu'\n\n    return cfg\n\n\n# Helper function to plot scores at the end of training\ndef metric_plot(cfg, scores, name):\n    plt.figure(figsize=(15, 5))\n    # Plot training scores\n    plt.plot(range(len(scores[\"train\"])),\n             scores[\"train\"],\n             label=f'train {name}')\n    # Plot validation scores\n    plt.plot(range(len(scores[\"val\"])),\n             scores[\"val\"],\n             label=f'val {name}')\n    plt.title(f'{name} plot')\n    plt.xlabel('Epoch')\n    plt.ylabel(f'{name}')\n    plt.legend()\n    # Create path if it doesn't exist\n    save_path = Path(cfg.training.results_dir) / f'{name}.png'\n    save_path.parent.mkdir(parents=True, exist_ok=True)\n    plt.savefig(save_path, bbox_inches='tight', pad_inches=1)\n\n\nif __name__ == \"__main__\":\n    train()\n","repo_name":"rshwndsz/histovision","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21252995264","text":"from django.urls import path\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom product.views import (index, blog, blog_details, check_out,\n    contact, faq, login, main, product, register, shop,\n    shopping_cart)\n\n\nurlpatterns = [\n    path(\"\", index, name=\"index\"),\n    path(\"blog/\", blog, name=\"blog\"),\n    path(\"blog_details/\", blog_details, name=\"blog_details\"),\n    path(\"check_out/\", check_out, name=\"check_out\"),\n    path(\"contacts/\", contact, name=\"contact\"),\n    path(\"faq/\", faq, name=\"faq\"),\n    path(\"logins/\", login, name=\"logins\"),\n    path(\"8/\", main, name=\"main\"),\n    path(\"9/\", product, name=\"product\"),\n    path(\"register/\", register, name=\"register\"),\n    path(\"shop/\", shop, name=\"shop\"),\n    path(\"shopping_cart/\", shopping_cart, name=\"shopping_cart\"),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\\\n    + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n","repo_name":"Ataiskih/fashi","sub_path":"product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42718043941","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 28 17:35:17 2018\n\n@author: Marley\n\"\"\"\nimport numpy as np, cmath,scipy as sp\nfrom numpy.fft import fft, ifft\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport scipy.io\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom numpy import pi, sin, cos, exp, sqrt, log, log10, random, angle  #import basic functions from numpy that we'll need\n\n\nfrom sklearn import model_selection\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\n\n\ndef epoch_data(data, window_length, overlap):\n    arr = []\n    i = 0\n    while i + window_length < len(data):\n        arr.append(data[i:i+window_length])\n        i += overlap\n    return np.array(arr)\ndef get_features(data_epochs, sampling_freq):\n    num_epochs, win_length = data_epochs.shape\n    Y = fft(data_epochs)/win_length\n    PSD = 2*np.abs(Y[0:int(win_length/2), :])\n    f = sampling_freq/2*np.linspace(0, 1, int(win_length/2))\n    # SPECTRAL FEATURES\n    # Average of band powers\n    # Delta <4\n    ind_delta, = np.where(f < 4)\n    meanDelta = np.mean(PSD[:, ind_delta], axis = -1)\n    # Theta 4-8\n    ind_theta, = np.where((f >= 4) & (f <= 8))\n    meanTheta = np.mean(PSD[:, ind_theta], axis = -1)\n    # Alpha 8-12\n    ind_alpha, = np.where((f >= 8) & (f <= 12))\n    meanAlpha = np.mean(PSD[:, ind_alpha], axis = -1)\n    # Beta 12-30\n    ind_beta, = np.where((f >= 12) & (f < 30))\n    meanBeta = np.mean(PSD[:, ind_beta], axis=-1)\n    feature_vector = np.array([meanDelta, meanTheta, meanAlpha,\n                                     meanBeta]).T\n    feature_vector = np.log10(feature_vector)\n    return feature_vector\ndef draw_specgram(ch, fs_Hz):\n    NFFT = fs_Hz*2\n    overlap = NFFT - int(0.25 * fs_Hz)\n    spec_PSDperHz, spec_freqs, spec_t = mlab.specgram(np.squeeze(ch),\n                                   NFFT=NFFT,\n                                   window=mlab.window_hanning,\n                                   Fs=fs_Hz,\n                                   noverlap=overlap\n                                   ) # returns PSD power per Hz\n    # convert the units of the spectral data\n    \n    spec_PSDperBin = spec_PSDperHz * fs_Hz / float(NFFT)\n    f_lim_Hz = [0, 60]   # frequency limits for plotting\n    plt.figure(figsize=(10,5))\n    ax = plt.subplot(1,1,1)\n    plt.pcolor(spec_t, spec_freqs, 10*np.log10(spec_PSDperBin))  # dB re: 1 uV\n    plt.clim([-25,26])\n    plt.xlim(spec_t[0], spec_t[-1]+1)\n    plt.ylim(f_lim_Hz)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.show()\n    return spec_freqs, spec_t, spec_PSDperHz\ndef nextpow2(i):\n    n = 1\n    while n < i: n *= 2\n    return n\n\ndef closest(X, p):\n    disp = X - p\n    return np.argmin((disp*disp))\n\ndef wavelet_spectrogram(ch, EEGtimes, plot=True, min_freq = 2, max_freq = 80, num_frex = 30):\n    #define wavelet parameters\n    time = np.arange(-1,1+1/sampling_freq,1/sampling_freq)\n    frex = np.logspace(log10(min_freq),log10(max_freq),num_frex)\n    s    = np.logspace(log10(3),log10(10),num_frex)/(2*pi*frex)\n    \n    \n    # define convolution parameters\n    n_wavelet = len(time)\n    n_data = len(ch)\n    n_convolution = n_wavelet + n_data - 1\n    n_conv_pow2 = nextpow2(n_convolution)\n    half_of_wavelet_size = int((n_wavelet-1)/2)\n    \n    \n    \n    #get FFT of data\n    eegfft = fft(ch,n_conv_pow2)\n    \n    \n    baseidx = [closest(EEGtimes,x) for x in [-500,-200]]\n    \n    \n    #initialize\n    eegpower = np.zeros([num_frex,n_data]) #frequencies x time x trials\n    for fi in range(num_frex):\n        \n        #take the fft of the wavelet\n        wavelet = fft(np.sqrt(1/(s[fi]*np.sqrt(pi))) * exp(2*1j*pi*frex[fi]*time) * exp(-time**2/(2*(s[fi]**2))),n_conv_pow2)\n    \n        #convolution \n        eegconv = ifft(wavelet*eegfft) #convolution theorem\n        eegconv = eegconv[:n_convolution]\n        eegconv = eegconv[half_of_wavelet_size:-half_of_wavelet_size]\n        \n        #average power over trials\n        #this performs baseline transform, which is covered in more depth in ch18\n        \n        #temppower = np.mean(np.absolute(np.reshape(eegconv,[EEGpnts,EEGtrials],order=\"F\"))**2,axis=1)\n        #eegpower[fi,:] = 10*log10(temppower/np.mean(temppower[baseidx[0]:baseidx[1]]))\n        eegpower[fi,:] = np.absolute(eegconv)**2\n        \n    if plot:\n        plt.figure(figsize=[20,6])\n        ax1=plt.subplot(121)\n        ax1.set_yscale(\"log\")\n            \n        CS = plt.contourf(EEGtimes,frex, 10*np.log10(eegpower), 40,\n                        #vmin=-3,vmax=3,\n                        cmap=plt.cm.jet,\n                        origin = \"lower\",\n                        )\n        ax1.set_yticks(np.logspace(log10(min_freq),log10(max_freq),6))\n        ax1.set_yticklabels(np.round(np.logspace(log10(min_freq),log10(max_freq),6)))\n        ax1.set_xlim([1000,3000])\n        plt.title(\"Logarithmic frequency scaling\")\n        \n        \n        ax2=plt.subplot(122)\n        #CS = plt.contourf(EEGtimes,frex, eegpower, 40,\n                        #vmin=-3,vmax=3,\n                        #cmap=plt.cm.jet,\n        #                 origin = ,)\n        plt.pcolor(EEGtimes, frex,10*np.log10(eegpower))\n        #plt.clim([-25,26])\n        #ax2.set_xlim([200,1200])\n        plt.title(\"Linear frequency scaling\")\n        \n        plt.tight_layout()\n    return frex, eegpower\n\nfname_ec = 'EyesClosedNTXDemo.txt' \nfname_eo = 'EyesOpenedNTXDemo.txt' \ndata_ec = np.loadtxt(fname_ec,\n                  delimiter=',',\n                  skiprows=7,\n                  usecols=(1))\ndata_eo = np.loadtxt(fname_eo,\n                  delimiter=',',\n                  skiprows=7,\n                  usecols=(1))\nsampling_freq = 250\nwindow_size = int(3 * sampling_freq)\nwindow_overlap = int(0.5 * sampling_freq)\n\ndata_epochs_ec = epoch_data(data_ec, window_size, window_overlap)\ndata_epochs_eo = epoch_data(data_eo, window_size, window_overlap)\n\nec_features = get_features(data_epochs_ec, sampling_freq)\neo_features = get_features(data_epochs_eo, sampling_freq)\n\nec = np.hstack((ec_features, np.ones([ec_features.shape[0],1])))\neo = np.hstack((eo_features, np.zeros([eo_features.shape[0],1])))\n\n'''        \nX = np.vstack((ec, eo))[:,:-1]\nY = np.vstack((ec, eo))[:,-1]\nvalidation_size = 0.20\nseed = 7\nX_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)\n\n# Test options and evaluation metric\nseed = 7\nscoring = 'accuracy'\n\n# Spot Check Algorithms\nmodels = []\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('LDA', LinearDiscriminantAnalysis()))\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('CART', DecisionTreeClassifier()))\nmodels.append(('NB', GaussianNB()))\nmodels.append(('SVM', SVC()))\n# evaluate each model in turn\nresults = []\nnames = []\nfor name, model in models:\n\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n\tcv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n\tresults.append(cv_results)\n\tnames.append(name)\n\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n\tprint(msg)     \n'''\n\n\nspec_freqs, spec_t, spec_PSDperHz = draw_specgram(data_eo, sampling_freq)\n_,_, spec_PSDperHz_ec = draw_specgram(data_ec, sampling_freq)\n\nEEGtimes = np.array([4 * t for t in range(len(data_eo))])\nfreqs, eegpower_eo = wavelet_spectrogram(data_eo, EEGtimes)\nEEGtimes = np.array([4 * t for t in range(len(data_ec))])\nfreqs, eegpower_ec = wavelet_spectrogram(data_ec, EEGtimes)\n\nplt.figure()\nfor slice_ in eegpower_eo.T[1::1000]:\n    plt.plot(freqs, slice_)\n\n\n\nfig=plt.figure(figsize=(6,6))\nax = fig.add_subplot(111, projection='3d')\nax.scatter(ec_features[:,1],ec_features[:,2],ec_features[:,3])\nax.scatter(eo_features[:,1],eo_features[:,2],eo_features[:,3])\nax.view_init(20, 20)\nax.set_xlabel('theta')\nax.set_ylabel('alpha')\nax.set_zlabel('beta')\nplt.title(\"\")\n#plt.scatter(ec_features[:,1],ec_features[:,2])\n#plt.scatter(eo_features[:,1],eo_features[:,2])\n\n","repo_name":"io0/NeuroTech-Workshop-Demo","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":8296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1664703092","text":"import random\n\nfrom telegram import Update, ParseMode\nfrom telegram.ext import CallbackContext, PrefixHandler, Filters\nfrom telegram.utils.helpers import escape_markdown\nfrom telegram.error import BadRequest, Unauthorized\n\nfrom .. import DB_URL, dispatcher, OWNER_ID, GROUP_ID\nfrom ..utils import get_id, get_name, group_id, delete, owner, add_help\nfrom ..slap_msgs import *\n\n\n@group_id\ndef slap(update: Update, context: CallbackContext):\n    user = update.effective_user\n    message = update.effective_message\n    chat = update.effective_chat\n    bot = context.bot\n\n    user1 = f\"[{user.full_name}](tg://user?id={user.id})\"\n    user2 = None\n    self = False\n    basic = False\n\n    if message.reply_to_message:\n\n        if message.reply_to_message.from_user.id == bot.id:\n            message.reply_text(\"Nah.\")\n            return\n\n        if user.id == message.reply_to_message.from_user.id:\n            self = True\n        else:\n            user2 = f\"[{message.reply_to_message.from_user.full_name}](tg://user?id={message.reply_to_message.from_user.id})\"\n\n        reply_msg = message.reply_to_message.message_id\n    else:\n\n        try:\n            user2 = message.parse_entity(message.entities[1])[1:]\n        except IndexError:\n            basic = True\n        else:\n\n            temp = user2.lower()\n\n            if temp == \"admin\" or \"/\" in temp:\n                delete(message)\n                return\n            if temp == bot.username.lower():\n                message.reply_text(\"Nah.\")\n                return\n            if temp == user.username.lower():\n                self = True\n            else:\n\n                if DB_URL:\n                    try:\n                        user2 = f\"[{get_name(user2)}](tg://user?id={get_id(user2)})\"\n                    except KeyError:\n                        user2 = \"@\" + escape_markdown(user2)\n                else:\n                    user2 = \"@\" + escape_markdown(user2)\n\n        reply_msg = message.message_id\n\n    if self:\n        temp = random.choice(SLAP_SELF)\n    elif basic:\n        temp = random.choice(SLAP_BASIC)\n    else:\n        temp = random.choice(SLAP_TEMPLATES)\n\n    item = random.choice(ITEMS)\n    hit = random.choice(HIT)\n    throw = random.choice(THROW)\n\n    chat.send_message(\n        temp.format(user1=user1, user2=user2, item=item, hits=hit, throws=throw),\n        ParseMode.MARKDOWN,\n        reply_to_message_id=reply_msg,\n    )\n\n\n@group_id\ndef runs(update: Update, _):\n    update.effective_message.reply_text(random.choice(RUN_STRINGS))\n\n\n@owner\ndef send(update: Update, context: CallbackContext):\n    message = update.effective_message\n    bot = context.bot\n    args = context.args\n\n    cmd = message.text.split()[0]\n\n    if cmd == \"#send\":\n        try:\n            chat_ids = [int(args[0])]\n        except ValueError:\n            message.reply_text(\"Invalid chat ID.\")\n            return\n        except IndexError:\n            message.reply_text(\"Please specify a chat ID.\")\n            return\n    elif GROUP_ID:\n        chat_ids = GROUP_ID\n    else:\n        message.reply_text(\"Env variable GROUP_ID not specified.\")\n        return\n\n    if cmd == \"#send\":\n        split_num = 2\n    else:\n        split_num = 1\n\n    try:\n        text = message.text.split(\" \", split_num)[split_num]\n    except IndexError:\n        message.reply_text(\"Please specify some text.\")\n        return\n\n    try:\n        for id in chat_ids:\n            bot.send_message(id, text)\n    except BadRequest:\n        message.reply_text(\"Couldn't send all messages.\")\n    except Unauthorized:\n        message.reply_text(\n            \"Request unauthorized. Are you trying to send a message to a bot?\"\n        )\n    else:\n        message.reply_text(\"Messages sent!\")\n\n\nSLAP_HANDLER = PrefixHandler(\"#\", \"slap\", slap, Filters.chat_type.groups, run_async=True)\nRUNS_HANDLER = PrefixHandler(\"#\", \"runs\", runs, Filters.chat_type.groups, run_async=True)\nSEND_HANDLER = PrefixHandler(\"#\", [\"send\", \"sendall\"], send, Filters.chat_type.private, run_async=True)\n\ndispatcher.add_handler(SLAP_HANDLER)\ndispatcher.add_handler(RUNS_HANDLER)\ndispatcher.add_handler(SEND_HANDLER)\n\nadd_help(\"slap\", \"Slaps a user.\")\nadd_help(\"runs\", \"Why are you running?\")\nadd_help(\n    \"send/sendall\", \"Sends a message to specified groups or users.\", owner_only=True\n)\n\n","repo_name":"uhwot/telegram-remix-bot","sub_path":"remix_bot/modules/fun.py","file_name":"fun.py","file_ext":"py","file_size_in_byte":4281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72402032248","text":"# 1이 될 때까지\n# 어떤 수 N이 1이 될 때 까지 다음의 두 과정 중 하나를 반복적으로 선택하여 수행\n# 1. N에서 1을 뺀다\n# 2. N을 K로 나눈다. (N이 K로 나누어질때만 가능)\n\nimport sys\n\nN, K = map(int, sys.stdin.readline().split())\ncount = 0\n\nwhile N != 1:\n    if N % K == 0:\n        N /= K\n    else:\n        N -= 1\n    \n    count += 1\n\nprint(count)","repo_name":"kybeen/CodingTest-Study","sub_path":"Algorithm/Greedy/greedy_3.py","file_name":"greedy_3.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35572976227","text":"import _init_paths\nfrom core.evaluate import AverageMeter\nimport numpy as np\nimport torch\nimport time\nimport clip\n\n\ndef valid_model(dataLoader, model, criterion_img, criterion_txt, cfg, device):\n    model.eval()\n    all_loss = AverageMeter()\n    \n    with torch.no_grad():\n        for i, (image, caption) in enumerate(dataLoader):\n            images = torch.stack([img for img in image], dim=0).to(device)\n            captions = clip.tokenize(caption).to(device)\n\n            logits_per_image, logits_per_text = model(images, captions)\n            logits_per_image *= (np.exp(0.01) / np.exp(0.07))\n            logits_per_text *= (np.exp(0.01) / np.exp(0.07))\n\n            ground_truth = torch.arange(cfg.TEST.BATCH_SIZE, dtype=torch.long, device=device)\n            total_loss = (criterion_img(logits_per_image, ground_truth) + criterion_txt(logits_per_text, ground_truth)) / 2\n            all_loss.update(total_loss.data.item(), len(caption))\n\n        return all_loss.avg\n","repo_name":"sarahESL/PubMedCLIP","sub_path":"PubMedCLIP/lib/core/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":97,"dataset":"github-code","pt":"77"}
+{"seq_id":"31602861792","text":"from typing import List\n\nclass Solution:\n    def findCircleNum(self, isConnected: List[List[int]]) -> int:\n        # dfs for every node\n        # adjacency matrix\n        n = len(isConnected)\n        searched = [0] * n\n        searched[0] = 0\n        \n\n\n        def dfs(node):\n            if searched[node]:\n                return\n\n            searched[node] = 1\n\n            for i in range(n):\n                if isConnected[node][i]:\n                    dfs(i)\n\n        prov = 0\n\n        for i in range(n):\n            if searched[i]:\n                continue\n            else:\n                dfs(i)\n                prov += 1\n\n        return prov","repo_name":"yellowdragoon/Leetcode-Solutions","sub_path":"547_number_of_provinces.py","file_name":"547_number_of_provinces.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39706277019","text":"import traceback\nimport sys\n\n\ndef handle_error(f):\n    \"\"\"\n    예외를 출력하고 더 이상의 예외 전파는 막는다.\n    :param f: 인자 없는 함수.\n    :type f: Callable[[]]\n    \"\"\"\n    try:\n        f()\n    except Exception:\n        print(traceback.format_exc(), file=sys.stderr)\n","repo_name":"helloworldpark/matjip","sub_path":"utils/errorhandler.py","file_name":"errorhandler.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"48828839046","text":"\nfrom collections import OrderedDict\nimport sys\nimport csv\nimport datetime\nimport os\n\nfrom peewee import *\n\n\ndb = SqliteDatabase('inventory.db')\n\n\nclass Product(Model):\n    product_id = PrimaryKeyField()\n    product_name = CharField(max_length=100, unique=True)\n    product_price = IntegerField()\n    product_quantity = IntegerField()\n    date_updated = DateTimeField(default=datetime.datetime.now)\n\n    class Meta:\n        database = db\n\n\ndef initialize():\n    db.connect()\n    db.create_tables([Product], safe=True)\n    db.close\n\n\ndef csv_data():\n# Reads, cleans and installs the csv data into a list.\n# Learned how to change dollars to cents from this site: https://codereview.stackexchange.com/questions/121074/safely-convert-dollars-to-cents/121077\n# Reminded me about how to strip the $: https://stackoverflow.com/questions/3887469/python-how-to-convert-currency-to-decimal\n    with open('inventory.csv') as csvfile:\n        store_list = csv.DictReader(csvfile, delimiter=',')\n        rows = list(store_list)\n        for row in rows:\n            row['product_name'] = row['product_name']\n            row['product_price'] = float(row['product_price'].replace('$','')) * 100\n            row['product_quantity'] = int(row['product_quantity'])\n            row['date_updated'] = datetime.datetime.strptime(row['date_updated'], '%m/%d/%Y')\n            try:\n                Product.create(\n                product_name=row['product_name'], \n                product_price=row['product_price'], \n                product_quantity=row['product_quantity'], \n                date_updated=row['date_updated']\n                ).save()\n\n            except IntegrityError:\n                product_list = Product.get(product_name=row['product_name'])\n\n                if product_list.date_updated <= row['date_updated']:\n                    product_list.product_price = row['product_price']\n                    product_list.product_quantity = row['product_quantity']\n                    product_list.date_updated = row['date_updated']\n                    product_list.save()\n\n\ndef clear():\n    os.system('cls' if os.name == 'nt' else 'clear')\n\n    \ndef view_menu():\n# Using OrderedDict to create, view and access the inventory's menu.\n    result = None    \n    while True:\n        print('\\n','=== MENU OPTIONS ===','\\n')\n        for key,value in menu.items():\n            print('{}) {}'.format(key, value.__doc__))           \n        result = input('Enter your choice here:  ').lower().strip()\n        print('\\n')\n        \n        if result in menu:\n            clear()\n            menu[result]()\n\n        elif result not in menu:\n            print('\\n','Please try an option from the menu.')\n   \n\ndef display_products():\n    \"\"\"Display inventory items.\"\"\" \n# Displays the inventory 1 product at a time.\n    product_entry = Product.select().order_by(Product.product_id.desc())               \n    for product in product_entry:\n        clear()\n        print('\\n')\n        print(' ID:', product.product_id, '\\n',\n        'Product:', product.product_name,'\\n',\n        'Price: $', product.product_price/100, '\\n',\n        'Quantity:', product.product_quantity, '\\n',\n        'Last updated:', product.date_updated.strftime('%m/%d/%Y'),\n        '\\n', '\\n')       \n       \n        print('q) Return to main menu.')\n        print('d) Delete product')\n        print('Or press enter to see the next product.','\\n')  \n    \n        choice = input('Enter your choice here:  ').lower().strip()\n        print('\\n')\n        if choice == 'q':\n            clear()\n            break\n        elif choice == 'd':\n            delete_product(product)\n\n\ndef delete_product(product):\n    if input('Delete product? (y/n)  ').lower() == 'y':\n        Product.delete_instance(product)\n        print('Product deleted.')\n\n\ndef search_product():\n    \"\"\"Search for product by ID#.\"\"\"   \n# Allows user to search products by the inventory id#.\n# Recieved assistance from Jennifer Nordell to use and utilize the get_by_id function. \n    while True:\n        try:\n            search = int(input('Enter the product ID you wish to search for:  ')) \n        except ValueError:\n            print('Invalid character, please try again.')           \n        else:\n            try:\n                item = Product.get_by_id(search)\n            except Product.DoesNotExist:   \n                print('Invalid ID, please try again.')\n            else:\n                clear()\n                print('\\n')\n                print(' ID:', item.product_id, '\\n',\n                'Product:', item.product_name,'\\n',\n                'Price: $', item.product_price/100, '\\n',\n                'Quantity:', item.product_quantity, '\\n',\n                'Last updated:', item.date_updated.strftime('%m/%d/%Y'),\n                '\\n', '\\n') \n                break\n                                       \n\ndef new_product_name():\n    new = Product()\n    while True:\n        try:    \n            new.name = input('Enter product name:  ')\n\n        except ValueError:\n            print('Invalid character, please try again.')\n            continue\n        else:\n            break\n    return new.name\n\n\ndef new_product_price():\n    new = Product()\n    while True:\n        try:\n            new.price = float(input('Enter product price:$  '))\n            new.price = int(new.price * 100)\n        \n        except ValueError:\n            print('Invalid character, please try again.')\n            continue\n        else:\n            break\n    return new.price\n\n\ndef new_product_quantity():\n    new = Product()\n    while True:\n        try:\n            new.quantity = int(input('Enter product quantity:  '))\n\n        except ValueError:\n            print('Invalid character, please try again.')\n            continue\n        else:\n            break\n    return new.quantity\n\n\ndef add_product():\n    \"\"\"Add a product to inventory.\"\"\"\n# Collects data from the users and inputs a new product into the inventory.   \n    new_name = new_product_name()\n    new_price = new_product_price()\n    new_quantity = new_product_quantity()\n        \n    if input('Would you like to save this product? (y/n)  ').lower() != 'n': \n        try:\n            Product.create(\n            product_name = new_name, \n            product_price = new_price, \n            product_quantity = new_quantity).save()\n            print('Product saved.')\n\n        except IntegrityError:\n            product_list = Product.get(product_name=new_name)\n            product_list.product_name = new_name\n            product_list.product_price = new_price\n            product_list.product_quantity = new_quantity        \n            product_list.save()\n\n\ndef backup_csv():\n    \"\"\"Backup inventory.\"\"\" \n# Creates a backup inventory.\n    \n    with open('backup.csv', 'w', newline='') as csvfile:\n        fieldnames = ['product_name', 'product_price', 'product_quantity', 'date_updated']\n        inventorywriter = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n        inventorywriter.writeheader() \n        backup = Product.select().order_by(Product.product_id.asc())            \n        for product in backup:\n            inventorywriter.writerow({\n            'product_name': product.product_name, \n            'product_price': '$' + '{}'.format(product.product_price / 100), \n            'product_quantity': product.product_quantity,     \n            'date_updated': product.date_updated.strftime('%m/%d/%Y')}) \n        print('Successfully updated!')   \n\n\ndef quit_inventory():\n    \"\"\"Quit.\"\"\"\n    sys.exit('Have a great day!')\n\n\nmenu = OrderedDict([\n    ('v', search_product),\n    ('d', display_products),\n    ('a', add_product),\n    ('b', backup_csv),\n    ('q', quit_inventory)\n    ])\n\n\nif __name__ == '__main__':\n    initialize()\n    csv_data()\n    view_menu()\n","repo_name":"daniellerg/CSV_Inventory_App","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23796667442","text":"#coding:utf-8\n#ファイルの読み込みと生成\n#xyのファイル書き込み\nimport numpy as np\n\n\ndef main():\n    # ファイルの書き込み\n    a = np.array([[1.0] * 2] * 20)\n    j = 0  # カウント用\n    text = \"person0/xy_0.txt\"\n    f = open(text)  # ファイルの読み込み\n    alldata = f.read()  # 全部読み込む\n    f.close()\n    lines = alldata.split('\\n')  # 改行で区切る\n    for line in lines:  # 1行\n        linedata = line.split(',')\n        line_x = linedata[0]  # 各行のx座標 str\n        line_y = linedata[1]  # 各行のy座標\n        a[j][0] = float(line_x)\n        a[j][1] = float(line_y)\n        j += 1\n    print(a)\n    for i in range(1, 200, 1):\n        number = i\n        filename = \"person0/xy_%d.txt\" % number\n        with open(filename, mode='w') as file:  # 書き込み\n            for j in range(20):\n                x = a[j][0] + np.random.normal(loc=0.0, scale=2)  # 前のx\n                y = a[j][1] + np.random.normal(loc=0.0, scale=2)  # 前のy\n                if (j < 19):\n                    file.write(str(x) + ',')\n                    file.write(str(y) + '\\n')\n                else:\n                    file.write(str(x) + ',')\n                    file.write(str(y))\n\n\nif __name__ == '__main__':\n    main()","repo_name":"kbmika27/LSTM","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38388437831","text":"# step 1\nimport random\n\nword_list = [\"ardvark\", \"baboom\", \"camel\"]\n\nchosen_word = random.choice(word_list)\n\nprint(chosen_word)\n\nguess = (input(\"Guess a letter\")).lower()\n\n\nfor letter in chosen_word:\n    if letter == guess:\n        print(guess)\n    else:\n        print(\"wrong\")\n","repo_name":"negaryuki/100DaysofCode","sub_path":"Day07/Hangman01.py","file_name":"Hangman01.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"11757330229","text":"import tkinter\r\nfrom converter import converter\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nimport re\r\nfrom tkinter import messagebox\r\n\r\nclass ui(converter):\r\n    def __init__(self) -> None:\r\n        super().__init__()\r\n        self.window=Tk()\r\n        self.window.title('Currency Converter')\r\n    def create_label(self):\r\n        self.label_header=Label(self.window,text='Foreign Currenciy Exchange Rate Against BDT',font=(\"Arial\", 25))\r\n        self.label_header.grid(row=1,column=1,padx=50,pady=10,rowspan=1,columnspan=1)\r\n        self.label_us=Label(self.window,text='US Dollar',font=(\"Arial\", 15))\r\n        self.label_us.grid(row=2,column=1,pady=10,padx=50,rowspan=1,columnspan=1)\r\n        self.label_br=Label(self.window,text='British Pound',font=(\"Arial\", 15))\r\n        self.label_br.grid(row=4,column=1,pady=20,padx=50,rowspan=1,columnspan=1)\r\n        self.label_eu=Label(self.window,text='EU Euro',font=(\"Arial\", 15))\r\n        self.label_eu.grid(row=6,column=1,pady=20,padx=50,rowspan=1,columnspan=1)\r\n        self.label_x=Label(self.window,text='Convert From',font=(\"Arial\", 15))\r\n        self.label_x.grid(row=9,column=0,pady=25,padx=15)\r\n        self.label_y=Label(self.window,text='Convert To',font=(\"Arial\", 15))\r\n        self.label_y.grid(row=9,column=2,padx=15,pady=20)\r\n        self.amount=Label(self.window,text=\"Amount\",font=(\"Arial\", 15))\r\n        self.amount.grid(row=10,column=0,columnspan=1)\r\n        self.conv=Label(self.window,text='Converted Amount',font=(\"Arial\", 15))\r\n        self.conv.grid(row=11,column=2,columnspan=1)\r\n\r\n        self.text_us=StringVar()\r\n        self.tk_us=Label(self.window,textvariable=self.text_us,bg='green',font=(\"Arial\", 15))\r\n        self.tk_us.grid(row=3,column=1,pady=1,columnspan=1,rowspan=1)\r\n\r\n        self.text_br=StringVar()\r\n        self.tk_br=Label(self.window,textvariable=self.text_br,bg='green',font=(\"Arial\", 15))\r\n        self.tk_br.grid(row=5,column=1,pady=1,columnspan=1,rowspan=1)\r\n\r\n        self.text_eu=StringVar()\r\n        self.tk_eu=Label(self.window,textvariable=self.text_eu,bg='green',font=(\"Arial\", 15))\r\n        self.tk_eu.grid(row=7,column=1,pady=1,columnspan=1,rowspan=1)\r\n    def callback(self,P):\r\n        if str.isdigit(P) or P == \"\":\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def convert_sec(self):\r\n        self.text_from=StringVar(self.window)\r\n        self.from_box=ttk.Combobox(self.window,values=self.keys,textvariable=self.text_from,width=25,height=8)\r\n        self.from_box.grid(row=10,column=0,pady=20,padx=10)\r\n\r\n        vcmd = (self.window.register(self.callback))\r\n\r\n        self.num_from=DoubleVar(self.window)\r\n        self.e_from=Entry(self.window,width=30,validatecommand=(vcmd,'%P'))\r\n        self.e_from.grid(row=12,column=0,pady=20,padx=5)\r\n\r\n        self.text_to=StringVar(self.window)\r\n        self.to_box=ttk.Combobox(self.window,values=self.keys,textvariable=self.text_to,width=12,height=8)\r\n        self.to_box.grid(row=10,column=2,pady=20,padx=5,columnspan=1)\r\n\r\n        self.num_to=DoubleVar(self.window)\r\n        self.e_to=Label(self.window,font=(\"Arial\", 25),textvariable=self.num_to,bg='green')\r\n        self.e_to.grid(row=12,column=2,pady=20,padx=10)\r\n    def change(self):\r\n        if self.text_from.get()!='' and self.text_to.get()!='':\r\n            if self.e_from.get():\r\n                self.temp_1=self.e_from.get()\r\n                if re.search(\"[+-]?[0-9]+(\\\\.[0-9]+)?([Ee][+-]?[0-9]+)?\",self.temp_1):\r\n                    self.value=float(self.temp_1)\r\n                    self.num_to.set(self.convert(self.text_from.get(),self.text_to.get(),self.value))\r\n                else:\r\n                    self.e_from.delete(0,END)\r\n                    messagebox.showerror('Error','Input Must be Number')\r\n            else:\r\n                messagebox.showerror('Error',\"Input is Empty\")\r\n        else:\r\n            messagebox.showerror('Error','Both currency Type must be choosen')\r\n            self.e_from.delete(0,END)\r\n    def clear(self):\r\n        self.e_from.delete(0,END)\r\n        self.from_box.delete(0,END)\r\n        self.to_box.delete(0,END)\r\n        #self.e_to.delete(0,END)\r\n\r\n        self.num_to.set(0.0)\r\n        self.text_to.set('')\r\n        self.text_from.set('')\r\n        self.num_from.set(0.0)                \r\n\r\n    def value_setter_label(self):\r\n        self.text_us.set(str(self.convert('USD','BDT')))\r\n        self.text_br.set(str(self.convert('GBP','BDT')))\r\n        self.text_eu.set(str(self.convert('EUR','BDT')))\r\n\r\n    def create_window(self):\r\n        self.create_label()\r\n        self.value_setter_label()\r\n        self.convert_sec()\r\n\r\n        self.b_act=Button(self.window,text='Convert',font=(\"Arial\", 15),command=self.change)\r\n        self.b_act.grid(row=13,column=1)\r\n        self.b_clear=Button(self.window,text='Clear',font=('Arial',15),command=self.clear)\r\n        self.b_clear.grid(row=13,column=2)\r\n    \r\nob2=ui()\r\nob2.create_window()\r\nob2.window.mainloop()\r\n        ","repo_name":"farhan1503001/Python-Projects","sub_path":"Currency Converter/UI.py","file_name":"UI.py","file_ext":"py","file_size_in_byte":4961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44531504815","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('auth/login/', views.LoginView.as_view(), name='auth-login'),\n    path('council/list/', views.ListCouncilView.as_view(), name='council-all'),\n    path('apartment/list/', views.ListApartmentAPIView.as_view(), name='apartment-all'),\n    path('aviarace/list/', views.ListAviaraceView.as_view(), name='aviarace-all'),\n    path('feedback/create/', views.FeedbackCreateView.as_view(), name='feedback-create'),\n    path('fcmdevice/create/', views.FcmGetDeviceToken.as_view(), name='fcmid-create'),\n    path('stream/list/', views.ListStreamView.as_view(), name='stream-list'),\n    path('book/list/', views.ListStreamView.as_view(), name='book-list'),\n]\n","repo_name":"shadowwa1k3r/ewp-web","sub_path":"ewp_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18093383198","text":"import fauxfactory\nimport pytest\nfrom widgetastic.utils import partial_match\n\nfrom cfme import test_requirements\nfrom cfme.fixtures.provider import setup_or_skip\nfrom cfme.infrastructure.provider.rhevm import RHEVMProvider\nfrom cfme.infrastructure.provider.virtualcenter import VMwareProvider\nfrom cfme.infrastructure.pxe import get_template_from_config\nfrom cfme.infrastructure.pxe import ISODatastore\nfrom cfme.markers.env_markers.provider import ONE\nfrom cfme.markers.env_markers.provider import SECOND\nfrom cfme.provisioning import do_vm_provisioning\nfrom cfme.utils.appliance.implementations.ui import navigate_to\nfrom cfme.utils.blockers import BZ\nfrom cfme.utils.conf import cfme_data\nfrom cfme.utils.version import Version\n\npytestmark = [\n    pytest.mark.meta(server_roles=\"+automate\"),\n    pytest.mark.usefixtures('uses_infra_providers', 'provider', 'setup_provider'),\n    pytest.mark.provider(\n        [RHEVMProvider, VMwareProvider],\n        required_fields=[\n            ('iso_datastore', True),\n            ['provisioning', 'host'],\n            ['provisioning', 'datastore'],\n            ['provisioning', 'iso_template'],\n            ['provisioning', 'iso_file'],\n            ['provisioning', 'iso_kickstart'],\n            ['provisioning', 'iso_root_password'],\n            ['provisioning', 'iso_image_type'],\n            ['provisioning', 'vlan'],\n        ],\n        scope=\"module\"),\n    pytest.mark.uncollectif(\n        lambda provider:\n        cfme_data.management_systems.get(provider.key, {}).get('provisioning', {}).get(\n            'iso_kickstart') not in cfme_data.get(\n            'customization_templates', {}).keys(),\n        reason=\"Uncollected if custom template not present\"\n    ),\n    pytest.mark.tier(2)\n]\n\n\n@pytest.fixture\ndef iso_cust_template(provider, appliance):\n    iso_cust_template = provider.data['provisioning']['iso_kickstart']\n    return get_template_from_config(iso_cust_template, create=True, appliance=appliance)\n\n\n@pytest.fixture\ndef iso_datastore(provider, appliance):\n    return ISODatastore(provider.name, appliance=appliance)\n\n\n@pytest.fixture\ndef datastore_init(iso_cust_template, iso_datastore, provisioning, setup_provider, appliance):\n    if not iso_datastore.exists():\n        iso_datastore.create()\n    iso_image_type = appliance.collections.system_image_types.instantiate(\n        name=provisioning['iso_image_type'])\n    iso_image = appliance.collections.system_images.instantiate(\n        name=provisioning['iso_file'], image_type=iso_image_type, datastore=iso_datastore)\n    iso_image.set_image_type()\n\n\n@pytest.fixture(scope=\"function\")\ndef vm_name():\n    vm_name = fauxfactory.gen_alphanumeric(20, start=\"test_iso_prov_\")\n    return vm_name\n\n\n@pytest.mark.meta(blockers=[BZ(1888748, forced_streams=[\"5.11\", \"5.10\"],\n                            unblock=lambda provider: provider.version != Version(\"4.4\"))])\n@pytest.mark.tier(2)\n@test_requirements.provision\ndef test_iso_provision_from_template(appliance, provider, vm_name, datastore_init, request):\n    \"\"\"Tests ISO provisioning\n\n    Metadata:\n        test_flag: iso, provision\n        suite: infra_provisioning\n\n    Polarion:\n        assignee: jhenner\n        caseimportance: high\n        casecomponent: Provisioning\n        initialEstimate: 1/4h\n    \"\"\"\n    # generate_tests makes sure these have values\n    (iso_template,\n     host,\n     datastore,\n     iso_file,\n     iso_kickstart,\n     iso_root_password,\n     iso_image_type,\n     vlan,\n     addr_mode) = tuple(map(provider.data['provisioning'].get,\n                            ('pxe_template',\n                             'host',\n                             'datastore',\n                             'iso_file',\n                             'iso_kickstart',\n                             'iso_root_password',\n                             'iso_image_type',\n                             'vlan',\n                             'iso_address_mode')))\n\n    request.addfinalizer(lambda:\n                         appliance.collections.infra_vms.instantiate(vm_name, provider)\n                         .cleanup_on_provider())\n\n    provisioning_data = {\n        'catalog': {\n            'vm_name': vm_name,\n            'provision_type': 'ISO',\n            'iso_file': {'name': iso_file}},\n        'environment': {\n            'host_name': {'name': host},\n            'datastore_name': {'name': datastore}},\n        'customize': {\n            'custom_template': {'name': iso_kickstart},\n            'root_password': iso_root_password,\n            'address_mode': addr_mode},\n        'network': {\n            'vlan': partial_match(vlan)},\n        'schedule': {\n            'power_on': False}}\n    do_vm_provisioning(appliance, iso_template, provider, vm_name, provisioning_data, request,\n                       num_sec=1800)\n\n\n@pytest.mark.provider([RHEVMProvider], selector=ONE,\n                      required_fields=[('iso_datastore', True)])\n@pytest.mark.provider([RHEVMProvider], selector=SECOND, fixture_name=\"second_provider\",\n                      scope=\"module\")\ndef test_add_multiple_iso_datastore(appliance, second_provider, datastore_init,\n                                    iso_datastore, request):\n    \"\"\"Tests ISO provisioning with multiple instances\n\n    Metadata:\n        test_flag: iso, provision\n        suite: infra_provisioning\n\n    Polarion:\n        assignee: jhenner\n        caseimportance: high\n        casecomponent: Provisioning\n        initialEstimate: 1/4h\n    \"\"\"\n\n    # setup second provider and assert button state\n    setup_or_skip(request, second_provider)\n    view = navigate_to(iso_datastore, 'All')\n    assert view.toolbar.configuration.item_enabled(\"Add a New ISO Datastore\")\n","repo_name":"ManageIQ/integration_tests","sub_path":"cfme/tests/infrastructure/test_iso_provisioning.py","file_name":"test_iso_provisioning.py","file_ext":"py","file_size_in_byte":5668,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"77"}
+{"seq_id":"40931050106","text":"import os\nimport tornado.web\nimport tornado.locale\nimport uimodules\nimport datetime,time\nimport logging\n\n__all__ = [\t\"tornadobb_handlers\",\n\t\t\t\"tornadobb_settings\",\n\t\t\t]\n\n###########################################\n#\n#\tYou can change below\n#\n###########################################\n# Forum title\nFORUM_TITLE = \"TornadoBB Forum\"\t\n\n# Forum sub title\nFORUM_SUB_TITLE = \"A Simple & Fast Forum based on Tornodo written by Python and MongoDB\"\n\n#html page template\nFORUM_TEMPLATE = \"fluxbb\"\n\n# selected default style\n# required, css file name without extend file name\nFORUM_STYLE = \"Air\"\n\n#root url\nTORNADOBB_ROOT_URL = r\"/tornadobb\"\n\n###########################################\n#\n#\tsetup administrator\n#\n###########################################\n\n# admin url, access it with TORNADOBB_ROOT_URL/TORNADOBB_ADMIN_URL\n# You can write a very complicated url for security, such as: HQr7$OP, whatever\nTORNADOBB_ADMIN_URL = r\"/admin\"\n\nTORNADOBB_FIRST_ADMIN_NAME=\"admin\"\nTORNADOBB_FIRST_ADMIN_PASSWORD=\"admin\"\nTORNADOBB_FIRST_ADMIN_EMAIL=\"songdi19@gmail.com\"\n\n# expired time for session\nTORNADOBB_SESSION_EXPIRE = 30 #mins\n\n# default time zone\nTORNADOBB_TIME_ZONE=\"Australia/Sydney\"\n\n#TORNADOBB_TIME_ZONE=\"Asia/Shanghai\"\n\n###########################################################\n#\n# setup database\n#\n###########################################################\n\n# for mongodb\nDATABASE_SETTINGS = {\n\t\t\"engine\": \"db_backends.mongodb\", # name of monogdb moduel\n        \"host\": \"localhost\",             # Set host address\n        \"port\": 27017,                   # Set port\n        \"data_file\":\"db_tornadobb\",\t\t # Set database file\n        \"safe_mode\": True,\t\t\t\t # True is to run as safe mode\n        \"last_error_options\":{\"j\":False,\"w\":1,\"wtimeout\":5000,\"fsync\":False}, #if safe_mode is True, mongodb will use this as last_error_options\n}\n\n# for sqlit3, NO IMPLEMENT yet\n#DATABASE_SETTINGS = {\n#\t\t\"engine\": \"db_backends.sqlite3\",\n#        \"data_file\":\"db_tornadobb\",\t\t # Set database file for mongodb\n#}\n\n\n###########################################################\n#\n#\tSMTP settings\n#\n###########################################################\n\"\"\"SMTP_SETTINGS = {\n\n\t\t\t\t\t\"server\":\"smtp.gmail.com\",\n\t\t\t\t\t\"use_authentication\": True,\n\t\t\t\t\t\"tls_port\": 587,\n\t\t\t\t\t\"ssl_port\": 465,\n\t\t\t\t\t\"port\":None,\n\t\t\t\t\t\"username\":\"tornadobb1@gmail.com\",\n\t\t\t\t\t\"password\":\"4nE7dbMk\",\n\t\t\t\t\t\"email_address\":\"tornadobb1@gmail.com\",\n\t\t\t\t}\n\"\"\"\t\t\t\t\nSMTP_SETTINGS = {\n\n\t\t\t\t\t\"server\":\"smtp.gmail.com\",\n\t\t\t\t\t\"use_authentication\": True,\n\t\t\t\t\t\"tls_port\": 587,\n\t\t\t\t\t\"ssl_port\": 465,\n\t\t\t\t\t\"port\":None,\n\t\t\t\t\t\"username\":\"auxpage@gmail.com\",\n\t\t\t\t\t\"password\":\"pZ8bo0vC1\",\n\t\t\t\t\t\"email_address\":\"auxpage@gmail.com\",\n\t\t\t\t}\n\n###########################################################\n#\n#\tDo NOT change below, ONLY IF YOU KNOW WHAT YOU ARE DOING\n#\n###########################################################\n\nversion=\"0.1\"\n\n###########################################################\n#\n#\tAdd current path into system path\n#\n###########################################################\nTORNADOBB_ROOT_PATH = os.path.dirname(__file__)\nimport sys\nsys.path.append(TORNADOBB_ROOT_PATH)\n\n##########################################################\n#\n#\tForum keywords\n#\n###########################################################\n\nFORUM_KEYWORDS = \",\".join([w.strip() for w in open(os.path.join(TORNADOBB_ROOT_PATH,\"keywords.txt\"))])\n\n##########################################################\n#\n#\tDatabase backends\n#\n###########################################################\n\n_db_backends_name = DATABASE_SETTINGS[\"engine\"]\n_class_name = _db_backends_name.split('.')[1]\n_db_backends_model =__import__(_db_backends_name,globals(),locals(),[_class_name],-1)\ndb_backend = getattr(_db_backends_model,_class_name)(DATABASE_SETTINGS)\n\n###########################################################\n#\n#\tcreate the first admin account\n#\n###########################################################\nimport hashlib\nm = hashlib.md5()\nm.update(TORNADOBB_FIRST_ADMIN_PASSWORD)\npassowrd = m.hexdigest().upper()\ndb_backend.do_create_first_admin(TORNADOBB_FIRST_ADMIN_NAME,passowrd,TORNADOBB_FIRST_ADMIN_EMAIL,time.time())\n\n###########################################################\n#\n#\tTornadobb default timezone\n#\n###########################################################\ntry:\n\tfrom pytz import common_timezones\n\tfrom pytz import timezone\nexcept ImportError:\n\n    raise ImportError\n\n_tornadobb_timezone = timezone(TORNADOBB_TIME_ZONE)\n\n###########################################################\n#\n#\texpired_tatal_seconds\n#\n###########################################################\n\n#for python 2.7.2\n#_expired_total_seconds = datetime.timedelta(minutes=settings.tornadobb_default_settings[\"session_expire\"]).total_seconds()\n#for blow python 2.7.2\ntd = datetime.timedelta(minutes=TORNADOBB_SESSION_EXPIRE)\n_expired_total_seconds = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10**6\n###########################################################\n#\n#\tLogging\n#\n###########################################################\t\n\n#logging.basicConfig(filename=os.path.join(TORNADOBB_ROOT_PATH, \"tornadobb.log\"),level=logging.DEBUG,format='%(asctime)s %(message)s')\n\n###########################################################\n#\n#\ti18n support\n#\n###########################################################\n\ntornado.locale.load_translations(os.path.join(TORNADOBB_ROOT_PATH, \"i18n\"))\n\n###########################################################\n#\n#\tTornadobb itself settings\n#\n###########################################################\n\n_emoticon_1_settings = {\n\t\t\":D\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-happy.png\",\n\t\t\":(\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-unhappy.png\",\n\t\t\":o\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-surprised.png\",\n\t\t\":p\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-tongue.png\",\n\t\t\";)\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-wink.png\",\n\t\t\":)\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon/emoticon-smile.png\",\n\t\t}\n\t\t\n_emoticon_2_settings = {\n\t\t\"m1\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood1.gif\",\n\t\t\"m2\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood2.gif\",\n\t\t\"m3\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood3.gif\",\n\t\t\"m4\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood4.gif\",\n\t\t\"m5\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood5.gif\",\n\t\t\"m6\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood6.gif\",\n\t\t\"m7\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood7.gif\",\n\t\t\"m8\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood8.gif\",\n\t\t\"m9\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood9.gif\",\n\t\t\"m10\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood10.gif\",\n\t\t\"m11\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood11.gif\",\n\t\t\"m12\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood12.gif\",\n\t\t\"m13\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood13.gif\",\n\t\t\"m14\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood14.gif\",\n\t\t\"m15\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood15.gif\",\n\t\t\"m16\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood16.gif\",\n\t\t\"m17\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood17.gif\",\n\t\t\"m18\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood18.gif\",\n\t\t\"m19\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood19.gif\",\n\t\t\"m20\" : TORNADOBB_ROOT_URL + \"/static/images/emoticon2/mood20.gif\",\n\t\t\n\t\t}\n\n###########################################################\n#\n#\tload all permissions settings\n#\n###########################################################\t\n\n_permission_settings = [\n\t\t\t\t\t\t\"sticky\",\n\t\t\t\t\t\t\"distillate\",\n\t\t\t\t\t\t\"close_topic\",\n\t\t\t\t\t\t\"hide_topic\",\n\t\t\t\t\t\t\"unhide_topic\",\n\t\t\t\t\t\t\"delete_topic\",\n\t\t\t\t\t\t\"move_topic\",\n\t\t\t\t\t\t\"delete_post\",\n\t\t\t\t\t\t\"edit_post\",\n\t\t\t\t\t\t\"highlight\",\n\t\t\t\t\t] \n# for topic type, NOT implement yet\n\"\"\"\n_topic_type_settings = [\n\t\t\t\t\t\t\"text\",\n\t\t\t\t\t\t\"vote\",\n\t\t\t\t\t\t\"picture\",\n\t\t\t\t\t\t\"video\",\n\t\t\t\t\t\t]\n\"\"\"\n###########################################################\n#\n#\tload highlight settings\n#\n###########################################################\t\n_highlight_settings = [\n\t\t\t\t\t\t\"red\",\n\t\t\t\t\t\t\"black\",\n\t\t\t\t\t\t\"underline\",\n\t\t\t\t\t]\n\t\t\t\t\t\n###########################################################\n#\n#\tload css style settings\n#\n###########################################################\t\t\t\t\n\n_css_style_settings = [\n\t\t\t\t\t\t\"Air\",\n\t\t\t\t\t\t\"Cobalt\",\n\t\t\t\t\t\t\"Earth\",\n\t\t\t\t\t\t\"Fire\",\n\t\t\t\t\t\t\"Lithium\",\n\t\t\t\t\t\t\"Mercury\",\n\t\t\t\t\t\t\"Oxygen\",\n\t\t\t\t\t\t\"Radium\",\n\t\t\t\t\t\t\"Sulfur\",\n\t\t\t\t\t\t\"Technetium\",\n\t\t\t\t\t]\n###########################################################\n#\n#\tApplication settings\n#\n###########################################################\n\ntornadobb_settings = {\n\t\t#tornado framework required\n\t\t\"xsrf_cookies\" : True,\n\t\t\"ui_modules\" : uimodules,\n\t\t#\"static_path\" : os.path.join(TORNADOBB_ROOT_PATH, \"static\"),\n\t\t#tornadobb required\n\t\t\"tornadobb.version\":version,\n\t\t\"tornadobb.root_url\": TORNADOBB_ROOT_URL,\n\t\t\"tornadobb.admin_url\": TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL,\n\t\t\"tornadobb.login_url\" : TORNADOBB_ROOT_URL + \"/login\",\n\t\t\"tornadobb.template_path\" : os.path.join(TORNADOBB_ROOT_PATH, \"templates/\" + FORUM_TEMPLATE),\n\t\t\"tornadobb.forum_title\" : FORUM_TITLE,\n\t\t\"tornadobb.forum_sub_title\" : FORUM_SUB_TITLE,\n\t\t\"tornadobb.forum_keywords\" : FORUM_KEYWORDS,\n\t\t\"tornadobb.root_path\" : TORNADOBB_ROOT_PATH,\n\t\t\"tornadobb.timezone_name\":TORNADOBB_TIME_ZONE,\n\t\t\"tornadobb.timezone_obj\" : _tornadobb_timezone,\n\t\t\"tornadobb.session_expire\" : _expired_total_seconds,\n\t\t\"tornadobb.style\" : FORUM_STYLE,\n\t\t\"tornadobb.style_settings\" : _css_style_settings,\n\t\t#\"tornadobb.verify_new_topic\": False, #NOT implement yet\n\t\t#\"tornadobb.topic_default_type\" : \"text\",#NOT implement yet\n\t\t#\"tornadobb.topic_type_settings\": _topic_type_settings,#NOT implement yet\n\t\t\"tornadobb.topic_default_highlight\":\"\",\n\t\t\"tornadobb.emoticon_settings\" : _emoticon_2_settings,\n\t\t\"tornadobb.permission_settings\" : _permission_settings,\n\t\t\"tornadobb.highlight_settings\" : _highlight_settings,\n\t\t#\tcreate a cache for categories and forums\n\t\t\"tornadobb.category_forum\" : db_backend.do_show_all_categories_forums_name_and_id(),\n\t\t\"tornadobb.datetime_format\": \" %Y-%m-%d %H:%M \",\n\t\t\"tornadobb.default_locale\" : \"en-US\",\n\t\t\"tornadobb.smtp_settings\" : SMTP_SETTINGS,\n\t\t\"tornadobb.set_access_log_interval\" : 10,\n\t\t\"tornadobb.pagination_pages_num\" : 5,\n\t\t\"tornadobb.topics_num_per_page\" : 20,\n\t\t\"tornadobb.posts_num_per_page\" : 20,\n\t\t\"tornadobb.distillat_threshold\" : 60, # replies number\n\t\t\"tornadobb.subject_min_chars_num\":1,\n\t\t\"tornadobb.subject_max_chars_num\":100,\n\t\t\"tornadobb.post_min_chars_num\":20,\n\t\t\"tornadobb.post_max_chars_num\":5000,\n\t\t\"tornadobb.signature_max_chars_num\":400,\n\t\t\"tornadobb.username_min_chars_num\" : 2,\n\t\t\"tornadobb.username_max_chars_num\" : 25,\n\t\t\"tornadobb.password_min_chars_num\" : 5,\n\t\t\"tornadobb.password_max_chars_num\" : 25,\n\t\t\"tornadobb.category_min_chars_num\" : 5,\n\t\t\"tornadobb.category_max_chars_num\" : 25,\n\t\t\"tornadobb.forum_min_chars_num\" : 5,\n\t\t\"tornadobb.forum_max_chars_num\" : 20,\n\t\t\"tornadobb.forum_des_min_chars_num\" : 0,\n\t\t\"tornadobb.forum_des_max_chars_num\" : 50,\n        }\n\n###########################################################\n#\n#\tApplication handlers\n#\n###########################################################\n\nfrom user_app import *\nfrom admin_app import *\n\ntornadobb_handlers = [\n\t\t\t#for the tornadobb can share with other tornado app, it can not have a \"static_path\" setting, so using below\n\t\t\t(TORNADOBB_ROOT_URL + \"/static/(.*)\", tornado.web.StaticFileHandler, {\"path\": os.path.join(TORNADOBB_ROOT_PATH, \"static\")}),\n\t\t\t(TORNADOBB_ROOT_URL + \"/mp$\", MarkitupPreviewHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/sendmail$\", SendEmailHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/tz$\", TimezoneHandler),\n\t\t\turl(TORNADOBB_ROOT_URL + \"/search/*$\", SearchHandler,name=\"search_page\"),\n\t\t\turl(TORNADOBB_ROOT_URL + \"/?$\", MainHandler,name=\"home_page\"),\n\t\t\t(TORNADOBB_ROOT_URL + \"/forum/[a-z0-9]+/[a-z0-9]+/new/*$\", PostNewTopicHandler),#post new topic\n\t\t\t#forum/category_id/forum_id/c[current page]/p[jump to page]/a[total pages]/i[total items]/t[top_time]/b[button time]\n\t\t    (TORNADOBB_ROOT_URL + \"/forum/[a-z0-9]+/[a-z0-9]+/(\\d+/\\d+/\\d+/\\d+/\\d+\\.\\d+/\\d+\\.\\d+)?/?\", ForumHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/topic/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/(\\d+/\\d+/\\d+)?$\", TopicHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/topic/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/cmd$\", TopicManagementHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/topic/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/reply$\", ReplyTopicHandler),#reply a topic\n\t\t    (TORNADOBB_ROOT_URL + \"/topic/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/edit$\", PostEditHandler),#edit a topic\n\t\t    (TORNADOBB_ROOT_URL + \"/topic/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/delete$\", PostDeleteHandler),#delete a topic\n\t\t    url(TORNADOBB_ROOT_URL + \"/login/*$\", UserLoginHandler,name=\"login_page\"),\n\t\t    (TORNADOBB_ROOT_URL + \"/logout/*$\", UserLogoutHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/register/*?$\", UserRegisterHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/resend/*?$\", ResendVerifyMailHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/active$\", UserActiveHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/forget/*?$\", UserForgetPasswordHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/user-info$\", UserInfoHandler),\n\t\t    url(TORNADOBB_ROOT_URL + \"/profile/?$\", UserProfileHandler,name=\"profile_page\"),\n\t\t    (TORNADOBB_ROOT_URL + \"/profile/avatar/*$\", UserAvatarHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/profile/personality/*$\", UserSignatureHandler),\n\t\t    (TORNADOBB_ROOT_URL + \"/profile/display/*$\", UserDisplayHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/topics/[a-z0-9]+$\", UserTopicsHandler),\n\t\t\t#user_di/category_di/forum_id/current_page/total_pages_num/total_items_num\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/topics/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/\\d+(/\\d+/\\d+)?$\", UserTopicsRepliesHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/replies/[a-z0-9]+$\", UserRepliesHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/replies/[a-z0-9]+/[a-z0-9]+/[a-z0-9]+/\\d+(/\\d+/\\d+)?$\", UserTopicsRepliesHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/pwd/*$\", UserPasswordHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/email/*$\", UserEmailHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/profile/privacy/*$\", UserPrivacyHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/?$\", AdminDashboardHandler),\n\t\t\turl(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/category/?$\", AdminCategoryHandler,name=\"admin_category_page\"),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/category/edit$\", AdminCategoryEditHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/category/close$\", AdminCategoryOpenCloseHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/category/delete$\", AdminCategoryDeleteHandler),\n\t\t\turl(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/forum/?\", AdminForumHandler,name=\"admin_forum_page\"),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/forum/edit\", AdminForumEditHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/forum/close$\", AdminForumOpenCloseHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/forum/delete$\", AdminForumDeleteHandler),\n\t\t\turl(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/moderator/?$\", AdminModeratorHandler,name=\"admin_moderator_page\"),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/moderator/edit$\", AdminModeratorEditHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/moderator/delete$\", AdminModeratorDeleteHandler),\n\t\t\turl(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/member/?$\", AdminMemberHandler,name=\"admin_member_page\"),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/member/close$\", AdminMemberOpenCloseHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/member/postable$\", AdminMemberShutHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/member/add$\", AdminMemberAddHandler),\n\t\t\t(TORNADOBB_ROOT_URL + TORNADOBB_ADMIN_URL + \"/member/add_with_file$\", AdminMemberAddWithFileHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/rules/*$\", ForumRulesHandler),\n\t\t\t(TORNADOBB_ROOT_URL + \"/language/*$\", ChangeLanguageHandler),\n\t\t\t]\n\n","repo_name":"ds828/tornadobb","sub_path":"tornadobb/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":16034,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"38644598576","text":"import sys\nimport csv\nimport json\n\ndata = {}\n\nkwargs = {'newline': ''}\nmode = 'r'\n\ndisengagement_histogram = {}\nif sys.version_info < (3, 0):\n    kwargs.pop('newline', None)\n    mode = 'rb'\n\nmonths = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'}\n\ninverse_month = {}\nfor k,v in months.items():\n\tinverse_month[v] = k\n\ndef get_month(row):\n\tprint(row)\n\tif '.' in row[2]:\n\t\tmonth = int(row[2].split('.')[0])\n\t\tif(month > 2000):\n\t\t\tmonth = int(row[2].split('.')[1])\n\t\t#print(month)\n\t\t#month = int(month)\n\telif('EasyMile' in row[0]): \n\t\tmonth = int(row[2].split('-')[1])\n\telif('/' in row[2]): \n\t\tmonth = row[2].split('/')[0]\n\t\tmonth = int(month)\n\telif 'Lyft' in row[0]:\n\t\tmonth = row[2].split('-')[1]\n\t\tmonth = int(month)\n\telif('Toyota' in row[0]): \n\t\tmonth = row[2].split('-')[1]\n\t\tmonth = int(month)\n\telif 'Apple' in row[0]:\n\t\tmonth = inverse_month[row[2].split('-')[1]]\n\telif('CRUISE' in row[0]): \n\t\tmonth = row[2].split('-')[1]\n\t\tmonth = inverse_month[month]\n\t\n\telif 'Aurora' in row[0]:\n\t\tmonth = row[2][4:6]\n\t\tmonth = int(month)\n\telif 'AutoX' in row[0]:\n\t\tmonth = row[2][:3]\n\t\tmonth = inverse_month[month]\n\telif 'Phantom' in row[0]:\n\t\tmonth = row[2][:3]\n\t\tmonth = inverse_month[month]\n\t\n\n\tmonth = int(month)\n\tif(month == 12):\n\t\tmonth = 0\n\treturn month\n\ndef process_disengagements(filename):\n\tfp = open(filename, mode, encoding='windows-1254', **kwargs) \n\treader = csv.reader(fp, delimiter=',', quotechar='\"')\n\tnext(reader, None)  # skip the headers\n\tdata_read = [row for row in reader]\n\tprint(len(data_read))\n\n\tfor row in data_read:\n\t\tif(row[0] == ''):\n\t\t\tcontinue\n\t\tmonth = get_month(row)\n\t\tprint(row)\n\t\tprint(\"month =\", month)\n\t\tdata[row[0]][\"disengagements_month\"][month] += 1\n\n\t\trow[8] = row[8].replace('\\n', '')\n\n\t\tif row[7] not in data[row[0]][\"disengagements_location\"]:\n\t\t\tdata[row[0]][\"disengagements_location\"][row[7]] = {}\n\t\t\tdata[row[0]][\"disengagements_location\"][row[7]]['location'] = row[7]\n\t\t\tdata[row[0]][\"disengagements_location\"][row[7]]['numbers_by_month'] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\tdata[row[0]][\"disengagements_location\"][row[7]]['numbers_by_month'][month] += 1\n\n\t\tif row[6] not in data[row[0]][\"disengagements_actor\"]:\n\t\t\tdata[row[0]][\"disengagements_actor\"][row[6]] = {}\n\t\t\tdata[row[0]][\"disengagements_actor\"][row[6]]['actor'] = row[6]\n\t\t\tdata[row[0]][\"disengagements_actor\"][row[6]]['numbers_by_month'] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\tdata[row[0]][\"disengagements_actor\"][row[6]]['numbers_by_month'][month] += 1\n\n\t\tif row[8] not in data[row[0]][\"disengagements_reason\"]:\n\t\t\tdata[row[0]][\"disengagements_reason\"][row[8]] = {}\n\t\t\tdata[row[0]][\"disengagements_reason\"][row[8]]['reason'] = row[8]\n\t\t\tdata[row[0]][\"disengagements_reason\"][row[8]]['numbers_by_month'] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\tdata[row[0]][\"disengagements_reason\"][row[8]]['numbers_by_month'][month] += 1\n\n\n\t\t\n\ndef process_miles(filename):\n\tfp = open(filename, mode, **kwargs) \n\treader = csv.reader(fp, delimiter=',', quotechar='\"')\n\tnext(reader, None)  # skip the headers\n\tdata_read = [row for row in reader]\n\n\tfor row in data_read:\n\t\tif(row[0] == ''):\n\t\t\tcontinue\n\t\tif(row[0] not in data):\n\t\t\tdata[row[0]] = {}\n\t\t\tdata[row[0]][\"company_name\"] = row[0]\n\t\t\tdata[row[0]][\"miles\"] = 0.0\n\t\t\tdata[row[0]][\"miles_month\"] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\t\tdata[row[0]][\"disengagements\"] = 0\n\t\t\tdata[row[0]][\"disengagements_month\"] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\t\tdata[row[0]][\"fleet\"] = 0\n\t\t\tdata[row[0]][\"fleet_month\"] = [0,0,0,0,0,0,0,0,0,0,0,0]\n\t\t\tdata[row[0]][\"disengagements_reason\"] = {}\n\t\t\tdata[row[0]][\"disengagements_actor\"] = {}\n\t\t\tdata[row[0]][\"disengagements_location\"] = {}\n\t\tdata[row[0]][\"miles\"] += float(row[16].replace(',',''))\n\t\tdata[row[0]][\"disengagements\"] += int(row[3])\n\t\t\n\n\t\tall_zero = True\n\t\tfor i in range(12):\n\t\t\tif(float(row[4+i])) > 0:\n\t\t\t\tall_zero = False\n\t\t\t\tdata[row[0]][\"fleet_month\"][i] += 1\n\t\t\tdata[row[0]][\"miles_month\"][i] += float(row[4+i])\n\n\n\t\tif(not all_zero):\n\t\t\tdata[row[0]][\"fleet\"] += 1\n\nprocess_miles('2020-Autonomous-Mileage-Reports_Amended-4.csv')\nprocess_miles('2019-20-Autonomous-Mileage-Reports-first-time-filers.csv')\n\nprocess_disengagements('2020-Autonomous-Vehicle-Disengagement-Reports.csv')\nprocess_disengagements('2019-20-Autonomous-Vehicle-Disengagement-Reports-first-time-filers.csv')\n\ndata_array = []\nfor k, v in data.items():\n\tfor k1,v1 in v.items():\n\t\tif type(v1) == dict:\n\t\t\tarray = []\n\t\t\tif type(v1) == dict:\n\t\t\t\tfor k2,v2 in v1.items():\n\t\t\t\t\tarray.append(v2)\n\t\t\tv[k1] = array\n\t\telse:\n\t\t\tv[k1] = v1\n\tdata_array.append(v)\n\n\nwith open('data_for_bad_chinni_whose_name_is_vivek.json', 'w') as fp:\n\tjson.dump(data_array, fp)\n\n\n","repo_name":"keerthanpg/stop-the-trolley","sub_path":"scripts/extractdata.py","file_name":"extractdata.py","file_ext":"py","file_size_in_byte":4631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"13649107815","text":"# This is the implementation of the actual Support Vector Machine meant to use the Kernel implemented in\n# ProtSeqKernel.py.\n# Author: Eric Boone\n# Date: 10/5/2022\n\nimport ProtSeqKernel\nfrom sklearn import svm\nimport numpy as np\nimport time\nimport AACount\nimport matplotlib.pyplot as plt\nimport membraneProtML_helper as hlpr\n\n\nif __name__ == \"__main__\":\n    prot_class_svm = svm.SVC(kernel=\"precomputed\")\n\n    train_data, train_labels = hlpr.read_data(\"rawdata/short_data_train.csv\")\n    test_data, test_labels = hlpr.read_data(\"rawdata/short_data_test.csv\")\n\n    # Train and test using the kernel implemented in ProtSeqKernel.py.\n\n    train_start = time.time()\n    train_gram_mat = ProtSeqKernel.protein_string_kernel(train_data, train_data)\n    print(\"Training Matrix time:\", time.time() - train_start)\n    print(train_gram_mat)\n    prot_class_svm.fit(train_gram_mat, train_labels)\n\n    test_start = time.time()\n    test_gram_mat = ProtSeqKernel.protein_string_kernel(test_data, train_data)\n    print(test_gram_mat)\n    plt.imshow(train_gram_mat, test_gram_mat, cmap='hot', interpolation='nearest')\n    plt.show()\n    print(\"Test Matrix time:\", time.time() - test_start)\n    print(np.array(test_labels))\n    kernel_results = prot_class_svm.predict(test_gram_mat)\n    print(kernel_results)\n\n    tot_correct = 0\n    for i in range(0, len(kernel_results)):\n        if kernel_results[i] == np.array(test_labels)[i]:\n            tot_correct += 1\n\n    print(\"Kernel accuracy:\" + str(tot_correct[1]/len(kernel_results) * 100) + \"%\")\n","repo_name":"eboone1001/membraneProtML","sub_path":"ProteinClassifierSVM.py","file_name":"ProteinClassifierSVM.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7771522261","text":"import pandas as pd\nfrom bs4 import BeautifulSoup\nfrom requests import get\nimport numpy as np\n\ndef extract_stats(url):\n    page = get(url).text\n    bs = BeautifulSoup(page, 'lxml')\n\n    nos = bs.findAll('table', {'class':'engineTable'})\n\n    table = nos[2]\n\n    headers = []\n    titles = table.findAll('th')\n    for item in titles:\n        headers.append(item.text)\n\n    body = table.findAll('tr')\n    headings = body[0]\n    tablebody = body[1:]\n    rows = []\n\n    for row_num in range(len(tablebody)):\n        row = []\n        for row_item in tablebody[row_num].findAll('td'):\n            aa = row_item.text\n            row.append(aa)\n\n\n        rows.append(row)\n\n    df = pd.DataFrame(data=rows, columns=headers)\n    df.to_csv('output_statsguru.csv', index=False)\n\ndef main():\n    url = ''\n    print('Enter/Paste ESPNCricinfo Statsguru url:')\n    url = input()\n    extract_stats(url)\n\n\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"karanathrey/CricketData","sub_path":"venv/statsguruscraper.py","file_name":"statsguruscraper.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23066924245","text":"import pygame as pg # This makes it so that whenever we reference pygame in the code below, we only have to write 'pg'\nfrom pygame.sprite import Sprite\nfrom assets import settings as sett\nimport os\nvec = pg.math.Vector2\n\n# This class is for the player, the ship\nclass Player(Sprite):\n    def __init__(self):\n        Sprite.__init__(self)\n\n        familiarShip = pg.image.load(os.path.join('assets', 'familiar_ship.png'))\n        familiarShip_rect = familiarShip.get_rect()\n        familiarShip.set_colorkey(sett.BLACK)\n        familiarShip = pg.transform.scale(familiarShip, (90,90))\n\n        self.image = familiarShip\n        self.rect = self.image.get_rect()\n        self.rect.center = (sett.WIDTH/2, sett.HEIGHT/2)\n        self.pos = vec(sett.WIDTH/2, sett.HEIGHT/2) # Position\n        self.vel = vec(0,0)\n        self.acc = vec(0,0)\n        print(self.rect.center)\n\n    def controls(self):\n        keys = pg.key.get_pressed() # This is saying \"Hey, a key has been pressed\".  The lines under define how when we press a certain button, a coorelating action happens\n        if keys[pg.K_a]:\n            self.acc.x = -5\n        if keys[pg.K_d]:\n            self.acc.x = 5\n        if keys[pg.K_w]:\n            self.acc.y = -5\n        if keys[pg.K_s]:\n            self.acc.y = 5\n\n# these lines make it so that the player teleports to the other side of the screen if they run into the screen borders\n        if self.pos.x > sett.WIDTH:\n            self.pos.x = 0\n        if self.pos.x < 0:\n            self.pos.x = sett.WIDTH\n\n        if self.pos.y > sett.HEIGHT:\n            self.pos.y = 0\n        if self.pos.y < 0:\n            self.pos.y = sett.HEIGHT\n\n    def update(self):\n        self.acc = vec(0, sett.PLAYER_GRAV)\n        self.controls()\n\n# makes the controls and their speeed work the way they do\n        self.acc.x += self.vel.x * sett.PLAYER_FRIC\n        self.acc.y += self.vel.y * sett.PLAYER_FRIC\n        self.vel += self.acc\n        self.pos += self.vel + 0.5 * self.acc\n        self.rect.center = self.pos\n\n","repo_name":"ericLedoux/videoGame","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69829724088","text":"import s3fs\nfrom prefect import flow, task, logging\n\n@task\ndef read_from_s3_file(p: str):\n    l = logging.get_logger()\n    l.info(\"Reading from S3\")\n    l.info(\"Data provided: \" + p)\n\n    s3 = s3fs.S3FileSystem()\n    s3_path = 's3://emilytest/' + p\n\n    with s3.open(s3_path, 'r') as f:\n        s = f.read()\n\n    l.info(s)\n\n    return s\n\n@task\ndef write_to_s3_file(s: str, p: str):\n    l = logging.get_logger()\n    l.info(\"Writing to S3\")\n    l.info(\"Data recieved: \" + s)\n\n    s3 = s3fs.S3FileSystem()\n    s3_path = 's3://emilytest/' + p\n\n    with s3.open(s3_path, 'w') as f:\n        f.write(s)\n\n    return p\n\n@flow(name=\"Simple Read and Write to S3\")\ndef run_flow():\n    one = read_from_s3_file(\"test20230606.txt\")\n    data = one + \" and goodbye\"\n    two = write_to_s3_file(data, \"test20230606.txt\")\n\nif __name__ == \"__main__\":\n    run_flow()","repo_name":"awormus/prefect-playground","sub_path":"s3-with-s3fs/simple-write-to-s3.py","file_name":"simple-write-to-s3.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"415179258","text":"# -*- coding:utf-8 _*-\n\"\"\"\n@Author:Tian Zhang\n@File: image_check.py\n@Create Time: 2021/6/9 11:02\n\"\"\"\n\nimport os\n\n\ndef image_check(path):\n    # 缺失计数\n    missing_count = 0\n    # 新建txt，写入首句\n    with open(\"log.txt\", \"w\") as f:\n        f.write(\"缺失KG结尾图片的文件夹：\\n\")\n    for dir_name, sub_dirs, filenames in os.walk(path):\n        for sub_dir in sub_dirs:\n            # 忽略父级文件夹\n            if sub_dir[0] == \"m\":\n                continue\n            else:\n                # 生成模板文件夹路径\n                dir_path = os.path.join(dir_name, sub_dir)\n                # 生成图片名\n                valid_name = sub_dir + \".jpg\"\n                for dir_name_2, sub_dirs_2, filenames_2 in os.walk(dir_path):\n                    # 模板文件夹中无同名图片\n                    if valid_name not in filenames_2:\n                        # 计数+1\n                        missing_count += 1\n                        # append模式写入缺失数据\n                        with open(\"log.txt\", \"a\") as f:\n                            f.write(dir_path + \"\\\\\" + valid_name + \"\\n\")\n    # 统计缺失数据，尾行添加\n    with open(\"log.txt\", \"a\") as f:\n        f.write(\"共缺失\" + str(missing_count) + \"个文件\\n\")\n    return missing_count\n\n\nif __name__ == '__main__':\n    target_path = input(\"请输入文件夹绝对路径：\")\n    count = image_check(target_path)\n    print(\"目录已检查完毕，共缺失\", count, \"个文件\")\n    input(\"回车键退出\")\n","repo_name":"ntexplorer/PythonPractice","sub_path":"GYY/GYY_CATIA_V5_Image_Check/image_check.py","file_name":"image_check.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2307911753","text":"\"\"\"\nPlot early metrics for sweeps across random seeds for random and GPT-3.5 agents.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\nfrom chart_utils import initialize_plot_bar, save_plot, get_results_full_path\n\nINPUT_FILE = \"../results/seed/Seed GPT-3.5.csv\"\nOUTPUT_DIR = \"./seed/gpt-3.5\"\n\n\ndef main() -> None:\n    \"\"\"Main function.\"\"\"\n\n    # Initialize\n    initialize_plot_bar()\n\n    # Load the data\n    df = pd.read_csv(get_results_full_path(INPUT_FILE))\n    print(f\"Loaded {INPUT_FILE} with shape {df.shape}\")\n\n    # Plot a bunch of different bar graphs for different metrics\n    # All grouped by map_name, x-axis is max_years\n    for metric_name, y_label in [\n        (\"welfare\", \"Welfare Points\"),\n        (\"centers\", \"Supply Centers\"),\n        (\"units\", \"Unit Counts\"),\n    ]:\n        # Plot the welfare scores for each power\n        cols_of_interest = [\n            f\"score/{metric_name}/AUS\",\n            f\"score/{metric_name}/ENG\",\n            f\"score/{metric_name}/FRA\",\n            f\"score/{metric_name}/GER\",\n            f\"score/{metric_name}/ITA\",\n            f\"score/{metric_name}/RUS\",\n            f\"score/{metric_name}/TUR\",\n        ]\n\n        welfare_df = df[cols_of_interest].copy()\n\n        # convert to long format\n        welfare_df = welfare_df.melt()\n\n        # update the column names\n        welfare_df.columns = [\"Power\", y_label]\n\n        # remove the 'score/welfare/' from the Power names\n        welfare_df[\"Power\"] = welfare_df[\"Power\"].str.replace(\n            f\"score/{metric_name}/\", \"\"\n        )\n\n        # Create the plot\n        plot = sns.barplot(\n            data=welfare_df,\n            x=\"Power\",\n            y=y_label,\n            capsize=0.1,\n            errorbar=\"ci\",\n        )\n\n        # Print the mean and error of each bar\n        power_names = welfare_df[\"Power\"].unique()\n        for bar_label, bar, line in zip(power_names, plot.patches, plot.lines[::3]):\n            error = (line.get_ydata()[1] - line.get_ydata()[0]) / 2\n            print(f\"{y_label} ({bar_label}): {bar.get_height():.2f} ± {error:.2f}\")\n        print()\n\n        # Set labels and title\n        plt.xlabel(\"Power\")\n        plt.ylabel(y_label)\n        title = f\"{y_label} by Power - GPT-3.5 Agent Sweep ($N=64$)\"\n        plt.title(title)\n\n        # Save the plot\n        output_file = get_results_full_path(OUTPUT_DIR + f\"/{metric_name}.png\")\n        save_plot(output_file)\n        print(f\"Saved plot '{title}' to {output_file}\")\n\n        # Clear the plot\n        plt.clf()\n\n    # Also plot the min, median, mean, and max aggregated welfare scores.\n    cols_of_interest = [\n        \"welfare/min\",\n        \"welfare/median\",\n        \"welfare/mean\",\n        \"welfare/max\",\n    ]\n\n    welfare_df = df[cols_of_interest].copy()\n\n    # convert to long format\n    welfare_df = welfare_df.melt()\n\n    # update the column names\n    bar_label = \"Aggregation Type\"\n    y_label = \"Welfare Points →\"\n    welfare_df.columns = [bar_label, y_label]\n\n    # Rename the aggregation types\n    welfare_df[bar_label] = (\n        welfare_df[bar_label].str.replace(\"welfare/\", \"\").str.title()\n    )\n\n    # Create the plot\n    plot = sns.barplot(\n        data=welfare_df,\n        x=bar_label,\n        y=y_label,\n        capsize=0.1,\n        errorbar=\"ci\",\n    )\n\n    # Print the mean and error of each bar\n    bar_labels = welfare_df[bar_label].unique()\n    for bar_label, bar, line in zip(bar_labels, plot.patches, plot.lines[::3]):\n        error = (line.get_ydata()[1] - line.get_ydata()[0]) / 2\n        print(f\"{y_label} ({bar_label}): {bar.get_height():.2f} ± {error:.2f}\")\n    print()\n\n    # Set labels and title\n    plt.xlabel(bar_label)\n    plt.ylabel(y_label)\n    title = \"Aggregated Welfare Points - GPT-3.5 Agent Sweep ($N=64$)\"\n    plt.title(title)\n\n    # Save the plot\n    output_file = get_results_full_path(OUTPUT_DIR + \"/aggregated_welfare.png\")\n    save_plot(output_file)\n    print(f\"Saved plot '{title}' to {output_file}\")\n\n    # Clear the plot\n    plt.clf()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mukobi/welfare-diplomacy","sub_path":"experiments/charts/random_agents.py","file_name":"random_agents.py","file_ext":"py","file_size_in_byte":4045,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"74442628407","text":"import os\nimport imageio\nimport numpy as np\n\n\ndef reflect(filename):\n    # Get filenames\n    base, ext = os.path.splitext(filename)\n    filename2 = base + \"_reflected\" + ext\n    # Get images\n    im1 = imageio.imread(filename)\n    im2 = reflect_image(im1)\n    # Write back\n    imageio.imsave(filename2, im2)\n\n\ndef reflect_image(im1, distance=0, startAlpha=0.20, N=80):\n\n    # Initialize im2\n    shape = list(im1.shape)\n    shape[0] *= 2\n    im2 = np.zeros(shape, im1.dtype)\n    im2[: im1.shape[0], : im1.shape[1], :] = im1\n\n    y2_max = 0\n    for x in range(im1.shape[1]):\n\n        # Search start of column\n        y = im1.shape[0]\n        while True:\n            y -= 1\n            if y < 0:\n                break\n            if im1[y, x, 3] > 0:\n                break\n        y0 = y\n        if not y0:\n            continue\n\n        # Reflect\n        for y in range(N):\n            y1 = y0 - y - bool(not distance)\n            y2 = y0 + y + distance\n            im2[y2, x, :] = im1[y1, x, :]\n            im2[y2, x, 3] *= startAlpha * (1.0 - float(y / N))\n        y2_max = max(y2_max, y2)\n\n    # Return im2, cropped appropriately\n    return im2[:y2_max, : shape[1], :]\n\n\nif __name__ == \"__main__\":\n    filename = \"/home/almar/projects/www/pyzo-www/wwwpyzo/_static/pyzo_box.png\"\n\n    im1 = imageio.imread(filename)\n    im2 = reflect_image(im1)\n\n    import visvis as vv\n\n    vv.clf()\n    vv.subplot(121)\n    vv.imshow(im1)\n    vv.subplot(122)\n    vv.imshow(im2)\n\n# reflect(filename)\n","repo_name":"pyzo/pyzo-website","sub_path":"static/refelector.py","file_name":"refelector.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"29381064409","text":"# Diccionario de productos y sus precios\nproductos = {\n    1: {\"nombre\": \"Juego Pokemon X para Nintendo 3DS\", \"precio\": 33.77},\n    2: {\"nombre\": \"Nintendo 3DS XL\", \"precio\": 203.00},\n    3: {\"nombre\": \"Juego Mario Kart 7 para Nintendo 3DS\", \"precio\": 27.58},\n    4: {\"nombre\": \"PlayStation 4\", \"precio\": 348.00},\n    5: {\"nombre\": \"FIFA 16, PlayStation 4\", \"precio\": 51.19}\n}\n\n# Carro de compras\ncarro = []\n\ndef agregar_producto(producto_id, cantidad):\n    producto_id = int(producto_id)\n    cantidad = int(cantidad)\n\n    # Verificar si el producto existe en el diccionario\n    if producto_id not in productos:\n        print(\"El producto seleccionado no existe.\")\n        return\n\n    # Agregar el producto al carro\n    producto = productos[producto_id]\n    carro.append({\"producto_id\": producto_id, \"nombre\": producto[\"nombre\"], \"precio\": producto[\"precio\"], \"cantidad\": cantidad})\n    print(f\"Se agregaron {cantidad} unidad(es) de {producto['nombre']} al carro.\")\n\ndef mostrar_productos():\n    if not carro:\n        print(\"El carro está vacío.\")\n        return\n\n    print(\"Productos en el carro:\")\n    for item in carro:\n        producto_id = item[\"producto_id\"]\n        nombre = item[\"nombre\"]\n        precio = item[\"precio\"]\n        cantidad = item[\"cantidad\"]\n        print(f\"Producto ID: {producto_id}, Nombre: {nombre}, Precio: {precio}, Cantidad: {cantidad}\")\n\ndef calcular_total():\n    total = 0.0\n\n    # Calcular el total sin descuentos\n    for item in carro:\n        precio = item[\"precio\"]\n        cantidad = item[\"cantidad\"]\n        subtotal = precio * cantidad\n        total += subtotal\n\n    # Aplicar descuentos según los productos en el carro\n    descuento = 0.0\n    productos_en_carro = [item[\"producto_id\"] for item in carro]\n\n    if set([1, 2, 3]).issubset(productos_en_carro):\n        descuento = total * 0.20\n    elif set([4, 5]).issubset(productos_en_carro):\n        descuento = total * 0.15\n\n    total -= descuento\n    total = round(total, 1)\n    return total\n\n# Programa principal\nwhile True:\n    orden = input(\"Ingrese una orden (agregar, ver, checkout): \")\n\n    if orden == \"agregar\":\n        producto_id, cantidad = input(\"Ingrese el producto y la cantidad (producto,cantidad): \").split(\",\")\n        agregar_producto(producto_id, cantidad)\n    elif orden == \"ver\":\n        mostrar_productos()\n    elif orden == \"checkout\":\n        total = calcular_total()\n        print(f\"Total a pagar: ${total}\")\n        break\n    else:\n        print(\"Orden inválida. Intente nuevamente.\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej4/hito2_ej4_06c5e8c2fb88969cf649207dabb88209.py","file_name":"hito2_ej4_06c5e8c2fb88969cf649207dabb88209.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36806144863","text":"def a():\n    b=\"The quick Brow Fox\"\n    i=0\n    c=0\n    c1=0\n    while i<len(b):\n        if b[i]>\"a\" and b[i]<\"z\":\n            c+=1\n        elif b[i]>\"A\" and b[i]<\"Z\":\n            c1+=1\n        i+=1\n    print(\"uppercase\",c1)\n    print(\"lowercase\",c)\na()\n        ","repo_name":"soudaminimalik2001/function","sub_path":"duck8.py","file_name":"duck8.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29321989319","text":"#Contestador de celular\n\"\"\"\n Declaración de variables.\n (Termina) es una variable booleana reutilizable para terminar los bucles while.\n (comprobador) es una variable booleana que comprueba solo en el bucle donde se intruduce el número de celular.\n (numero) es la variale que guarda el número de celular como int.\n (hora) es la variable que guarda el valor de la hora en que se llama, como int.\n (cantidad_n) es una variable que guarda el número de celular pero como str para posteriormente \n              poder usarla con funciones como len() y .found(). \n\n\"\"\"\n\ntermina = False\ncomprobador = False\nnumero = 0\nhora = 0\ncantidad_n = 0\n\n\n\"\"\"\n Comprobador si el input es realmente sólo números, sino, se ingresa nuevamente el número. El valor se guarda en la variable numero como int. \n Guardar el valor de la variable numero en la variable numero_str.\n Se \n \n\"\"\"\nwhile termina != True:\n    try:\n        numero = input(\"Número de celular del que llamas: \")\n        numero_str = str(numero)\n        int(numero)\n        comprobador = True\n    except:\n        comprobador = False\n\n    if comprobador == True:\n        if len(numero_str) == 8:\n            termina = True\n        else:\n            print(\"Ingresa un número de 8 cifras.\\n\")        \n    else:\n        print(\"Ingresa un número válido, sin letras.\\n\")\n\ntermina = False\n\nwhile termina != True:\n    hora = int(input(\"Hora de la llamada: \"))\n    if hora <= 23 and hora >= 0:\n        termina = True\n    else:\n        print(\"La hora no es válida\\n\")\n\ntermina = False\n\nwhile termina != True:\n\n    if 0 < hora < 7:\n        print(\"CONTESTAR.\")\n    elif 7 <= hora < 14 :\n        if numero_str.find(\"909\") == 5:\n            print(\"CONTESTAR.\")\n        else:\n            print(\"NO CONTESTAR.\")\n    elif 14 <= hora <= 17:\n        print(\"NO CONTESTAR.\")\n    elif 17 <= hora < 19:\n        if numero_str.find(\"877\") == 0:\n            print(\"NO CONTESTAR.\")\n        else:\n            print(\"CONTESTAR.\")\n    elif 19 <= hora <= 23 :\n        print(\"NO CONTESTAR.\")        \n    elif hora == 0 :\n        print(\"NO CONTESTAR.\") \n    termina = True\n\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej2/hito1_ej2_96ebcd16616f42d15540c92590fd4f2d.py","file_name":"hito1_ej2_96ebcd16616f42d15540c92590fd4f2d.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11459652258","text":"import math\n\nInvestmentAmount = input(\"Enter Investment: \")\nAnnualInterest = input(\"Enter Annual Interest: \")\nYears = input(\"Approximate number of years the investment will be held: \")\n\nAnnualInterest = float(AnnualInterest)/100\n\nInterestAccrued =  math.pow( (1 + (AnnualInterest/12 ) ) , 12 )\n\nfutureInvestmentValue = float(InvestmentAmount) * InterestAccrued\n\nprint(\"Accumulated Value: %.2f\" % futureInvestmentValue)\n","repo_name":"TPBvirus/PyClass","sub_path":"Lab2/lab2Exercises4.py","file_name":"lab2Exercises4.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29421403649","text":"# completa el código de la función\ndef amigos(a,b):\n    sum_div_a = sum(divisores(a))\n    sum_div_b = sum(divisores(b))\n    return sum_div_a == b and sum_div_b == a\n\ndef divisores(n):\n    div = [1]\n    for i in range(2, int(n ** 0.5) + 1):\n        if n % i == 0:\n            div.append(i)\n            if i != n // i:\n                div.append(n // i)\n    return div\n\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej2/tema2_ej2_b6ade3804649fc1a6edee8e89d342e4d.py","file_name":"tema2_ej2_b6ade3804649fc1a6edee8e89d342e4d.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7915569136","text":"'''\r\nCreated on 2 / 16 / 2022\r\n@author:   Ethan Kalika\r\nPledge:    \"I pledge my honor that I have abided by the Stevens Honor System\"\r\n\r\nCS115 - Hw 3\r\n'''\r\n# Be sure to submit hw3.py.  Remove the '_template' from the file name.\r\n\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\n' PROBLEM 0\r\n' Implement the function giveChange() here:\r\n' See the PDF in Canvas for more details.\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\ndef giveChange(num, coins):\r\n    if num == 0:\r\n        return [0,[]]\r\n    elif num < 0 or coins == []:\r\n        return [float(\"inf\"),[]]\r\n    elif coins[0] > num:\r\n        return giveChange(num, coins[1:])\r\n    else:\r\n        var1 = giveChange(num - coins[0], coins)\r\n        use_it = [1 + var1[0], [coins[0]] + var1[1]]\r\n        loose_it = giveChange(num, coins[1:])\r\n        if use_it[0] <= loose_it[0]:\r\n            return use_it\r\n        else:\r\n            return loose_it\r\n\r\n# Here's the list of letter values and a small dictionary to use.\r\n# Leave the following lists in place.\r\nscrabbleScores = \\\r\n   [ ['a', 1], ['b', 3], ['c', 3], ['d', 2], ['e', 1], ['f', 4], ['g', 2],\r\n     ['h', 4], ['i', 1], ['j', 8], ['k', 5], ['l', 1], ['m', 3], ['n', 1],\r\n     ['o', 1], ['p', 3], ['q', 10], ['r', 1], ['s', 1], ['t', 1], ['u', 1],\r\n     ['v', 4], ['w', 4], ['x', 8], ['y', 4], ['z', 10] ]\r\n\r\nDictionary = ['a', 'am', 'at', 'apple', 'bat', 'bar', 'babble', 'can', 'foo',\r\n              'spam', 'spammy', 'zzyzva']\r\n\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\n' PROBLEM 1\r\n' Implement wordsWithScore() which is specified below.\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\ndef getLetterScore(letter, scores):\r\n    \"\"\"\r\n    Input: a letter and a list of letter scores\r\n    Output: the letter score associated with that letter\r\n    \"\"\"\r\n    if scores[0][0] == letter:\r\n        return scores[0][1]\r\n    else:\r\n        return getLetterScore(letter, scores[1:])\r\n\r\ndef wordScore(word, scores):\r\n    \"\"\"\r\n    Input: a word and a list of scores associated with letters\r\n    Output: a score associated with that word\r\n    \"\"\"\r\n    if word == \"\":\r\n        return 0\r\n    else:\r\n        return getLetterScore(word[0], scores) + wordScore(word[1:], scores)\r\n\r\ndef wordsWithScore(dct, scores):\r\n    '''List of words in dct, with their Scrabble score.\r\n\r\n    Assume dct is a list of words and scores is a list of [letter,number]\r\n    pairs. Return the dictionary annotated so each word is paired with its\r\n    value. For example, wordsWithScore(Dictionary, scrabbleScores) should\r\n    return [['a', 1], ['am', 4], ['at', 2] ...etc... ]\r\n    '''\r\n    if dct == []:\r\n        return []\r\n    else:\r\n        return [[dct[0], wordScore(dct[0], scores)]] + wordsWithScore(dct[1:], scores)\r\n\r\n\r\n\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\n' PROBLEM 2\r\n' For the sake of an exercise, we will implement a function\r\n' that does a kind of slice. You must use recursion for this\r\n' one. Your code is allowed to refer to list index L[0] and\r\n' also use slice notation L[1:] but no other slices.\r\n' (Notice that you cannot assume anything about the length of the list.)\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\ndef take(n, L):\r\n    '''Returns the list L[0:n], assuming L is a list and n is at least 0.'''\r\n    if n == 0 or L == []:\r\n        return []\r\n    else:\r\n        return [L[0]] + take(n - 1, L[1:])\r\n\r\n\r\n\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\n' PROBLEM 3\r\n' Similar to problem 2, will implement another function\r\n' that does a kind of slice. You must use recursion for this\r\n' one. Your code is allowed to refer to list index L[0] and\r\n' also use slice notation L[1:] but no other slices.\r\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\r\ndef drop(n, L):\r\n    '''Returns the list L[n:], assuming L is a list and n is at least 0.'''\r\n    if L == []:\r\n        return []\r\n    elif n > 0:\r\n        return drop(n - 1, L[1:])\r\n    else:\r\n        return [L[0]] + drop(n - 1, L[1:])\r\n","repo_name":"EthanKalika/StevensClasses","sub_path":"CS115IntrotoPython/HW3_CoinProblem2/hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":4165,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21960629354","text":"import unittest\nfrom structura import RingBuffer\n\n\nclass TestRingBuffer(unittest.TestCase):\n    def test_ringbuffer_enqueue(self):\n        rb = RingBuffer(100)\n        for i in range(1000):\n            rb.enqueue(f'qwerqwer[{i}]]')\n        self.assertEqual(rb.size, 100)\n\n    def test_ringbuffer_peek(self):\n        rb = RingBuffer(32)\n        for i in range(32):\n            rb.enqueue(f'qwerqwer[{i}]]')\n        self.assertEqual(rb.peek(), 'qwerqwer[0]]')\n\n    def test_ringbuffer_dequeue(self):\n        rb = RingBuffer(32)\n        for i in range(32):\n            rb.enqueue(f'qwerqwer[{i}]]')\n        self.assertEqual(rb.dequeue(), 'qwerqwer[0]]')\n        self.assertEqual(rb.size, 31)\n\n    def test_ringbuffer_is_empty(self):\n        rb = RingBuffer(32)\n        self.assertTrue(rb.is_empty())\n        rb.enqueue('qwerqwer')\n        self.assertFalse(rb.is_empty())\n        rb.dequeue()\n        self.assertTrue(rb.is_empty())\n\n    def test_ringbuffer_is_full(self):\n        rb = RingBuffer(32)\n        self.assertFalse(rb.is_full())\n        for i in range(32):\n            rb.enqueue(f'qwerqwer[{i}]]')\n        self.assertTrue(rb.is_full())\n        rb.dequeue()\n        self.assertFalse(rb.is_full())\n\n    def test_ringbuffer_size(self):\n        rb = RingBuffer(32)\n        self.assertEqual(rb.size, 0)\n        for i in range(32):\n            rb.enqueue(f'qwerqwer[{i}]]')\n        self.assertEqual(rb.size, 32)\n        rb.dequeue()\n        self.assertEqual(rb.size, 31)\n\n    def test_ringbuffer_dequeue_when_empty(self):\n        rb = RingBuffer(32)\n        self.assertRaises(IndexError, rb.dequeue)\n\n    def test_ringbuffer_peek_when_empty(self):\n        rb = RingBuffer(32)\n        self.assertRaises(IndexError, rb.peek)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Sekomer/structura","sub_path":"test/test-ringbuffer.py","file_name":"test-ringbuffer.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"43364607030","text":"import h5py\nimport numpy as np\n\ndef LoadFile(fileName, loadLabel = True):\n    maps = h5py.File(fileName,'r')\n    data = maps['imdb']['images']['data'][:]\n    label = None\n    if loadLabel:\n        label = maps['imdb']['images']['labels'][:]\n        if label.shape[1] > 88:\n            label = label[:,21:109]\n        label = np.where(label.squeeze()>0, 1, 0)\n    \n    maps.close()\n    \n    return data, label\n    \ndef LoadbyFold(fold_name, feature_path, use_type=['Spec', 'Ceps', 'GCoS'], t_range = None):\n# Load songs according to the content of given the fold.\n    f = open(fold_name, 'r')\n    songs = f.readlines()\n    f.close()\n    \n    Data = []\n    Label = []\n    Index = [] # Record each song's start index and length\n    \n    start_i = 0\n    samples = 0\n    for s_name in songs:\n        print('Loading songs: %d/%d' % (songs.index(s_name)+1, len(songs)), end='\\r')\n        \n        ### Load different types of feature of each song ### \n        tmp_d = []\n        skipped = False\n        for i in range(len(use_type)):\n            s_name = s_name.replace('/', '_').replace('\\n','').replace('.wav', '.mat')\n            useType = use_type[i]\n            data, label = LoadFile(feature_path+useType+'/'+s_name, loadLabel=True if i==0 else False) # only need to load labels once\n            \n            if i==0 and label.shape[0] != data.shape[0]:\n                print('\\nSkipped one song because sample number of label and data does not match')\n                print('%s - Label shape: %s - Data shape: %s' % (s_name, str(label.shape), str(data.shape)))\n                skipped = True\n                break\n            \n            ### Dealing with the given range of using specific time slice ###\n            if t_range != None:\n                samples_sec = 100 # 0.01s/sample\n                start_s = t_range[0]*samples_sec\n                end_s = t_range[1]*samples_sec\n                song_len = data.shape[0]\n                \n                if start_s >= song_len:\n                    err_msg = 'The given start time longer than current song(%.2fs)' % (song_len/samples_sec)\n                    print('Error messege: ' + err_msg + '\\n')\n                    assert False, err_msg\n                if end_s > song_len:\n                    end_s = song_len\n                    if i == 0:\n                        print('The given end time longer than current song(%.2fs). Resetting...' % (song_len/samples_sec))\n                \n                data = data[range(start_s, end_s)]\n                if i==0:\n                    label = label[range(start_s, end_s)]\n            \n            tmp_d.append(data)\n            if i==0: \n                Label.append(label)\n        if skipped: continue\n        \n        tmp_d = np.asarray(tmp_d)\n        tmp_data = tmp_d[0]\n        for i in range(1, len(use_type)):\n            tmp_data = np.concatenate((tmp_data, tmp_d[i]), axis=3)\n        shape = tmp_data.shape\n        tmp_data = tmp_data.reshape((shape[0], shape[2], shape[1], shape[3]))\n        Data.append(tmp_data)\n        \n        Index.append({'start_i': start_i, 'length': shape[0]})\n        start_i += shape[0]\n        samples += shape[0]\n    \n    shape = Data[0].shape\n    data_shape = (samples, shape[1], shape[2], shape[3])\n    \n    return Data, Label, Index, data_shape\n\ndef Hack4LoadbyFold(numSongs, givenFold, outName, use_same = False):\n# Create a new text file that is needed for LoadbyFold\n# Random select $numSongs in $givenFold\n\n    if use_same:\n        old = open(outName, 'r')\n        content = old.readlines()\n        old.close()\n        if len(content) < numSongs:\n            print('The given number of songs is less than that in the given fold. Re-selecting...')\n        elif len(content) > numSongs:\n            print('Using same fold (%s)' % outName)\n           \n            f = open(outName, 'w')\n            for i in range(numSongs):\n                if '\\n' not in content[i]:\n                    content[i] += '\\n'\n                f.write(content[i])\n            f.close()\n            \n            return outName\n        else:\n            print('Using same fold (%s)' % outName)\n            return outName\n    \n    f = open(givenFold, 'r')\n    songs = f.readlines()\n    f.close() \n    songs = np.random.permutation(songs)\n    \n    f = open(outName, 'w')\n    for i in range(numSongs):\n        if '\\n' not in songs[i]:\n            songs[i] += '\\n'\n        f.write(songs[i])\n    f.close()\n    \n    return outName\n\n    \n    \n    \n    \n    \n    \n","repo_name":"BreezeWhite/CFP_NeuralNetwork","sub_path":"loadFile.py","file_name":"loadFile.py","file_ext":"py","file_size_in_byte":4474,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"71950841848","text":"from typing import NamedTuple, Set\n\n\nGOODS_SEPARATOR_SYMBOL = \",\"\nGOODS_EXCLUDE_SYMBOL = \"-\"\n\n\nclass IngredientComposition(NamedTuple):\n    \"\"\"\n    Список желательных и нежелательных ингредиентов для коктейля\n    \"\"\"\n\n    include_ingredients: Set[str]\n    exclude_ingredients: Set[str]\n\n\ndef get_ingredients_from_str(goods_string: str) -> IngredientComposition:\n    \"\"\"\n    Из строки получаем список желательных и нежелательных ингредиентов\n    \"\"\"\n    include_ingredients = set()\n    exclude_ingredients = set()\n\n    for good in goods_string.split(GOODS_SEPARATOR_SYMBOL):\n        good = good.strip().lower()\n        if good.startswith(GOODS_EXCLUDE_SYMBOL):\n            good = good.lstrip(GOODS_EXCLUDE_SYMBOL).lstrip()\n            exclude_ingredients.add(good)\n        else:\n            include_ingredients.add(good)\n    include_ingredients -= exclude_ingredients\n\n    return IngredientComposition(include_ingredients, exclude_ingredients)\n","repo_name":"BPIlyich/telegram_alcobot","sub_path":"alcobot/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29481947479","text":"class Twitter:\n    def __init__(self):\n        self.trending_topics=[]\n\n    def tweet(self,mensaje):\n        if len(mensaje)>140:\n            print('Su tweet es muy largo, no se ha podido publicar')\n        else:\n            lista=mensaje.split()\n            tt_totales=[]\n            conteo_tt=[]\n            tt_sin_repetir=[]\n            for i in lista:\n                if i[0]=='#':\n                    tt_totales.append(i)\n            for i in tt_totales:\n                if i not in self.trending_topics:\n                    self.trending_topics.append(i)\n\n\n    \nif __name__ == \"__main__\":\n    twitter=Twitter()\n    twitter.tweet(\"gano #laroja\")\n    twitter.tweet(\"grande #chile\")\n    twitter.tweet(\"#laroja con dos goles, le gano a brasil, grande #laroja\")\n    print(twitter.trending_topics)\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema9_ej1/tema9_ej1_15634337.py","file_name":"tema9_ej1_15634337.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21831915545","text":"from tkinter import *\r\nimport random as rdm\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nclass Main(Frame):\r\n    def __init__(self, root):\r\n        super(Main, self).__init__(root)\r\n        self.UI()\r\n\r\n    def UI(self):\r\n        self.win = self.drow = self.lose = self.count = 0\r\n        firstbutton = Button(root, text=\"Камень\",bg=\"#8D6E63\", font=(\"Calibri\", 50),  command=lambda btn=1: self.btn_click(btn))\r\n        secondbutton = Button(root, text=\"Ножницы\", bg=\"#9E9E9E\", font=(\"Calibri\", 50), command=lambda btn=2: self.btn_click(btn))\r\n        thirdbutton = Button(root, text=\"Бумага\",bg=\"#EFEBE9\", font=(\"Calibri\", 50), command=lambda btn=3: self.btn_click(btn))\r\n        fourthbutton = Button(root, text=\"Перезапуск\",bg=\"#A7FFEB\", font=(\"Calibri\", 25), command=lambda btn=0: self.btn_click(btn))\r\n        fifthbutton = Button(root,text=\"График\", bg=\"#A7FFEB\", font=(\"Calibri\", 25), command=lambda btn=4: self.btn_click(btn))\r\n        \r\n        firstbutton.place(x=10, y=200, width=300, height=70)\r\n        secondbutton.place(x=350, y=200, width=300, height=70)\r\n        thirdbutton.place(x=690, y=200, width=300, height=70)\r\n        fourthbutton.place(x=10, y = 450, width=170, height=35)\r\n        fifthbutton.place(x=825, y=450, width=170, height=35 )\r\n\r\n        self.lbl = Label(root, text=\"Игра начинается\", bg=\"#B2DFDB\", font=(\"Calibri\", 30, \"bold\"))\r\n        self.lbl.place(x=355, y=100)\r\n        \r\n        self.lbl2 = Label(root, justify=\"left\", font=(\"Calibri\", 20), text=f\"Побед: {self.win}\\nПроигрышей:\"f\" {self.lose}\\nНичей: {self.drow}\",bg=\"#E1BEE7\")\r\n        self.lbl2.place(x=5, y=5)\r\n        \r\n        self.lbl3 = Label(root, text=\"Камень, ножницы или бумага ?\", bg=\"#66FFCC\", font=(\"Calibri\", 30, \"bold\"))\r\n        self.lbl3.place(x=220, y=20)\r\n        \r\n    def btn_click(self, choise):\r\n        enemy_choise = rdm.randint(1, 3)\r\n        i = np.arange(1,self.count+1)\r\n        j = np.empty((self.count), dtype=int)\r\n        \r\n        if (choise == 4):            \r\n            plt.title(\"График\")\r\n            plt.xlabel(\"X\")\r\n            plt.ylabel(\"Y\")\r\n            plt.plot(i, j, color =\"green\")\r\n            plt.show()\r\n\r\n        elif (choise == enemy_choise):\r\n            self.drow += 1\r\n            self.count +=1\r\n            j = np.append(j,[0])\r\n            self.lbl.configure(text=\"Ничья\", bg=\"#FFC233\")\r\n            self.lbl.place(x=440, y=100)\r\n            \r\n        elif (choise == 1 and enemy_choise == 2) or (choise == 2 and enemy_choise == 3) or (choise == 3 and enemy_choise == 1):\r\n            self.win += 1\r\n            self.count +=1\r\n            j = np.append(j,[1])\r\n            self.lbl.configure(text=\"Победа\", bg=\"#8BC34A\")\r\n            self.lbl.place(x=430, y=100)\r\n            \r\n        elif (choise == 0):\r\n            self.win = self.drow = self.lose = self.count = 0\r\n            \r\n        else:\r\n            self.lose += 1\r\n            self.count +=1\r\n            j = np.append(j,[-1])\r\n            self.lbl.configure(text=\"Проигрыш\", bg =\"#D50000\")\r\n            self.lbl.place(x=410, y=100)\r\n\r\n        self.lbl2.configure(text=f\"Побед: {self.win}\\nПроигрышей:\"f\" {self.lose}\\nНичей: {self.drow}\")\r\n        del enemy_choise\r\n\r\nif __name__ == '__main__':\r\n    root = Tk()\r\n    root.geometry(\"1000x500\")\r\n    root.title(\"Камень, ножницы, бумага\")\r\n    root.resizable(False, False)\r\n    root[\"bg\"] = \"#FFE0B2\"\r\n    app = Main(root)\r\n    app.pack()\r\n    root.mainloop()\r\n","repo_name":"RageDisplay/Python","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13652790422","text":"#!/usr/bin/env python3\n\n\"\"\"\nSome common transforms for use with time series data.\n\"\"\"\n\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter1d\nfrom scipy.signal import butter, gaussian, sosfiltfilt\n\n\ndef instantaneous_frequency(\n    signal: np.ndarray,\n    samplerate: int,\n    smoothing_window: int,\n) -> np.ndarray:\n    \"\"\"\n    Compute the instantaneous frequency of a signal that is approximately\n    sinusoidal and symmetric around 0.\n\n    Parameters\n    ----------\n    signal : np.ndarray\n        Signal to compute the instantaneous frequency from.\n    samplerate : int\n        Samplerate of the signal.\n    smoothing_window : int\n        Window size for the gaussian filter.\n    interpolation : str, optional\n        Interpolation method to use during resampling. Should be either 'linear' or 'cubic'. Default is 'linear'.\n\n    Returns\n    -------\n    tuple[np.ndarray, np.ndarray]\n\n    \"\"\"\n    # calculate instantaneous frequency with zero crossings\n    roll_signal = np.roll(signal, shift=1)\n    time_signal = np.arange(len(signal)) / samplerate\n    period_index = np.arange(len(signal))[(roll_signal < 0) & (signal >= 0)][1:-1]\n\n    upper_bound = np.abs(signal[period_index])\n    lower_bound = np.abs(signal[period_index - 1])\n    upper_time = np.abs(time_signal[period_index])\n    lower_time = np.abs(time_signal[period_index - 1])\n\n    # create ratio\n    lower_ratio = lower_bound / (lower_bound + upper_bound)\n\n    # appy to time delta\n    time_delta = upper_time - lower_time\n    true_zero = lower_time + lower_ratio * time_delta\n\n    # create new time array\n    instantaneous_frequency_time = true_zero[:-1] + 0.5 * np.diff(true_zero)\n\n    # compute frequency\n    instantaneous_frequency = gaussian_filter1d(1 / np.diff(true_zero), smoothing_window)\n\n    # Resample the frequency using specified interpolation method to match the dimensions of the input array\n    orig_len = len(signal)\n    old_len = len(instantaneous_frequency)\n    old_x = np.arange(0, old_len)\n    new_x = np.linspace(0, old_len, orig_len)\n\n    freq = np.interp(new_x, old_x, instantaneous_frequency)\n\n    return freq\n\n\ndef envelope(\n    signal: np.ndarray, samplerate: float, cutoff_frequency: float\n) -> np.ndarray:\n    \"\"\"Calculate the envelope of a signal using a lowpass filter.\n\n    Parameters\n    ----------\n    signal : np.ndarray\n        The signal to calculate the envelope of\n    samplingrate : float\n        The sampling rate of the signal\n    cutoff_frequency : float\n        The cutoff frequency of the lowpass filter\n\n    Returns\n    -------\n    np.ndarray\n        The envelope of the signal\n    \"\"\"\n    sos = butter(2, cutoff_frequency, \"lowpass\", fs=samplerate, output=\"sos\")\n    envelope = np.sqrt(2) * sosfiltfilt(sos, np.abs(signal))\n\n    old_x = np.arange(0, len(envelope))\n    new_x = np.linspace(0, len(envelope), len(signal))\n    envelope = np.interp(new_x, old_x, envelope)\n\n    return envelope\n","repo_name":"weygoldt/gridprepro","sub_path":"gridtools/utils/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35030747669","text":"from app import app\nfrom flask import render_template, redirect, url_for, flash\nfrom app.forms import ContactForm, Signupform\nfrom app.models import User\n\n@app.route(\"/\")\ndef index():\n    return render_template('index.html')\n\n@app.route('/signup', methods=[\"GET\", \"POST\"])\ndef signup():\n    form = Signupform()\n    if form.validate_on_submit():\n        username = form.username.data\n        email = form.email.data\n        password = form.password.data\n        check_user = User.query.filter((email == email) | (username == username)).all\n        if check_user:\n            flash(\"This user already exist\")\n            return redirect(url_for(\"signup\"))\n        new_user = User(\n            email = email, \n            password = password, \n            username = username)\n        if new_user:\n            flash(f\"Thank {username}/{email} for signing up\")\n            return redirect(url_for('index'))\n    return render_template(\"signup.html\", form = form)\n\n@app.route('/posts', methods=['GET', 'POST'])\ndef posts():\n    form = ContactForm()\n    if form.validate_on_submit():\n        first_name = form.first_name.data\n        last_name = form.last_name.data\n        mobile_number = form.mobile_phone.data\n        office_number = form.office_phone.data\n        new_phonebook = User.phonebook(\n            first_name = first_name,\n            last_name = last_name,\n            mobile_number = mobile_number,\n            office_number = office_number\n            )\n        if new_phonebook:\n            flash('Contact has been added')\n            return redirect(url_for('index'))\n    return render_template('posts.html', form = form)","repo_name":"Fangmbeng/phonebook_flask","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70538928889","text":"import pygame\nimport enemy\nimport constants\nfrom setup import screen, xSize, ySize\nfrom floor_generation import xScreenScaling, yScreenScaling\n\ndef InitFightScreen(enemy_difficulty):\n    screen.fill((0, 0, 0))\n    textBarSize = yScreenScaling * 3\n    curr_enemy = InitEnemy(enemy_difficulty)\n    DisplayInventory(textBarSize)\n    pygame.draw.rect(screen, constants.MAIN_TEXT_SCREEN, (0, ySize - textBarSize, xSize, textBarSize))\n    pygame.draw.rect(screen, constants.MAIN_TEXT_BORDER, (0, ySize - textBarSize, xSize, textBarSize), 10)\n    enemy_encounter_string = \"Enemy encounter! A {} approaches!\".format(curr_enemy.enemyType)\n    initial_enemy_encounter_text = constants.ROBOTO_30.render(enemy_encounter_string, False, (0, 0, 0))\n    screen.blit(initial_enemy_encounter_text, (round(xScreenScaling / 3), ySize - textBarSize + 25))\n    screen.blit(constants.SPRITE_ARROW, (xSize - 95, ySize - 95))\n\n    # testing text, instant win condition for testing\n    test_text = constants.ROBOTO_30.render(\"win\", False, (0, 0, 0))\n    screen.blit(test_text, (0, ySize - 40))\n\ndef InitEnemy(enemy_difficulty):\n    current_enemy = enemy.Enemy(enemy_difficulty)\n    current_enemy_sprite = current_enemy.sprite\n    screen.blit(current_enemy_sprite, (100, 100))\n    return current_enemy\n    \ndef DisplayInventory(textBarSize):\n    inventory_bar_size = yScreenScaling * 1.25\n    inventory_top_pos = ySize - textBarSize - inventory_bar_size - 8\n    pygame.draw.rect(screen, constants.INVENTORY_COLOR, (0, inventory_top_pos, xSize, inventory_bar_size))\n    pygame.draw.rect(screen, constants.INVENTORY_BORDER_COLOR, (0, inventory_top_pos, xSize, inventory_bar_size), 10)\n    screen.blit(constants.SPRITE_DAGGER, (30, inventory_top_pos + 10))","repo_name":"ken12321/room-algorithm","sub_path":"fight_scene.py","file_name":"fight_scene.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19715332079","text":"import requests\n\nurl = 'https://api.fpt.ai/hmi/asr/general'\npayload = open('voices/test.mp3', 'rb').read()\nheaders = {\n    'api-key': 'lNbxag5GVRA3ZYBYzjeijcmY2BSAChdr'\n}\n\nresponse = requests.post(url=url, data=payload, headers=headers)\n\nprint(response.json())\n","repo_name":"giangcse/VideoSearchEngine","sub_path":"stt.py","file_name":"stt.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28443809110","text":"from tkinter import ttk\nfrom tkinter import *\nfrom servis.kalendar.kalendar_servis import KalendarServis\n\n\nclass PrikazZauzeca(object):\n\n    def __init__(self, root, korisnicko_ime_lekara):\n        self._root = root\n        self.treeview = ttk.Treeview(self._root)\n        self.scroll = ttk.Scrollbar(self._root, orient='vertical', command=self.treeview.yview)\n        self.scroll.pack(side='right', fill='y')\n        self.treeview.configure(yscrollcommand=self.scroll.set)\n        self._korisnicko_ime_lekara = korisnicko_ime_lekara\n        self.napravi_treeview()\n\n    def napravi_treeview(self):\n        self.treeview[\"columns\"] = [\"datum\", \"vreme\", \"broj_slotova\", \"soba\", \"pacijent\"]\n        self.treeview[\"show\"] = \"headings\"\n        self.treeview.heading(\"datum\", text=\"Datum\")\n        self.treeview.heading(\"vreme\", text=\"Vreme\")\n        self.treeview.heading(\"broj_slotova\", text=\"Vreme trajanja\")\n        self.treeview.heading(\"soba\", text=\"Soba\")\n        self.treeview.heading(\"pacijent\", text=\"Pacijent\")\n        self.treeview.pack()\n        self.treeview.config(height=13)\n        self.__popuni_treeview()\n\n    def __popuni_treeview(self):\n        index = iid = 0\n        lista_dogadjaja = KalendarServis().dobavi_listu_dogadjaja()\n        for dogadjaj in lista_dogadjaja:\n            if self._korisnicko_ime_lekara in dogadjaj.spisak_doktora:\n                dog = (dogadjaj.datum, dogadjaj.vreme_pocetka_str, str(dogadjaj.broj_termina * 30) + \" minuta\",\n                       \"sprat: \" + dogadjaj.sprat + \" | soba: \" + dogadjaj.broj_prostorije,\n                       dogadjaj.pacijent)\n                self.treeview.insert(\"\", index, iid, values=dog)\n                index = iid = index + 1\n\n\nif __name__ == '__main__':\n    root = Tk()\n    PrikazZauzeca(root, \"goran431\")\n    root.mainloop()\n","repo_name":"dragicevic19/usi-hospital","sub_path":"gui/prikaz_entiteta/prikaz_zauzeca.py","file_name":"prikaz_zauzeca.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42696373367","text":"script_version = 1.0\n\n##\n# Import the required modules\nimport os\nimport sys\nimport argparse\nimport vtk\nimport vtkbone\nimport time\nimport numpy as np\nimport SimpleITK as sitk\nfrom scipy import stats\nfrom datetime import date\nfrom collections import OrderedDict\nfrom vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk\n\nimport ogo.dat.MassAttenuationTables as mat\nimport ogo.dat.OgoMasterLabels as lb\nfrom ogo.calib.internal_calibration import InternalCalibration\nfrom ogo.calib.mindways_calibration import MindwaysCalibration\nfrom ogo.calib.standard_calibration import StandardCalibration\nimport ogo.util.Helper as ogo\nfrom ogo.util.echo_arguments import echo_arguments\nfrom ogo.util.write_txt import write_txt\n\n\n# L4 VERTEBRA ---------------------------------------------------------------------------\ndef vertebra(input_image, input_mask, output_model, vertebra, iso_resolution, overwrite, func):\n    ogo.message('Generate an FE model of the ' + vertebra + ' vertebra.')\n\n    # Check if output exists and should overwrite\n    if os.path.isfile(output_model) and not overwrite:\n        result = input('File \\\"{}\\\" already exists. Overwrite? [y/n]: '.format(output_model))\n        if result.lower() not in ['y', 'yes']:\n            ogo.message('Not overwriting. Exiting...')\n            os.sys.exit()\n\n    if not output_model.lower().endswith('.n88model'):\n        os.sys.exit('[ERROR] Input must be .n88model file: \\\"{}\\\"'.format(output_model))\n\n    # Read input image\n    if not os.path.isfile(input_image):\n        os.sys.exit('[ERROR] Cannot find file \\\"{}\\\"'.format(input_image))\n\n    if not (input_image.lower().endswith('.nii') or input_image.lower().endswith('.nii.gz')):\n        os.sys.exit('[ERROR] Input must be type NIFTI file: \\\"{}\\\"'.format(input_image))\n\n    ogo.message('Reading calibrated input CT image:')\n    ogo.message('      \\\"{}\\\"'.format(input_image))\n    ct_reader = vtk.vtkNIFTIImageReader()\n    ct_reader.SetFileName(input_image)\n    ct_reader.Update()\n    ct = ct_reader.GetOutput()\n    \n    # Read input mask\n    if not os.path.isfile(input_mask):\n        os.sys.exit('[ERROR] Cannot find file \\\"{}\\\"'.format(input_mask))\n\n    if not (input_mask.lower().endswith('.nii') or input_mask.lower().endswith('.nii.gz')):\n        os.sys.exit('[ERROR] Input must be type NIFTI file: \\\"{}\\\"'.format(input_mask))\n\n    ogo.message('Reading input mask:')\n    ogo.message('      \\\"{}\\\"'.format(input_mask))\n    mask_reader = vtk.vtkNIFTIImageReader()\n    mask_reader.SetFileName(input_mask)\n    mask_reader.Update()\n    mask = mask_reader.GetOutput()\n    \n    # Check that mask contains label\n    vertebra_label = ogo.get_label(vertebra) # Gets label ID from name of label\n    if vertebra_label not in vtk_to_numpy(mask.GetPointData().GetScalars()).ravel():\n        os.sys.exit('[ERROR] Mask does not contain label {} for \\\"{}\\\" vertebra.'.format(vertebra_label, vertebra))\n\n    # Extract isosurface of bone mask\n    thres = vtk.vtkImageThreshold()\n    thres.SetInputConnection(mask_reader.GetOutputPort())\n    thres.ThresholdBetween(vertebra_label, vertebra_label)\n    thres.ReplaceInOn()\n    thres.SetInValue(1)\n    thres.ReplaceOutOn()\n    thres.SetOutValue(0)\n    thres.SetOutputScalarTypeToUnsignedChar()\n    \n    mcubes = vtk.vtkImageMarchingCubes()\n    mcubes.SetInputConnection(thres.GetOutputPort())\n    mcubes.SetValue(1, 1.0)\n    mcubes.Update()\n    \n    # Use iterative closest points\n    reference_bone = vtk.vtkPolyDataReader()\n    reference_bone.SetFileName(\"/Users/skboyd/Desktop/ML/code/Ogo/ogo/dat/L4_BODY_SPINE_COMPRESSION_REF.vtk\")\n    reference_bone.Update()\n    \n    icp = vtk.vtkIterativeClosestPointTransform()\n    icp.SetTarget(reference_bone.GetOutput())\n    icp.SetSource(mcubes.GetOutput())\n    icp.StartByMatchingCentroidsOn()\n    icp.GetLandmarkTransform().SetModeToRigidBody()\n    icp.SetMeanDistanceModeToRMS()\n    icp.SetMaximumMeanDistance(0.05)\n    icp.CheckMeanDistanceOn()\n    icp.SetMaximumNumberOfLandmarks(250)\n    icp.SetMaximumNumberOfIterations(75)\n    icp.Update()\n    \n    transform = vtk.vtkTransform()\n    transform.SetMatrix(icp.GetMatrix())\n    transform.Update()\n\n    transformFilter = vtk.vtkTransformFilter()\n    transformFilter.SetInputConnection( mcubes.GetOutputPort() )\n    transformFilter.SetTransform( transform )\n    transformFilter.Update()\n    \n    #reslice = vtk.vtkImageReslice()\n    #reslice.SetInputData(vtk_image)\n    #reslice.SetInterpolationModeToCubic()\n    #reslice.SetResliceTransform(transform)\n    #reslice.AutoCropOutputOn()\n    #reslice.Update()\n    \n    \n    #print(icp.GetMatrix())\n    \n    writer = vtk.vtkPolyDataWriter()\n    writer.SetInputConnection(transformFilter.GetOutputPort())\n    writer.SetFileName(\"/Users/skboyd/Desktop/ML/code/Ogo/ogo/dat/tmp_transform.vtk\")\n    writer.Update()\n    \n    \n    exit()\n    # Resample to isotropic voxel size\n    ogo.message('Resample mask.')\n    mask_iso = ogo.isotropicResampling(mask, iso_resolution, 'mask')\n    ogo.message('Resample ct.')\n    ct_iso = ogo.isotropicResampling(ct, iso_resolution, 'ct')\n\n    # Write output n88model file\n    ogo.message('Writing n88model file:')\n    ogo.message('      \\\"{}\\\"'.format(output_model))\n    writer = vtkbone.vtkboneN88ModelWriter()\n    # writer.SetInputData(model)\n    writer.SetFileName(output_model)\n    # writer.Update()\n\n    ogo.message('Done generating finite element file.')\n\n\n# FEMUR ---------------------------------------------------------------------------------\ndef femur(input_image, input_mask, output_model, side, iso_resolution, overwrite, func):\n    ogo.message('Generate an FE model of the ' + side + ' femur.')\n\n    # Check if output exists and should overwrite\n    if os.path.isfile(output_model) and not overwrite:\n        result = input('File \\\"{}\\\" already exists. Overwrite? [y/n]: '.format(output_model))\n        if result.lower() not in ['y', 'yes']:\n            ogo.message('Not overwriting. Exiting...')\n            os.sys.exit()\n\n    if not output_model.lower().endswith('.n88model'):\n        os.sys.exit('[ERROR] Input must be .n88model file: \\\"{}\\\"'.format(output_model))\n\n    # Read input image\n    if not os.path.isfile(input_image):\n        os.sys.exit('[ERROR] Cannot find file \\\"{}\\\"'.format(input_image))\n\n    if not (input_image.lower().endswith('.nii') or input_image.lower().endswith('.nii.gz')):\n        os.sys.exit('[ERROR] Input must be type NIFTI file: \\\"{}\\\"'.format(input_image))\n\n    ogo.message('Reading calibrated input CT image:')\n    ogo.message('      \\\"{}\\\"'.format(input_image))\n    ct = sitk.ReadImage(input_image)\n\n    # Read input mask\n    if not os.path.isfile(input_mask):\n        os.sys.exit('[ERROR] Cannot find file \\\"{}\\\"'.format(input_mask))\n\n    if not (input_mask.lower().endswith('.nii') or input_mask.lower().endswith('.nii.gz')):\n        os.sys.exit('[ERROR] Input must be type NIFTI file: \\\"{}\\\"'.format(input_mask))\n\n    ogo.message('Reading input mask:')\n    ogo.message('      \\\"{}\\\"'.format(input_mask))\n    mask = sitk.ReadImage(input_mask)\n\n    # Check for the appropriate label in mask\n    filt = sitk.LabelStatisticsImageFilter()\n    filt.Execute(ct, mask)\n\n    if side in 'left':\n        femur = 'Femur Left'\n    else:\n        femur = 'Femur Right'\n    femur_label = ogo.get_label(femur)\n    if femur_label not in filt.GetLabels():\n        os.sys.exit('[ERROR] Mask does not contain label {} for \\\"{}\\\".'.format(femur_label, femur))\n\n    # Resample to isotropic voxel size\n    ogo.message('Resample mask.')\n    mask_iso = ogo.isotropicResampling(mask, iso_resolution, 'mask')\n    ogo.message('Resample ct.')\n    ct_iso = ogo.isotropicResampling(ct, iso_resolution, 'ct')\n\n    # sitk.WriteImage(mask_iso, '/Users/skboyd/Desktop/mask.nii.gz')\n    # sitk.WriteImage(ct_iso, '/Users/skboyd/Desktop/ct.nii')\n\n    # Write output n88model file\n    ogo.message('Writing n88model file:')\n    ogo.message('      \\\"{}\\\"'.format(output_model))\n    writer = vtkbone.vtkboneN88ModelWriter()\n    # writer.SetInputData(model)\n    writer.SetFileName(output_model)\n    # writer.Update()\n\n    ogo.message('Done generating finite element file.')\n\n    # ogo.message('  {:>27s} {:8s}'.format('---------------------------','--------'))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Energy [keV]:',calib.effective_energy))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Max R^2:',calib.max_r2))\n    # ogo.message('  {:>27s} {:8s}'.format('---------------------------','--------'))\n    # ogo.message('  {:>27s}'.format('Mass Attenuation [cm2/g]:'))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Adipose ',calib.adipose_mass_attenuation))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Air ',calib.air_mass_attenuation))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Blood ',calib.blood_mass_attenuation))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Cortical Bone ',calib.bone_mass_attenuation))\n    # ogo.message('  {:>27s} {:8.3f}'.format('Skeletal Muscle ',calib.muscle_mass_attenuation))\n    # ogo.message('  {:>27s} {:8s}'.format('---------------------------','--------'))\n\n\ndef main():\n    # Setup description\n    description = '''\n\nUNDER CONSTRUCTION. This script is not functional yet. August 30, 2022.\n\nUses a calibrated CT scan and mask to generate an input file for finite \nelement modelling. There are two main types of FE model supported: an L4\nvertebral body or a femur (left or right).\n\nOutput is an .n88model file suitable for solving using FAIM software. \nFor a free copy of FAIM software follow the installation instructions at:\n\nhttps://bonelab.github.io/n88\n \nPlease cite:\nMichalski AS, Besler BA, Burt LA, Boyd SK, 2021. Opportunistic CT screening \npredicts individuals at risk of major osteoporotic fracture. Osteoporos Int \n32, 1639-1649.\n\nMichalski AS, Besler BA, Michalak GJ, Boyd SK, 2020. CT-based internal density \ncalibration for opportunistic skeletal assessment using abdominal CT scans. \nMed Eng Phys 78, 55-63.\n\n'''\n\n    epilog = '''\nExample calls: \nogoGenerateFEM vertebra image.nii.gz mask.nii.gz vertL4.n88model\nogoGenerateFEM femur image.nii.gz mask.nii.gz --side left femurL.n88model\n\nogoGenerateFEM vertebra /Users/skboyd/Desktop/ML/test/retro.nii /Users/skboyd/Desktop/ML/test/retro_mask.nii.gz vertL4.n88model\n\nogoGenerateFEM femur /Users/skboyd/Desktop/ML/test/retro.nii /Users/skboyd/Desktop/ML/test/retro_mask.nii.gz --side left femurL.n88model\n\n'''\n\n    # Setup argument parsing\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawTextHelpFormatter,\n        prog=\"ogoGenerateFEM\",\n        description=description,\n        epilog=epilog\n    )\n    subparsers = parser.add_subparsers()\n\n    # Vertebra\n    parser_vertebra = subparsers.add_parser('vertebra')\n    parser_vertebra.add_argument('input_image', help='Input image file (*.nii, *.nii.gz)')\n    parser_vertebra.add_argument('input_mask',\n                                 help='Image mask of rods for input image or asynchronous image (*.nii, *.nii.gz)')\n    parser_vertebra.add_argument('output_model', help='Output N88 model file (*.n88model)')\n    parser_vertebra.add_argument('--vertebra', default='L4', choices=['L1', 'L2', 'L3', 'L4', 'L5'],\n                                 help='Specify vertebra for analysis (default: %(default)s)')\n    parser_vertebra.add_argument(\"--iso_resolution\", type=float, default=1.0,\n                                 help=\"Set the isotropic voxel size [mm]. (default: %(default)s [mm])\")\n    parser_vertebra.add_argument('--overwrite', action='store_true', help='Overwrite output without asking')\n    parser_vertebra.set_defaults(func=vertebra)\n\n    # Femur\n    parser_femur = subparsers.add_parser('femur')\n    parser_femur.add_argument('input_image', help='Input image file (*.nii, *.nii.gz)')\n    parser_femur.add_argument('input_mask', help='Input image mask file (*.nii, *.nii.gz)')\n    parser_femur.add_argument('output_model', help='Output N88 model file (*.n88model)')\n    parser_femur.add_argument('--side', default='left', choices=['left', 'right'],\n                              help='Specify left or right femur (default: %(default)s)')\n    parser_femur.add_argument(\"--iso_resolution\", type=float, default=1.0,\n                              help=\"Set the isotropic voxel size [mm]. (default: %(default)s [mm])\")\n    parser_femur.add_argument('--overwrite', action='store_true', help='Overwrite output without asking')\n    parser_femur.set_defaults(func=femur)\n\n    # Parse and display\n    args = parser.parse_args()\n    print(echo_arguments('GenerateFEM', vars(args)))\n\n    # Run program\n    args.func(**vars(args))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Bonelab/Ogo","sub_path":"ogo/cli/GenerateFEM.py","file_name":"GenerateFEM.py","file_ext":"py","file_size_in_byte":12568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14393756191","text":"import torch\nimport torch.nn as nn\n\n\nclass Conv2d(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n                 padding=0, padding_mode='zeros', bias=True, residual=False):\n        super(Conv2d, self).__init__()\n        self.conv_block = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, kernel_size, stride,\n                      padding, padding_mode=padding_mode, bias=bias),\n            nn.BatchNorm2d(out_channels)\n        )\n        self.residual = residual\n        self.act = nn.ReLU()\n\n    def forward(self, x):\n        out = self.conv_block(x)\n        if self.residual:\n            out += x\n        out = self.act(out)\n        return out\n\n\nclass ResnetBlock(nn.Module):\n    def __init__(self, channel, padding_mode, norm_layer=nn.BatchNorm2d, bias=False):\n        super().__init__()\n        if padding_mode not in ['reflect', 'zero']:\n            raise NotImplementedError(f\"{padding_mode} is not supported!\")\n\n        self.block = nn.Sequential(\n            nn.Conv2d(channel, channel, kernel_size=3, padding=1, padding_mode=padding_mode, bias=bias),\n            norm_layer(channel)\n        )\n        self.act = nn.ReLU()\n\n    def forward(self, x):\n        out = self.block(x)\n        out = out + x\n        out = self.act(out)\n        return out\n\n\nclass ResidualBlocks(nn.Module):\n    def __init__(self, channel, n_blocks=6):\n        super().__init__()\n        model = []\n        for i in range(n_blocks):  # add ResNet blocks\n            model += [ResnetBlock(channel, padding_mode='reflect')]\n\n        self.module = nn.Sequential(*model)\n\n    def forward(self, x):\n        return self.module(x)\n\n\nclass SelfAttentionBlock(nn.Module):\n\n    def __init__(self, in_dim):\n        super().__init__()\n        self.feature_dim = in_dim // 8\n        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=self.feature_dim, kernel_size=1)\n        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=self.feature_dim, kernel_size=1)\n        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n        self.gamma = nn.Parameter(torch.zeros(1))\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x):\n        B, C, H, W = x.size()\n        _query = self.query_conv(x).view(B, -1, H * W).permute(0, 2, 1)  # B x C x (H'*W')\n        _key = self.key_conv(x).view(B, -1, H * W)  # B x C x (H'*W')\n        attn_matrix = torch.bmm(_query, _key)\n        attention = self.softmax(attn_matrix)  # B x (H'*W') x (H'*W')\n        _value = self.value_conv(x).view(B, -1, H * W)  # B X C X (H * W)\n\n        out = torch.bmm(_value, attention.permute(0, 2, 1))\n        out = out.view(B, C, H, W)\n\n        out = self.gamma * out + x\n        return out\n\n\nclass ContextAwareAttentionBlock(nn.Module):\n\n    def __init__(self, in_channels, hidden_dim=128):\n        super().__init__()\n        self.self_attn = SelfAttentionBlock(in_channels)\n        self.fc = nn.Linear(in_channels, hidden_dim)\n        self.context_vector = nn.Linear(hidden_dim, 1, bias=False)\n        self.softmax = nn.Softmax(dim=1)\n\n    def forward(self, style_features):\n        B, C, H, W = style_features.size()\n        h = self.self_attn(style_features)\n        h = h.permute(0, 2, 3, 1).reshape(-1, C)\n        h = torch.tanh(self.fc(h))  # (B*H*W) x self.hidden_dim\n        h = self.context_vector(h)  # (B*H*W) x 1\n        attention_score = self.softmax(h.view(B, H * W)).view(B, 1, H, W)  # B x 1 x H x W\n        return torch.sum(style_features * attention_score, dim=[2, 3])  # B x C\n\n\nclass LayerAttentionBlock(nn.Module):\n    \"\"\"from FTransGAN\n    \"\"\"\n\n    def __init__(self, in_channels):\n        super().__init__()\n        self.in_channels = in_channels\n        self.width_feat = 4\n        self.height_feat = 4\n        self.fc = nn.Linear(self.in_channels * self.width_feat * self.height_feat, 3)\n        self.softmax = nn.Softmax(dim=1)\n\n    def forward(self, style_features, style_features_1, style_features_2, style_features_3, B, K):\n        style_features = torch.mean(style_features.view(B, K, self.in_channels, self.height_feat, self.width_feat), dim=1)\n        style_features = style_features.view(B, -1)\n        weight = self.softmax(self.fc(style_features))\n\n        style_features_1 = torch.mean(style_features_1.view(B, K, self.in_channels), dim=1)\n        style_features_2 = torch.mean(style_features_2.view(B, K, self.in_channels), dim=1)\n        style_features_3 = torch.mean(style_features_3.view(B, K, self.in_channels), dim=1)\n\n        style_features = (style_features_1 * weight.narrow(1, 0, 1) +\n                          style_features_2 * weight.narrow(1, 1, 1) +\n                          style_features_3 * weight.narrow(1, 2, 1))\n        style_features = style_features.view(B, self.in_channels, 1, 1)\n        return style_features\n\n\nclass StyleAttentionBlock(nn.Module):\n    \"\"\"from FTransGAN\n    \"\"\"\n\n    def __init__(self, in_channels):\n        super().__init__()\n        self.num_local_attention = 3\n        for module_idx in range(1, self.num_local_attention + 1):\n            self.add_module(f\"local_attention_{module_idx}\",\n                            ContextAwareAttentionBlock(in_channels))\n\n        for module_idx in range(1, self.num_local_attention):\n            self.add_module(f\"downsample_{module_idx}\",\n                            Conv2d(in_channels, in_channels,\n                                   kernel_size=3, stride=2, padding=1, bias=False))\n\n        self.add_module(f\"layer_attention\", LayerAttentionBlock(in_channels))\n\n    def forward(self, x, B, K):\n        feature_1 = self.local_attention_1(x)\n\n        x = self.downsample_1(x)\n        feature_2 = self.local_attention_2(x)\n\n        x = self.downsample_2(x)\n        feature_3 = self.local_attention_3(x)\n\n        out = self.layer_attention(x, feature_1, feature_2, feature_3, B, K)\n\n        return out\n","repo_name":"deepkyu/multilingual-font-style-transfer","sub_path":"models/module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":5875,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"8140714000","text":"##get a list of top 100 movies from imdb\n\nimport requests\nimport json\n\nimdbKey = \"k_aaaaaaaa\"\nimdbAPIURL = \"imdb-api.com\"\n\n# topMovies = requests.get('http://' + imdbAPIURL + '/en/API/Top250Movies/' + imdbKey)\n\n\n# ##save as json file\n# with open('topMovies.json', 'w') as outfile:\n#     json.dump(topMoviesJson, outfile)\n\ntopMoviesJson = json.load(open('topMovies.json'))\n\n##get a list of just titles\ntopMovies = []\nfor movie in topMoviesJson['items']:\n    topMovies.append(movie['fullTitle'])\nprint(topMovies)\n\n##save top movies as text tile\nwith open('topMovies.txt', 'w') as outfile:\n    for movie in topMovies:\n        outfile.write(movie + '\\n')\n        ","repo_name":"BlaineCowen/MoviesAbriged","sub_path":"getMovieList.py","file_name":"getMovieList.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40674413280","text":"import logging\n\nimport numpy as np\nimport pycuda.autoinit  # noqa: F401\nimport pycuda.driver as cuda  # noqa: F401\nimport pycuda.gpuarray as gpuarray  # noqa: F401\nfrom cufinufft import cufinufft\n\nfrom aspire.nufft import Plan\nfrom aspire.utils import complex_type\n\nlogger = logging.getLogger(__name__)\n\n\nclass CufinufftPlan(Plan):\n    def __init__(self, sz, fourier_pts, epsilon=1e-8, ntransforms=1, **kwargs):\n        \"\"\"\n        A plan for non-uniform FFT in 2D or 3D.\n\n        :param sz: A tuple indicating the geometry of the signal\n        :param fourier_pts: The points in Fourier space where the Fourier transform is to be calculated,\n            arranged as a dimension-by-K array. These need to be in the range [-pi, pi] in each dimension.\n        :param epsilon: The desired precision of the NUFFT\n        :param ntransforms: Optional integer indicating if you would like to compute a batch of `ntransforms`\n        transforms.  Implies vol_f.shape is (..., `ntransforms`). Defaults to 0 which disables batching.\n        \"\"\"\n\n        # Passing \"ntransforms\" > 1 expects one large higher dimensional array later.\n        self.ntransforms = ntransforms\n\n        # Workaround cufinufft A100 singles issue\n        # ASPIRE-Python/703\n        # Cast to doubles.\n        self._original_dtype = fourier_pts.dtype\n        fourier_pts = fourier_pts.astype(np.float64, copy=False)\n\n        # Basic dtype passthough.\n        dtype = fourier_pts.dtype\n        if dtype == np.float64 or dtype == np.complex128:\n            self.dtype = np.float64\n            self.complex_dtype = np.complex128\n        elif dtype == np.float32 or dtype == np.complex64:\n            self.dtype = np.float32\n            self.complex_dtype = np.complex64\n        else:\n            raise RuntimeError(\"Unsupported dtype encountered\")\n\n        self.sz = sz\n        self.dim = len(sz)\n\n        if not fourier_pts.flags.c_contiguous:\n            logger.debug(\n                \"cufinufft has caught a non C_CONTIGUOUS array,\"\n                \" `fourier_pts` will be copied to C_CONTIGUOUS.\"\n            )\n        self.fourier_pts = np.ascontiguousarray(\n            np.mod(fourier_pts + np.pi, 2 * np.pi) - np.pi, dtype=self.dtype\n        )\n\n        self.num_pts = fourier_pts.shape[1]\n        self.epsilon = max(epsilon, np.finfo(self.dtype).eps)\n\n        self._transform_plan = cufinufft(\n            2, self.sz, self.ntransforms, self.epsilon, -1, dtype=self.dtype\n        )\n\n        self.adjoint_opts = dict()\n        if self.dtype is np.float64 and self.dim == 3 and self.epsilon < 1e3:\n            # Note this is an algorithmic implementation dictated by shmem.\n            logger.info(\n                \"Converting cufinufft gpu_method=1 from default of 2 for 3D1 transform,\"\n                f\"to support computation in double precision with tol={self.epsilon}.\"\n            )\n            self.adjoint_opts[\"gpu_method\"] = 1\n\n        self._adjoint_plan = cufinufft(\n            1,\n            self.sz,\n            self.ntransforms,\n            self.epsilon,\n            1,\n            dtype=self.dtype,\n            **self.adjoint_opts,\n        )\n\n        # Note, I store self.fourier_pts_gpu so the GPUArrray life\n        #   is tied to instance, instead of this method.\n        self.fourier_pts_gpu = gpuarray.to_gpu(self.fourier_pts)\n\n        self._transform_plan.set_pts(*self.fourier_pts_gpu)\n        self._adjoint_plan.set_pts(*self.fourier_pts_gpu)\n\n    def transform(self, signal):\n        \"\"\"\n        Compute the NUFFT transform using this plan instance.\n\n        :param signal: Signal to be transformed. For a single transform,\n        this should be a a 1, 2, or 3D array matching the plan `sz`.\n        For a batch, signal should have shape `(*sz, ntransforms)`.\n\n        :returns: Transformed signal of shape `num_pts` or\n        `(ntransforms, num_pts)`.\n        \"\"\"\n\n        # Check we're not forcing a dtype workaround for ASPIRE-Python/703,\n        #   then check if we have a dtype mismatch.\n        # This avoids false positive complaint for the workaround.\n        if (self._original_dtype == self.dtype) and not (\n            signal.dtype == self.dtype or signal.dtype == self.complex_dtype\n        ):\n            logger.warning(\n                \"Incorrect dtypes passed to (a)nufft.\"\n                \" In the future this will be an error.\"\n            )\n\n        sig_shape = signal.shape\n        res_shape = self.num_pts\n        # Note, there is a corner case for ntransforms == 1.\n        if self.ntransforms > 1 or (\n            self.ntransforms == 1 and len(signal.shape) == self.dim + 1\n        ):\n            assert (\n                len(signal.shape) == self.dim + 1\n            ), f\"For multiple transforms, {self.dim}D signal should be a {self.ntransforms} element stack of {self.sz}.\"\n\n            assert (\n                signal.shape[0] == self.ntransforms\n            ), \"For multiple transforms, signal stack length should match ntransforms {self.ntransforms}.\"\n\n            sig_shape = signal.shape[1:]  # order...\n            res_shape = (self.ntransforms, self.num_pts)\n\n        assert (\n            sig_shape == self.sz\n        ), f\"Signal frame to be transformed must have shape {self.sz}\"\n\n        signal_gpu = gpuarray.to_gpu(\n            np.ascontiguousarray(signal, dtype=self.complex_dtype)\n        )\n\n        result_gpu = gpuarray.GPUArray(res_shape, dtype=self.complex_dtype)\n\n        self._transform_plan.execute(result_gpu, signal_gpu)\n\n        result = result_gpu.get()\n        # ASPIRE-Python/703\n        result = result.astype(complex_type(self._original_dtype), copy=False)\n\n        return result\n\n    def adjoint(self, signal):\n        \"\"\"\n        Compute the NUFFT adjoint using this plan instance.\n\n        :param signal: Signal to be transformed. For a single transform,\n        this should be a a 1D array of len `num_pts`.\n        For a batch, signal should have shape `(ntransforms, num_pts)`.\n\n        :returns: Transformed signal `(sz)` or `(sz, ntransforms)`.\n        \"\"\"\n\n        # Check we're not forcing a dtype workaround for ASPIRE-Python/703,\n        #   then check if we have a dtype mismatch.\n        # This avoids false positive complaint for the workaround.\n        if (self._original_dtype == self.dtype) and not (\n            signal.dtype == self.complex_dtype or signal.dtype == self.dtype\n        ):\n            logger.warning(\n                \"Incorrect dtypes passed to (a)nufft.\"\n                \" In the future this will be an error.\"\n            )\n\n        res_shape = self.sz\n        # Note, there is a corner case for ntransforms == 1.\n        if self.ntransforms > 1 or (self.ntransforms == 1 and len(signal.shape) == 2):\n            assert (\n                len(signal.shape) == 2\n            ), f\"For multiple {self.dim}D adjoints, signal should be a {self.ntransforms} element stack of {self.num_pts}.\"\n            assert (\n                signal.shape[0] == self.ntransforms\n            ), \"For multiple transforms, signal stack length should match ntransforms {self.ntransforms}.\"\n            res_shape = (self.ntransforms, *self.sz)\n\n        signal_gpu = gpuarray.to_gpu(\n            np.ascontiguousarray(signal, dtype=self.complex_dtype)\n        )\n\n        result_gpu = gpuarray.GPUArray(res_shape, dtype=self.complex_dtype)\n\n        self._adjoint_plan.execute(signal_gpu, result_gpu)\n\n        result = result_gpu.get()\n        # ASPIRE-Python/703\n        result = result.astype(complex_type(self._original_dtype), copy=False)\n\n        return result\n","repo_name":"ComputationalCryoEM/ASPIRE-Python","sub_path":"src/aspire/nufft/cufinufft.py","file_name":"cufinufft.py","file_ext":"py","file_size_in_byte":7520,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"77"}
+{"seq_id":"16347285306","text":"\nimport os\nimport svhn\nimport six\nimport numpy as np\nimport itertools\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\n\nfrom adversarial_attack import *\n\ntf.logging.set_verbosity(tf.logging.ERROR)\n\nGPU_NUM = 4\n\nlearningrate = 0.01\nglobal_step = tf.Variable(0, trainable=False)\n\nsess_config = tf.ConfigProto()\nsess_config.gpu_options.allow_growth = True\nsess = tf.Session(config=sess_config)\n\nfrom wideresnet28model import *\ndef forward_pass(x, dropout_keep_prob, is_train=False, only_logits=False):\n    x = x / 128 - 1\n    end_points = {}\n    with tf.variable_scope(name_or_scope='resnet9', reuse=tf.AUTO_REUSE):\n        model = ResNetCifar10(is_training=is_train)\n        global_pool = model.forward_pass(x)\n        global_pool = tf.nn.dropout(global_pool, dropout_keep_prob)\n        logits = model.fully_connected(global_pool, 10)\n    if only_logits:\n        return logits\n    return logits, end_points\n\n\ndef tower_fn(is_training, feature, label, open_adv_trainn=False):\n    if is_training:\n        keepprob = 0.5\n    else:\n        keepprob = 1.0\n    logits, end_point = forward_pass(feature, keepprob, is_train=is_training)\n\n    clean_correct_p = tf.equal(tf.argmax(logits, -1), tf.cast(label, tf.int64))\n    clean_accuracy = tf.reduce_mean(tf.cast(clean_correct_p, \"float\"))\n    adv_accuracy = 0\n\n    if open_adv_trainn:\n        model_fn_eval_mode = lambda x: forward_pass(x, dropout_keep_prob=1.0, is_train=False, only_logits=True)\n        bounds = (0, 255)\n        adv_feature = generate_adversarial_examples(feature, bounds, model_fn_eval_mode, \"pgd_12_3_10\")\n        adv_logits, adv_end_point = forward_pass(adv_feature, keepprob, is_train=is_training)\n\n        clean_correct_p = tf.equal(tf.argmax(logits, -1), tf.cast(label, tf.int64))\n        clean_accuracy = tf.reduce_mean(tf.cast(clean_correct_p, \"float\"))\n        adv_correct_p = tf.equal(tf.argmax(adv_logits, -1), tf.cast(label, tf.int64))\n        adv_accuracy = tf.reduce_mean(tf.cast(adv_correct_p, \"float\"))\n\n        logits = tf.concat([logits, adv_logits], axis=0)\n        label = tf.concat([label, label], axis=0)\n\n    tower_loss = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=label))\n    l2_var = tf.trainable_variables()\n    tower_loss += 2e-4 * tf.add_n([tf.nn.l2_loss(v) for v in l2_var])\n\n    model_params = tf.trainable_variables()\n    tower_grad = tf.gradients(tower_loss, model_params)\n    return tower_loss, zip(tower_grad, model_params), clean_accuracy, adv_accuracy\n\n\ndef input_fn(data_dir, subset, batch_size):\n    with tf.device('/cpu:0'):\n        use_distortion = subset == 'train'\n        dataset = svhn.SVHNDataSet(data_dir, subset, use_distortion)\n        image_batch, label_batch = dataset.make_batch(batch_size)\n        return image_batch, label_batch\n\n\ndef model_fn(features, labels, is_training, open_adv_train=False):\n    tower_features = tf.split(features, GPU_NUM)\n    tower_labels = tf.split(labels, GPU_NUM)\n    tower_losses = []\n    tower_gradvars = []\n    tower_clean_acc = []\n    tower_adv_acc = []\n    for i in range(GPU_NUM):\n        worker_device = '/{}:{}'.format(\"GPU\", i)\n        with tf.device(worker_device):\n            loss, gradvars, clean_AC, adv_AC = tower_fn(is_training, tower_features[i], tower_labels[i], open_adv_train)\n            tower_losses.append(loss)\n            tower_gradvars.append(gradvars)\n            tower_clean_acc.append(clean_AC)\n            tower_adv_acc.append(adv_AC)\n    # Now compute global loss and gradients.\n    if is_training:\n        gradvars = []\n        with tf.name_scope('gradient_averaging'):\n            all_grads = {}\n            for grad, var in itertools.chain(*tower_gradvars):\n                if grad is not None:\n                    all_grads.setdefault(var, []).append(grad)\n            for var, grads in six.iteritems(all_grads):\n                with tf.device(var.device):\n                    if len(grads) == 1:\n                        avg_grad = grads[0]\n                    else:\n                        avg_grad = tf.multiply(tf.add_n(grads), 1. / len(grads))\n                gradvars.append((avg_grad, var))\n        with tf.device('/cpu:0'):\n            update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n            with tf.control_dependencies(update_ops):\n                optimizer = tf.train.MomentumOptimizer(learning_rate=learningrate, momentum=0.9)\n                train_op = [optimizer.apply_gradients(gradvars, global_step=global_step)]\n                train_op = tf.group(*train_op)\n    else:\n        train_op = 0\n\n    with tf.device('/cpu:0'):\n        loss = tf.reduce_mean(tower_losses, name='loss')\n        clean_acc = tf.reduce_mean(tower_clean_acc)\n        adv_acc = tf.reduce_mean(tower_adv_acc)\n    return train_op, loss, clean_acc, adv_acc\n","repo_name":"superxiaotutu/mm","sub_path":"model_base.py","file_name":"model_base.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42643393748","text":"import discord\nfrom discord.ext import commands\nimport datetime\n\nclass Message(commands.Cog):\n    \"\"\"This category is for managing messages\"\"\"\n\n    def __init__(self, client):\n        self.client = client\n\n    @commands.command()\n    @commands.has_any_role(\"Admin\", \"Moderator\")\n    async def user_messages(self, ctx, user: discord.Member, channel : discord.TextChannel, date_begin, date_stop=None):\n        \"\"\"This command checks all messages given by a user in a channel within a certain date\"\"\"\n        worked, work = False, True\n        if(work == False):\n            await ctx.send('No Member Found')\n        else:\n            try:\n                monthb, dayb, yearb = map(int, date_begin.split('/'))\n                try:\n                    months, days, years = map(int, date_stop.split('/'))\n                    bef = datetime.datetime(years, months, days)\n                except:\n                    bef = None\n                    date_stop = 'current'\n                worked = True\n            except:\n                await ctx.send('You have entered a wrong date start or stop')\n            if(worked == True):\n                embed = discord.Embed(title='All Messages Sent By {}'.format(user.display_name), description='Dates from {} to {}'.format(date_begin, date_stop), color=discord.Color.teal())\n                embed.set_author(name=user.display_name, icon_url=user.avatar_url)\n                async for message in channel.history(after=datetime.datetime(yearb, monthb, dayb), before=bef, oldest_first=True):\n                    if(message.author == user):\n                        month, day, year = map(str, str(message.created_at).split(' ')[0].split('-'))\n                        hour, minute, second = map(str, str(message.created_at).split(' ')[1].split(':'))\n                        second = second.split('.')[0]\n                        hour = str(int(hour) + 5)\n                        if(hour > '12'):\n                            hour = str(int(hour) - 12)\n                            ti = 'P'\n                            if(hour > '12'):\n                                hour = str(int(hour) - 12)\n                                ti = 'A'\n                        else:\n                            ti = 'A'\n                        embed.add_field(name=message.content, value='{}/{}/{} {}:{}:{} {}M'.format(month, day, year, abs(hour), minute, second, ti), inline=False)\n                await ctx.author.send('', embed=embed)\ndef setup(client):\n    client.add_cog(Message(client))\n","repo_name":"Salif500/ycwbot","sub_path":"unused/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27138252648","text":"#!/usr/bin/python3\n\ndef uniq_add(my_list=[]):\n    used_int = []\n    result = 0\n    for i in range(0, len(my_list)):\n        found = 'f'\n        for j in range(0, len(used_int)):\n            if (my_list[i] == used_int[j]):\n                found = 't'\n        if (found == 'f'):\n            used_int.append(my_list[i])\n            result = result + my_list[i]\n    return (result)\n","repo_name":"samcalde/holbertonschool-higher_level_programming","sub_path":"python-more_data_structures/2-uniq_add.py","file_name":"2-uniq_add.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10955239596","text":"import sqlalchemy\nfrom sqlalchemy.sql import select\nimport pandas as pd\nfrom datetime import datetime\nfrom sqlalchemy import (\n    MetaData,\n    Table,\n    Column,\n    Integer,\n    Numeric,\n    String,\n    DateTime,\n    Boolean,\n    ForeignKey,\n    create_engine,\n)\nfrom sqlalchemy import PrimaryKeyConstraint, UniqueConstraint, CheckConstraint\n\nengine = create_engine(\"sqlite:///listings_program.db\")\nmetadata = MetaData()\n\n# 1-2\n\nlisting = Table(\n    \"listing\",\n    metadata,\n    Column(\"listing_id\", Integer(), primary_key=True),\n    Column(\"listing_name\", String(50), index=True),\n    Column(\"listing_url\", String(255)),\n    Column(\"host_id\", Integer()),\n    Column(\"neighbourhood_id\", Integer()),\n    Column(\"amenities\", String(300)),\n    Column(\"property_type_id\", Integer()),\n    Column(\"room_type_id\", Integer()),\n    Column(\"bedrooms\", Integer()),\n    Column(\"beds\", Integer()),\n    Column(\"price\", Numeric(7, 2)),\n    CheckConstraint(\"price >= 0.00\", name=\"listing_price_positive\"),\n    extend_existing=True,\n)\n\n# 1-3\nuser = Table(\n    \"user\",\n    metadata,\n    Column(\"user_id\", Integer(), primary_key=True),\n    Column(\"user_name\", String(15), nullable=False, unique=True),\n    Column(\"email_address\", String(255), nullable=False),\n    Column(\"phone\", String(20), nullable=False),\n    Column(\"password\", String(25), nullable=False),\n    Column(\"created_on\", DateTime(), default=datetime.now),\n    Column(\"updated_on\", DateTime(), default=datetime.now, onupdate=datetime.now),\n)\n\n# 1-4\n\norder = Table(\n    \"order\",\n    metadata,\n    Column(\"order_id\", Integer(), primary_key=True),\n    Column(\"user_id\", ForeignKey(\"user.user_id\")),\n    Column(\"confirmed\", Boolean(), default=False),\n    Column(\"order_price\", Integer()),\n    extend_existing=True,\n)\n\n# 1-5\n\nline_item = Table(\n    \"line_item\",\n    metadata,\n    Column(\"line_item_id\", Integer(), primary_key=True),\n    Column(\"order_id\", ForeignKey(\"order.order_id\")),\n    Column(\"listing_id\", ForeignKey(\"listing.listing_id\")),\n    Column(\"item_start_date\", DateTime()),\n    Column(\"item_end_date\", DateTime()),\n    Column(\"item_price\", Integer()),\n    extend_existing=True,\n)\n\n# 1-6\n\nneighbourhood = Table(\n    \"neighbourhood\",\n    metadata,\n    Column(\"neighbourhood_id\", Integer(), primary_key=True),\n    Column(\"neighbourhood_name\", String(30)),\n)\n\n# 1-7\n\nproperty_type = Table(\n    \"property_type\",\n    metadata,\n    Column(\"property_type_id\", Integer(), primary_key=True),\n    Column(\"property_type_name\", String(30)),\n)\n\n# 1-8\n\nroom_type = Table(\n    \"room_type\",\n    metadata,\n    Column(\"room_type_id\", Integer(), primary_key=True),\n    Column(\"property_type_name\", String(30)),\n)\n\n# Создаем структуру\nmetadata.create_all(engine)\n\n# Данные, которые вставляем в таблицу\nam = pd.read_csv(\"ListingsAmsterdam.csv\", sep=\";\")\n# Вставка данных\nins = listing.insert().values(\n    listing_id=20168,\n    listing_name=\"Studio with private bathroom in the centre 1\",\n    listing_url=\"https://www.airbnb.com/rooms/20168\",\n    host_id=59484,\n    neighbourhood_id=4,\n    property_type_id=35,\n    room_type_id=3,\n    amenities=am.loc[0, \"amenities\"][:300],\n    bedrooms=1,\n    beds=1,\n    price=236,\n)\n\nins.compile().params\nconnection = engine.connect()\nresult = connection.execute(ins)\nprint(result.inserted_primary_key)\n\n#SELECT *\ns = select([listing])\nrp = connection.execute(s)\nresults = rp.fetchall()\nprint(results)\n\n#SELECT WHERE\nselected_listingid = sqlalchemy.sql.column('listing_id')\nwhere_query = sqlalchemy.sql.select([listing]).where(selected_listingid == 20168)\nrp = connection.execute(where_query)\nresults = rp.fetchall()\nprint(results)","repo_name":"GeorgiyDemo/FA","sub_path":"Course_II/Python_SQL/pract/pract3/T01_Core/program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"77"}
+{"seq_id":"40720444558","text":"class Solution:\n    def findMaxLength(self, nums: List[int]) -> int:\n        seen, running, res = {0: -1}, 0, 0\n        for i, num in enumerate(nums):\n            if num == 0:\n                running += 1\n            else:\n                running -= 1\n            if running in seen:\n                res = max(res, i - seen[running])\n            else:\n                seen[running] = i\n        return res\n","repo_name":"wondershow/CodingTraining","sub_path":"Python/LC129_SumRootToLeafNumbers.py","file_name":"LC129_SumRootToLeafNumbers.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9982279931","text":"from django.shortcuts import render , redirect\nfrom .models import Consume, food\n# Create your views here.\ndef index(request):\n    # Add function\n    if request.method == \"POST\":\n        food_consumed = request.POST['food_consumed']\n        # Querying database for food which has the name equal to the food which we got from Add.\n        consume = food.objects.get(name=food_consumed)\n        #Add the food item to consumption list of current user.\n        user = request.user\n        consume = Consume(user=user,food_consumed=consume)\n        consume.save()\n        foods = food.objects.all()\n\n    else:  \n        # Get all objects in food model\n        foods = food.objects.all()\n    # Querying database for food items consumed by the current user(filtered by current user).\n    consumed_food = Consume.objects.filter(user=request.user) \n    return render(request,'myapp/index.html',{'foods':foods,'consumed_food':consumed_food})\n\ndef delete_consume(request,id):\n    consumed_food = Consume.objects.get(id=id)\n    if request.method =='POST':\n        consumed_food.delete()\n        return redirect('/')\n    return render(request,'myapp/delete.html')","repo_name":"chandrachuds/Calorie-Counter-App","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22353431336","text":"\"\"\"This script is intended to be used with https://github.com/kkoutini/passt_hear21\"\"\"\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torchaudio\nfrom tqdm import tqdm\n\nfrom hear21passt.base30sec import load_model, get_scene_embeddings, get_timestamp_embeddings\n\n\ndef process_audios(model, input_dir: Path, output_dir: Path, audio_file_extension: str):\n    for audio_fn in tqdm(input_dir.rglob(\"*\" + audio_file_extension)):\n        sub_path = audio_fn.relative_to(input_dir)\n        # print(audio_fn)\n        audio, sample_rate = torchaudio.load(input_dir / audio_fn)\n        audio = torchaudio.functional.resample(audio, sample_rate, 32000)\n        embed = get_scene_embeddings(audio, model).squeeze(0)\n        embed_np = embed.cpu().numpy()\n        (output_dir / sub_path).parent.mkdir(exist_ok=True)\n        np.save((output_dir / sub_path).with_suffix(\".npy\"), embed_np)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Extract embeddings from audio files.')\n    parser.add_argument('input_dir', help='directory containing audio files', type=Path)\n    parser.add_argument('output_dir', help='target directory to write the embedding files to', type=Path)\n    parser.add_argument('--ext', help='file extension of the audio files', default=\".wav\", type=str)\n\n    args = parser.parse_args()\n    model = load_model().cuda()\n    process_audios(model, args.input_dir, args.output_dir, args.ext)\n","repo_name":"Bomme/freesound-crossmodal-search","sub_path":"scripts/extract_passt_embeddings.py","file_name":"extract_passt_embeddings.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"72634594488","text":"import pandas as pd\nimport numpy as np\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https://www.iplt20.com/stats/2022'\nreq = requests.get(url)\n\n# 200 means we can successfully scrape the data\nprint(req.status_code)\n\n# print the entire html content\n# data is of type 'bytes', NOT string\nprint(req.content)\n\n# export to html file with 'wb' as 'write bytes'\nwith open('ipl20.html', 'wb') as fobj:\n    fobj.write(req.content)\n\n# store the page into a variable\ndata = req.content\nprint(data)\n\n# let's use beautiful soup to hash the data\n# goto inspect element in browser, click on network, paste the url, and you get this information\n# fetching without the table data\nreq = requests.get(url)\nprint(req.headers, req.cookies)\n\n# fetching with table data // we didnt finish this\nheaders_info = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:101.0) Gecko/20100101 Firefox/101.0'}\nreq = requests.get(url, headers=headers_info)\nprint(req.status_code)\n\n# assigning data from a html table and plop it into a dataframe\nurl = 'https://www.arraysolar.co.in/group/group_dec.php?flag=group'\nreq = requests.get(url)\nprint(req.status_code)\n\n# if theres multiple tables in the html page, then you'll get a list of dataframes\nwith open('pagedata.html', 'wb') as fobj:\n    fobj.write(req.content)\n\n\n\n\n\n","repo_name":"CorpHackRyan/web-scraping-practice","sub_path":"web_scraping.py","file_name":"web_scraping.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29423606759","text":"def numero_perfecto(a):\n    divisor_de_A=0\n    for i in range(1,a):\n        if a%i==0:\n            divisor_de_A=i+divisor_de_A\n    if divisor_de_A==a:\n        return True\n    else:\n        return False\n\nif __name__==\"__main__\":\n    sumatoria=0\n    a=int(input(\"Ingrese variable a: \"))\n    print(numero_perfecto(a))\n    nueva=int(input(\"Ingrese variable nueva: \"))\n    for i in range(1,nueva):\n        if numero_perfecto(i)==True:\n            sumatoria=i+sumatoria\n    print(suma)\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej3/tema2_ej3_15636275.py","file_name":"tema2_ej3_15636275.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4054060732","text":"\"\"\"This module starts a json parser\"\"\"\r\n\r\nfrom ulrs_to_parse import get_total_num_pages, create_all_sector_pages\r\nfrom create_json_offer import get_products_on_pages, ProductPageInfo\r\nfrom settings import sectors_search_url\r\n\r\n\r\ndef start_sector_parser(sector_url):\r\n    \"\"\"This func launch parser\"\"\"\r\n    sector_pages_qt = get_total_num_pages(sector_url)\r\n    all_sector_pages = create_all_sector_pages(sector_pages_qt, sector_url)\r\n    for url in all_sector_pages:\r\n        page_soup_objects = get_products_on_pages(url)\r\n        for item_soup_object in page_soup_objects:\r\n            data = ProductPageInfo(item_soup_object)\r\n            data.create_offers()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    for sector_url in sectors_search_url:\r\n        start_sector_parser(sector_url)\r\n","repo_name":"islamariya/product_info_parser","sub_path":"src/start_json_parser.py","file_name":"start_json_parser.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19054528198","text":"from google.cloud import secretmanager\n\n\ndef gcloud_read_secret(project_id: str, secret: str) -> str:\n    \"\"\"Downloads a secret from the secret manager.\"\"\"\n    client = secretmanager.SecretManagerServiceClient()\n    response = client.access_secret_version(\n        name=f\"projects/{project_id}/secrets/{secret}/versions/2\"\n    )\n    payload = response.payload.data.decode(\"UTF-8\")\n    print(\"Secret Manager -- Secret fetched successfully.\")\n    return payload\n","repo_name":"Baarosh/storage","sub_path":"python/secret_manager/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74260276089","text":"\"\"\"test task detail\"\"\"\nimport pytest\nfrom sqlalchemy import select\nfrom sqlalchemy.orm import selectinload\n\nfrom crawlerstack_spiderkeeper_server.models import Task\nfrom crawlerstack_spiderkeeper_server.repository.task_detail import \\\n    TaskDetailRepository\nfrom crawlerstack_spiderkeeper_server.utils.exceptions import \\\n    ObjectDoesNotExist\n\n\n@pytest.fixture()\nasync def repo():\n    \"\"\"repo fixture\"\"\"\n    return TaskDetailRepository()\n\n\n@pytest.mark.parametrize(\n    'task_name, exist',\n    [\n        ('test1', True),\n        ('test3', False)\n    ]\n)\nasync def test_get_task_detail_from_task_name(init_task_detail, repo, session, task_name, exist):\n    \"\"\"Test get a object.\"\"\"\n    if exist:\n        exist_obj = await session.scalar(\n            select(Task).filter(Task.name == task_name).options(selectinload(Task.task_details)))\n        obj = await repo.get_task_detail_from_task_name(exist_obj.name)\n        assert obj\n        assert exist_obj.task_details[0].id == obj.id\n    else:\n        with pytest.raises(ObjectDoesNotExist):\n            await repo.get_task_detail_from_task_name(task_name)\n","repo_name":"crawlerstack/crawlerstack-spiderkeeper","sub_path":"tests/crawlerstack_spiderkeeper_server/repository/test_task_detail.py","file_name":"test_task_detail.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"3027825737","text":"def make_dict(map_x):\n    map_dict = []\n    for i in range(len(map_x)):\n        for j in range(len(map_x[i])):\n            row = {}\n            row['x'] = j\n            row['y'] = i\n            if map_x[i][j] == 0:\n                row['type'] = 'floor'\n            if map_x[i][j] == 1:\n                row['type'] = 'wall'\n            if map_x[i][j] == 2:\n                row['type'] = 'hero'\n            map_dict.append(row)\n    return map_dict\n","repo_name":"agyenes/greenfox-exercises","sub_path":"12 boardgame_python/make_dict.py","file_name":"make_dict.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3737232488","text":"from OpenGL.GL import *\nfrom OpenGL.GLU import *  # opengl utility library\nfrom OpenGL.GLUT import *  # opengl utility toolkit\nimport sys\nimport math\n\n\ndef init():\n    glClearColor(0.0, 0.0, 0.0, 1.0)  # set background color\n    # set the range of coordinate system\n    gluOrtho2D(-100.0, 100.0, -100.0, 100.0)\n\n\ndef drawPixel(x, y):\n    glColor3f(1.0, 0.0, 0.0)\n    glPointSize(5.0)\n    glBegin(GL_POINTS)\n    glVertex2f(x, y)\n    glEnd()\n    glFlush()\n\n\ndef midPointCircleDraw(xc, yc, r):\n    x = r\n    y = 0\n    # drawPixel(x+xc, y+yc)\n    # if (r > 0):\n    #     drawPixel(x+xc, -y+yc)\n    #     drawPixel(y+xc, x+yc)\n    #     drawPixel(-y+xc, x+yc)\n    P = 1 - r\n\n    while x > y:\n        y = y + 1\n        if P <= 0:\n            P = P + 2*y + 1\n\n        else:\n            x = x - 1\n            P = P + 2*y - 2*x + 1\n            \n        drawPixel(x+xc, y+yc)\n        drawPixel(-x+xc, y+yc)\n        drawPixel(x+xc, -y+yc)\n        drawPixel(-x+xc, -y+yc)\n        drawPixel(y+xc, x+yc)\n        drawPixel(-y+xc, x+yc)\n        drawPixel(y+xc, -x+yc)\n        drawPixel(-y+xc, -x+yc)\n\n\ndef non_polar_circle(xc, yc, radius):\n    glColor3f(1.0, 0.0, 0.0)\n    glPointSize(5.0)\n    glBegin(GL_POINTS)\n    x = xc - radius\n    target = xc + radius\n    glVertex2f(x, yc)\n    glVertex2f(target, yc)\n    factor = 7500\n    incr = 1/factor\n    x += incr\n    while x < target:\n        adder = math.sqrt(radius*radius - (x - xc)*(x - xc))\n        glVertex2f(x, yc + adder)\n        glVertex2f(x, yc - adder)\n        x += incr\n    glEnd()\n    glFlush()\n\n\ndef polar_circle(xc, yc, radius):\n    theta = 0\n    factor = 500\n    incr = 1 / factor\n    target = math.pi / 4\n    glColor3f(1.0, 0.0, 0.0)\n    glPointSize(5.0)\n    glBegin(GL_POINTS)\n    while (theta <= target):\n        x = radius*math.cos(theta)\n        y = radius*math.sin(theta)\n        glVertex2f(x + xc, y + yc)\n        glVertex2f(-x + xc, -y + yc)\n        glVertex2f(-x + xc, y + yc)\n        glVertex2f(x + xc, -y + yc)\n        glVertex2f(y + xc, x + yc)\n        glVertex2f(-y + xc, -x + yc)\n        glVertex2f(-y + xc, x + yc)\n        glVertex2f(y + xc, -x + yc)\n        theta = theta + incr\n    glEnd()\n    glFlush()\n# push the pixels to display\n\n\ndef main():\n    choice = input('''\n\t***MENU***\n\tEnter 1 to draw circle using mid point algorithm.\n\tEnter 2 to draw circle using non polar algorithm.\n\tEnter 3 to draw circle using polar algorithm.\n\t----------------------\n\t''')\n    if int(choice) == 1:\n        x = int(input(\"Enter the value of x center : \"))\n        y = int(input(\"Enter the value of y center : \"))\n        r = int(input(\"Enter the value of Radius: \"))\n        glutInit(sys.argv)\n        glutInitDisplayMode(GLUT_RGB)\n        glutInitWindowSize(500, 500)\n        glutInitWindowPosition(0, 0)\n        glutCreateWindow(\"Plot Circle using Midpoint Circle Drawing Algorithm\")\n        glutDisplayFunc(lambda: midPointCircleDraw(x, y, r))\n        init()\n        glutMainLoop()\n    elif int(choice) == 2:\n        x = int(input(\"Enter the value of x center: \"))\n        y = int(input(\"Enter the value of y center: \"))\n        r = int(input(\"Enter the value of Radius: \"))\n        glutInit(sys.argv)\n        glutInitDisplayMode(GLUT_RGB)\n        glutInitWindowSize(500, 500)\n        glutInitWindowPosition(0, 0)\n        glutCreateWindow(\"Plot Circle using Non Polar Circle\")\n        glutDisplayFunc(lambda: non_polar_circle(x, y, r))\n        init()\n        glutMainLoop()\n    elif int(choice) == 3:\n        x = int(input(\"Enter the value of x center: \"))\n        y = int(input(\"Enter the value of y center: \"))\n        r = int(input(\"Enter the value of Radius: \"))\n        glutInit(sys.argv)\n        glutInitDisplayMode(GLUT_RGB)\n        glutInitWindowSize(500, 500)\n        glutInitWindowPosition(0, 0)\n        glutCreateWindow(\"Plot Circle using Polar Circle\")\n        glutDisplayFunc(lambda: polar_circle(x, y, r))\n        init()\n        glutMainLoop()\n    else:\n        print(\"Check your input and try again..\")\n\n\nmain()","repo_name":"abhinav-TB/graphics-lab","sub_path":"circles/circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":3979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13357950238","text":"\"\"\"Baseball Clerk live baseball updates for Reddit game threads.\"\"\"\n\nimport argparse\nimport datetime\nimport json\nimport logging\nimport time\n\nimport praw\nfrom praw.models import Comment, Submission\n\nfrom baseballclerk import baseballbot\nfrom baseballclerk import comment\nfrom baseballclerk import datastore\nfrom baseballclerk import mlb\nfrom baseballclerk import savant\n\n\nEVENTS = datastore.Table(\"event\")\nCOMMENTS = datastore.Table(\"comment\")\n\n\ndef _parse_args():\n    parser = argparse.ArgumentParser()\n\n    def config_from_path(filepath: str):\n        with open(filepath, encoding=\"utf-8\") as conf_fo:\n            config = json.load(conf_fo)\n        return config\n\n    parser.add_argument(\n        \"config\", type=config_from_path, help=\"Path to a local configuration JSON file.\"\n    )\n    return parser.parse_args()\n\n\ndef due_up(game_pk: str, gamechat: Submission):\n    \"\"\"Post gamechat linescore updates (due up batters, pitching changes, substitutions, etc.).\"\"\"\n    due_up = mlb.due_up(game_pk)\n    if not due_up:\n        return\n\n    key = f\"dueup-{game_pk}-{gamechat.subreddit.display_name}-{due_up['inning']}-{due_up['inningHalf']}\"\n    if due_up and not COMMENTS.get(key):\n        EVENTS[key] = due_up\n        try:\n            cmnt = comment.due_up(gamechat, due_up)\n            COMMENTS[key] = cmnt\n        except comment.DataObjectError as err:\n            logging.error(err)\n\n\ndef play_by_play(game_pk: str, gamechat: Submission):\n    \"\"\"Post gamechat announcements (statcast & other play by play data).\"\"\"\n    for idx, play in enumerate(mlb.completed_plays(game_pk)):\n        # Update stored play.\n        key = f\"play-{game_pk}-{gamechat.subreddit.display_name}-{idx}\"\n        EVENTS[key] = play\n\n        # Skip if it isn't fresh.\n        end_time = datetime.datetime.fromisoformat(play[\"playEndTime\"].rstrip(\"Z\"))\n        if (datetime.datetime.utcnow() - end_time).seconds > 300:\n            continue\n\n        # Skip if we've already commented on this play.\n        if COMMENTS.get(key):\n            continue\n\n        # Comment for the play if necessary.\n        play_result = play.get(\"result\", {}).get(\"event\", \"\").lower()\n        if not play_result:\n            continue\n        if play_result == \"strikeout\":\n            try:\n                cmnt = comment.strikeout(gamechat, play)\n                COMMENTS[key] = cmnt\n            except comment.DataObjectError as err:\n                logging.error(err)\n        elif play_result == \"home run\":\n            try:\n                cmnt = comment.homerun(gamechat, play)\n                COMMENTS[key] = cmnt\n            except comment.DataObjectError as err:\n                logging.error(err)\n\n\ndef exit_velocities(game_pk: str, gamechat: Submission):\n    \"\"\"Post gamechat announcements for BaseballSavant data (i.e. hit probability data).\"\"\"\n    for idx, evo in enumerate(savant.exit_velocities(game_pk)):\n        # Update stored evo.\n        key = f\"evo-{game_pk}-{gamechat.subreddit.display_name}-{idx}\"\n        EVENTS[key] = evo\n\n        # Skip if we've already commented on this play.\n        if COMMENTS.get(key):\n            continue\n\n        # Comment for the play if necessary.\n        if not (\"xba\" in evo and \"is_bip_out\" in evo and evo.get(\"des\", \"\").strip()):\n            continue\n\n        xba = float(evo[\"xba\"])\n        is_bip_out = evo[\"is_bip_out\"].lower() == \"y\"\n        is_hit = evo.get(\"result\", \"\").lower() in (\n            \"single\",\n            \"double\",\n            \"triple\",\n            \"home run\",\n        )\n\n        if xba > 0.80 and is_bip_out:\n            try:\n                cmnt = comment.robbed(gamechat, evo)\n                COMMENTS[key] = cmnt\n            except comment.DataObjectError as err:\n                logging.error(err)\n        elif xba < 0.20 and not is_bip_out and is_hit:\n            try:\n                cmnt = comment.boxscore_linedrive(gamechat, evo)\n                COMMENTS[key] = cmnt\n            except comment.DataObjectError as err:\n                logging.error(err)\n\n\ndef main():\n    \"\"\"Write and post new BaseballClerk comments.\"\"\"\n    logging.basicConfig(\n        level=logging.INFO,\n        format=\"%(levelname)s:%(module)s:%(filename)s:%(lineno)s:%(message)s\",\n    )\n\n    logger = logging.getLogger(__name__)\n\n    args = _parse_args()\n    config = args.config\n\n    start_time = datetime.datetime.utcnow()\n    logger.info(\n        json.dumps(\n            {\n                \"msg\": \"Starting BaseballClerk.\",\n                \"subreddits\": list(config[\"subreddits\"].keys()),\n                \"start_time\": start_time.isoformat(),\n            }\n        )\n    )\n\n    # Connect the datastore and create tables if not existing.\n    datastore.connect(\"BaseballClerk.db\")\n    EVENTS.create_if_needed()\n    COMMENTS.create_if_needed()\n\n    for game_thread in baseballbot.active_game_threads():\n        subreddit_config = config[\"subreddits\"].get(\n            game_thread[\"subreddit\"][\"name\"]\n        )  # type: dict\n        if not subreddit_config:\n            continue\n\n        logger.info(\n            json.dumps(\n                {\n                    \"msg\": \"Running game thread.\",\n                    \"subreddit\": game_thread[\"subreddit\"][\"name\"],\n                    \"game_pk\": game_thread[\"gamePk\"],\n                }\n            )\n        )\n\n        reddit = praw.Reddit(subreddit_config[\"praw_bot\"])\n\n        game_pk = game_thread[\"gamePk\"]\n        gamechat = reddit.submission(game_thread[\"postId\"])\n\n        play_by_play(game_pk, gamechat)\n        exit_velocities(game_pk, gamechat)\n        due_up(game_pk, gamechat)\n\n        time.sleep(2)\n\n    for subreddit_config in config[\"subreddits\"].values():\n        praw_bot = subreddit_config[\"praw_bot\"]\n        reddit = praw.Reddit(praw_bot)\n\n        logger.info(\n            json.dumps(\n                {\"msg\": \"Running replies.\", \"subreddit\": subreddit_config[\"name\"]}\n            )\n        )\n\n        for item in reddit.inbox.unread():\n            # Make sure it is fresh.\n            created_utc = datetime.datetime.fromtimestamp(item.created_utc)\n            if (datetime.datetime.utcnow() - created_utc).seconds > 600:\n                item.mark_read()\n                continue\n\n            if isinstance(item, Comment) and praw_bot.lower() in item.body.lower():\n                key = f\"textface-{item.id}\"\n                cmnt = comment.default_mention_reply(\n                    item, subreddit_config[\"default_replies\"]\n                )\n                COMMENTS[key] = cmnt\n\n            item.mark_read()  # Keep the inbox clean.\n\n    end_time = datetime.datetime.utcnow()\n    elapsed = (end_time - start_time).total_seconds()\n    logger.info(\n        json.dumps(\n            {\n                \"msg\": \"Finished BaseballClerk.\",\n                \"subreddits\": list(config[\"subreddits\"].keys()),\n                \"start_time\": start_time.isoformat(),\n                \"end_time\": end_time.isoformat(),\n                \"elapsed\": elapsed,\n            }\n        )\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"troxellophilus/baseball-clerk","sub_path":"baseballclerk/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":6982,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"72553434168","text":"from core.test.text_generator.random_generator import Generator\nfrom core.test.name_generator import names\nfrom core.test.category_generator.choose_category import choose_random_category\nfrom core.test.text_generator.hive_name_generator import create_hive_name\nimport random\nimport hashlib\nimport time\nfrom django.utils import timezone\nfrom uuid import uuid4\nimport string\nfrom datetime import datetime\nfrom core.test.predefined_tags import predefined_unique_tags, predefined_non_unique_tags\n\n\n##########################################################\n# THIS IS FOR RANDOM BIG TEXT (random_generator package) #\n##########################################################\nimport os\n\nfile_path1 = os.path.join(os.path.dirname(__file__), 'text_generator/default/sample.txt')\nfile_path2 = os.path.join(os.path.dirname(__file__), 'text_generator/default/dictionary.txt')\n\nwith open(file_path1, 'r') as sample_txt:\n    sample = sample_txt.read()\nwith open(file_path2, 'r') as dictionary_txt:\n    dictionary = dictionary_txt.read().split()\n\ngenerator = Generator(sample, dictionary)\n##########################################################\n\n# This is for get_random_email() method\ndomains = [\"hotmail.com\", \"gmail.com\", \"aol.com\", \"mail.com\", \"mail.kz\", \"yahoo.com\"]\n\nSECRET_KEY = 'asd538J878hdfjKEidrfdgf0954lKUJMd03l4mfjejJKkek4AA'\n\ntry:\n    random = random.SystemRandom()\n    using_sysrandom = True\nexcept NotImplementedError:\n    import warnings\n    warnings.warn('A secure pseudo-random number generator is not available '\n                  'on your system. Falling back to Mersenne Twister.')\n    using_sysrandom = False\n\n\ndef __string_generator(size=6, chars=string.ascii_uppercase + string.digits):\n    \"\"\"\n    Returns a securely generated random string.\n\n    The default length of 12 with the a-z, A-Z, 0-9 character set returns\n    a 71-bit value. log_2((26+26+10)^12) =~ 71 bits\n    \"\"\"\n    if not using_sysrandom:\n        # This is ugly, and a hack, but it makes things better than\n        # the alternative of predictability. This re-seeds the PRNG\n        # using a value that is hard for an attacker to predict, every\n        # time a random string is required. This may change the\n        # properties of the chosen random sequence slightly, but this\n        # is better than absolute predictability.\n        random.seed(\n            hashlib.sha256(\n                (\"%s%s%s\" % (\n                    random.getstate(),\n                    time.time(),\n                    SECRET_KEY)).encode('utf-8')\n            ).digest())\n    return ''.join(random.choice(chars) for _ in range(size))\n\n\ndef get_random_email():\n    if not using_sysrandom:\n        random.seed(\n            hashlib.sha256(\n                (\"%s%s%s\" % (\n                    random.getstate(),\n                    time.time(),\n                    SECRET_KEY)).encode('utf-8')\n            ).digest())\n    # Not all the possibilities are taking into account:\n    local_part = string.ascii_lowercase + string.ascii_uppercase + string.digits + \"#-_~!$&'()*+,;=:\"\n\n    email = ''\n    while True:\n        # Django demands a maximum of 75 chars for EmailField, this is a limitation of Django Framework\n        # If we need more we should look at the consecuences of raising this to 254 (estándar)\n\n        if len(email) < 75:\n            email = ''.join(random.choice(local_part) for _ in range(random.randint(1, 50))) + '@' + random.choice(domains)\n            break\n    print(\"Chosen email: \", email)\n    return email\n\n\ndef get_random_date():\n    year = random.randint(2014, 2015)\n\n    if year == 2015:\n        month = random.randint(1, 3)\n    else:\n        month = random.randint(1, 12)\n\n    if month == 2:\n        day = random.randint(1, 28)\n    else:\n        day = random.randint(1, 30)\n\n    hour = random.randint(0, 23)\n    minute = random.randint(0, 59)\n    second = random.randint(0, 59)\n    microsecond = random.randint(0, 999999)\n\n    random_date = datetime(year, month, day, hour, minute, second, microsecond, tzinfo=timezone.get_default_timezone())\n    return random_date\n\n\n# probability defines de probability of having a totally random tag or a predefined tag\ndef get_random_tag(probability=30):\n    if random.randrange(100) < probability:\n        # The higher percent value is, the more likely it will get into this part of the if else\n        tag = __string_generator(size=random.randint(1, 32),\n                                 chars=string.ascii_uppercase + string.ascii_lowercase + string.digits + '_')\n    else:\n        tag = random.choice(predefined_unique_tags)\n        predefined_unique_tags.remove(tag)\n    return tag\n\n\ndef get_random_tags(quantity=random.randint(1, 5), probability=40):\n    tags = []\n    for tag in range(0, quantity):\n        if random.randrange(100) < probability:\n            # The higher percent value is, the more likely it will get into this part of the if else\n            tags[tag] = __string_generator(size=random.randint(1, 32),\n                                           chars=string.ascii_uppercase + string.ascii_lowercase + string.digits + '_')\n        else:\n            tags[tag] = random.choice(predefined_non_unique_tags)\n    return tags\n\n\ndef get_random_hive_name():\n    return create_hive_name()\n\n\ndef get_random_hive_priority():\n    return random.randint(1, 100)\n\n\ndef get_random_public_name():\n    return __string_generator(size=random.randint(1, 20),\n                              chars=string.ascii_uppercase + string.ascii_lowercase + string.digits + '_')\n\n\ndef get_random_username():\n    return uuid4().hex\n\n\ndef get_random_category_code():\n    return choose_random_category()\n\n\ndef get_random_full_name(gender):\n    return names.get_full_name(gender)\n\n\ndef get_random_hive_description():\n    # Because we don't always want long names we change the relative maximum length of the description\n    max_length = random.randint(100, 2048)\n    random_description = ''\n    last_paragraph = ''\n    while len(random_description) < (max_length + 1):\n        last_paragraph = generator.generate_paragraph(1)[2]\n        print(\"last_paragraph es de tipo\")\n        type(last_paragraph)\n        print(\"last_paragraph: \", last_paragraph)\n        random_description += last_paragraph + '\\n'\n    random_description = random_description[0:(len(random_description) - (len(last_paragraph) + 1))]\n    return random_description","repo_name":"diegopau/chattyhive-backend","sub_path":"core/test/get_random_stuff.py","file_name":"get_random_stuff.py","file_ext":"py","file_size_in_byte":6342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74523792568","text":"\"\"\"\nTic Tac Toe Player\n\"\"\"\n\nfrom copy import deepcopy\nfrom math import inf\n\nX = \"X\"\nO = \"O\"\nEMPTY = None\n\n# ---------------HELPER---------------\n\n\ndef maxMinValue(board):\n    if terminal(board):\n        return utility(board)\n    value = -inf\n    for action in actions(board):\n        value = max(value, minimum(result(board, action)))\n\n    return value\n\n\ndef minimum(board):\n    if terminal(board):\n        return utility(board)\n    value = inf\n    for action in actions(board):\n        value = min(value, maxMinValue(result(board, action)))\n\n    return value\n# ---------------HELPER----------------\n\n\ndef initial_state():\n    \"\"\"\n    Returns starting state of the board.\n    \"\"\"\n    return [[EMPTY, EMPTY, EMPTY],\n            [EMPTY, EMPTY, EMPTY],\n            [EMPTY, EMPTY, EMPTY]]\n\n\ndef player(board):\n    \"\"\"\n    Returns player who has the next turn on a board.\n    \"\"\"\n    xCount = 0\n    oCount = 0\n\n    for row in board:\n        for cell in row:\n            if cell == X:\n                xCount += 1\n            elif cell == O:\n                oCount += 1\n\n    if xCount <= oCount:\n        return 'X'\n    else:\n        return 'O'\n\n\ndef actions(board):\n    \"\"\"\n    Returns set of all possible actions (i, j) available on the board.\n    \"\"\"\n\n    possibleActionSet = set()\n\n    for row in range(3):\n        for cell in range(3):\n            if board[row][cell] == EMPTY:\n                possibleActionSet.add((row, cell))\n\n    return possibleActionSet\n\n\ndef result(board, action):\n    \"\"\"\n    Returns the board that results from making move (i, j) on the board.\n    \"\"\"\n\n    if terminal(board):\n        raise ValueError(\"Game over.\")\n\n    row = action[0]\n    cell = action[1]\n    copyBoard = deepcopy(board)\n    copyBoard[row][cell] = player(board)\n\n    return copyBoard\n\n\ndef winner(board):\n    \"\"\"\n    Returns the winner of the game, if there is one.\n    \"\"\"\n\n    # checks diagonal decline\n    diagonalCheck = board[0][0]\n    diagonalCount = 0\n    for cell in range(3):\n        if board[cell][cell] == diagonalCheck:\n            diagonalCount += 1\n        else:\n            break\n    if diagonalCount == 3:\n        return diagonalCheck\n\n    # checks horizontally for wins\n    for row in range(3):\n        xScore = 0\n        oScore = 0\n        for column in range(3):\n            if board[row][column] == X:\n                xScore += 1\n            elif board[row][column] == O:\n                oScore += 1\n        if xScore == 3:\n            return X\n        elif oScore == 3:\n            return O\n\n    # checks vertically for wins\n    for row in range(3):\n        xScore = 0\n        oScore = 0\n        for column in range(3):\n            if board[column][row] == X:\n                xScore += 1\n            elif board[column][row] == O:\n                oScore += 1\n        if xScore == 3:\n            return X\n        elif oScore == 3:\n            return O\n\n    if board[0][2] == board[1][1] == board[2][0] != None:\n        return board[0][2]\n\n    return None\n\n\ndef terminal(board):\n    \"\"\"\n    Returns True if game is over, False otherwise.\n    \"\"\"\n\n    # if there is a winner end game\n    if winner(board) != None:\n        return True\n\n    # if there are no more space return true\n    for row in range(3):\n        for column in range(3):\n            if board[row][column] == None:\n                return False\n    return True\n\n\ndef utility(board):\n    \"\"\"\n    Returns 1 if X has won the game, -1 if O has won, 0 otherwise.\n    \"\"\"\n\n    if winner(board) == X:\n        return 1\n\n    elif winner(board) == O:\n        return -1\n\n    else:\n        return 0\n\n\ndef minimax(board):\n    \"\"\"\n    Returns the optimal action for the current player on the board.\n    \"\"\"\n\n    if board == initial_state():\n        return (0, 0)\n\n    elif player(board) == X:\n        optimalMove = None\n        value = -inf\n        for action in actions(board):\n            minValueResult = inf\n            if terminal(board):\n                minValueResult = utility(board)\n            else:\n                for action in actions(board):\n                    minValueResult = min(\n                        minValueResult, maxMinValue(result(board, action)))\n\n            if minValueResult > value:\n                value = minValueResult\n                optimalMove = action\n\n    elif player(board) == O:\n        optimalMove = None\n        value = inf\n        for action in actions(board):\n            maxValueResult = -inf\n            if terminal(board):\n                maxValueResult = utility(board)\n            else:\n                for action in actions(board):\n                    maxValueResult = max(maxValueResult, minimum(result(board, action)))\n            \n            if maxValueResult < value:\n                value = maxValueResult\n                optimalMove = action\n\n    return optimalMove\n","repo_name":"cjto/HarvardEdX-CS50s-Intro-to-AI","sub_path":"tictactoe/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":4782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13881066516","text":"#import all the required libraries\nimport torch\nfrom flask import Flask, render_template, request\nfrom instruct_pipeline import InstructionTextGenerationPipeline\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\napp = Flask(__name__)\n\n#initialize tokenizer and model\ntokenizer = AutoTokenizer.from_pretrained(\"databricks/dolly-v2-3b\", padding_side=\"left\")\nmodel = AutoModelForCausalLM.from_pretrained(\"databricks/dolly-v2-3b\", device_map=\"auto\", torch_dtype=torch.bfloat16)\n\n#create a text generation pipeline\ngenerate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/generate')\ndef generate():\n    #prompt from the request arguments\n    prompt = request.args.get('prompt')\n    if not prompt:\n        return render_template('index.html')\n    #generates text based on the prompt\n    generated_text = generate_text(prompt)\n    return render_template('index.html', prompt=prompt, generated_text=generated_text)\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0',port=80)\n\n\n\n\n\n","repo_name":"aiprasad/test3","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38698573426","text":"from __future__ import unicode_literals\n\nimport optparse\nimport threading\nimport time\nimport sys\nimport os\nimport Queue\nimport logging\n\nfrom components.cfg_parse_strzip import SafeConfigParserStringZip\nfrom components.rm_session import Session\nfrom components.utils import *\n\n\nclass ResourceManagerMainLoop(threading.Thread):\n    def __init__(self,\n                 pipe_cmdresp_write,\n                 pipe_uiinfo_update_write,\n                 queue_uicmds,\n                 start_options,\n                 session_dirs):\n        self.logger = logging.getLogger(\"RM.MainLoop\")\n        self.logger.debug(\"initialize ResourceManagerMainLoop object\")\n\n        threading.Thread.__init__(self)\n        self.pipe_cmdresp_write = pipe_cmdresp_write\n        self.pipe_uiinfo_update_write = pipe_uiinfo_update_write\n        self.queue_uicmds  = queue_uicmds\n        self.start_options = start_options\n        self.session_dirs = session_dirs\n\n    def run (self):\n        \"\"\"\n        Thread start method. Initialize main loop.\n        \"\"\"\n        start_options = self.start_options\n        self.logger.info(\"thread started.\")\n        self.session = Session(start_options, self.session_dirs)\n        self.logger.info(\"LOAD INITIAL SESSION CONFIGURATION...\")\n        self.session.load_initial_session_config()\n\n        cloudstring = ''\n        if self.session.inicfg.ec2.use:\n            cloudstring += 'EC2, '\n        for nimbus_cloud in self.session.nimbus_clouds:\n            cloudstring += 'Nb%s, ' % nimbus_cloud.cloud_index\n        self.request_update_uiinfo(dict(\n            txt_cloud=cloudstring.rstrip(\", \"),\n            txt_name=self.session.inicfg.session.name))\n\n        self.logger.info(\"FINISH SESSION SETUP...\")\n        self.session.finish_setup()\n        self.main_loop()\n\n    def main_loop(self):\n        \"\"\"\n        Resource Manager's main loop. Do all the work after session init.\n        Provide interactive mode.\n        \"\"\"\n        self.logger.debug(\"main_loop() started.\")\n\n        # init some needed variables\n        self.sqs_last_checked = time.time()\n        self.sdb_last_checked = time.time()\n        self.ec2_inststates_last_checked = time.time()\n\n        pause_loop = True\n        timeout = 1\n\n        self.ui_msg((\"Interactive mode started. Type\"\n            \" 'help' to get a list of available commands.\"))\n        while True:\n            # check user-given command (`uicmd` is a unicode object!)\n            uicmd = self.poll_command_queue(timeout)\n            if uicmd is not None:\n                self.ui_msg('>>> '+uicmd)\n                if uicmd == 'quit':\n                    self.ui_msg('end RM main loop')\n                    break\n                    # future: continue statement .. really quit? enter again..\n                elif uicmd == 'help':\n                    self.display_help_message()\n                elif uicmd == 'pause':\n                    pause_loop = True\n                    self.ui_msg((\"ResourceManagerMainLoop paused.\"\n                        \" Enter 'start' to go on.\"))\n                elif uicmd == 'start':\n                    pause_loop = False\n                    self.ui_msg(\"ResourceManagerMainLoop starts/continues.\")\n                elif uicmd == 'poll_sqs':\n                    self.ui_msg(\"Manual SQS monitoring data update triggered.\")\n                    self.sqs_check()\n                elif uicmd == 'poll_sdb':\n                    self.ui_msg(\"Manual SDB monitoring data update triggered.\")\n                    self.sdb_check()\n                elif uicmd.startswith('run_vms'):\n                    self.run_vms(uicmd)\n                elif uicmd.startswith('query_fact_rp'):\n                    self.nimbus_query_factory_rp(uicmd)\n                elif uicmd.startswith('query_workspace'):\n                    self.nimbus_query_workspace(uicmd)\n                elif uicmd.startswith('destroy_workspace'):\n                    self.nimbus_destroy_workspace(uicmd)\n                elif uicmd.startswith('show_vm_info'):\n                    self.show_vm_info(uicmd)\n                elif uicmd.startswith('set_highest_prio'):\n                    self.set_highest_prio(uicmd)\n                elif uicmd.startswith('write_jmi_cfg_file'):\n                    self.write_jmi_cfg_file()\n                elif uicmd.startswith('softkill_vm'):\n                    self.softkill_vm(uicmd)\n                else:\n                    self.ui_msg('unknown command')\n\n            # continue with automatic process, if not paused:\n            if not pause_loop:\n                self.do_sqs_sdb_update_if_necessary()\n\n            # check nimbus cloud subprocesses\n            # this is done each main loop turn, it's not logged. (only success/\n            # error events are logged, but not when subprocesses didn't return)\n            self.check_nimbus_cloud_client_wrappers()\n\n            # check EC2 instances that were requested to start recently.\n            # do it based on self.session.inicfg.ec2.instance_state_pollinterval\n            self.check_runinstances_request_states_if_necessary()\n\n            # build the `started_vms_string` and displays it in the UI\n            self.display_started_vms()\n\n    def write_jmi_cfg_file(self):\n        \"\"\"\n        Instruct the Session class to write a config file that contains all\n        information the JobManagementInterface needs.\n        \"\"\"\n        self.ui_msg((\"Instructed to write Clobi's Job Management Interface\"\n            \" configuration file...\"))\n        self.session.write_job_management_interface_config_file()\n\n    def display_started_vms(self):\n        \"\"\"\n        Generate a string like \"EC2: 3    Nb1: 15    Nb2: 1\" and display it i\n        the UI info panel.\n        \"\"\"\n        started_vms_string = self.session.get_started_vms_string()\n        if started_vms_string is not False:\n            self.request_update_uiinfo(dict(txt_started_vms=started_vms_string))\n\n    def check_runinstances_request_states_if_necessary(self):\n        \"\"\"\n        Check states of EC2 instances that were requested to start recently.\n        This updates save.session.vms about success/error while starting.\n        After this information is gathered, the corresponding boto ec2\n        reservation objects will be deleted (information about VMs/instances\n        in save.session.vms is the only information left).\n        \"\"\"\n        now = time.time()\n        lastchecked = self.ec2_inststates_last_checked\n        interval = self.session.inicfg.ec2.instance_state_pollinterval\n        next_state_check_in = abs(min(0, (now - (lastchecked + interval))))\n        if next_state_check_in == 0:\n            self.session.ec2.check_runinstances_request_states()\n            self.ec2_inststates_last_checked = time.time()\n\n    def check_nimbus_cloud_client_wrappers(self):\n        \"\"\"\n        Check for running/returned subprocesses of nimbus cloud client.\n        This will e.g. trigger update of save.session.vms file if there was\n        success/error with starting some VM(s). After this information is\n        gathered, the corresponding Nimbus Cloud Client Wrapper objects will be\n        deleted (information about VMs in save.session.vms is the only\n        information left).\n        \"\"\"\n        for nimbus_cloud in self.session.nimbus_clouds:\n            nimbus_cloud.check_nimbus_cloud_client_wrappers()\n\n    def nimbus_query_factory_rp(self, cmd):\n        words = [word.lower() for word in cmd.split()]\n        nbcldidcs = self.session.nimbus_cloud_indices()\n        if len(words) == 2 and words[0] == 'query_fact_rp':\n            cloud = words[1]                 # cloud name\n            index = self.session.nbx(cloud)  # valid nimbus cloud index or False\n            if index:\n                self.session.nimbus_query_factory_rp(cloud_index=index)\n                return\n        self.ui_msg((\"better: e.g. 'query_fact_rp Nb1' or 'query_fact_rp Nb13'.\"\n            \" Nimbus cloud indices available: %s\"%str(nbcldidcs)))\n\n    def nimbus_query_workspace(self, cmd):\n        words = [word.lower() for word in cmd.split()]\n        if len(words) == 2 and words[0] == 'query_workspace':\n            entered_vm_id = words[1]\n            vm_info = self.session.get_vm_info_from_file(vm_id=entered_vm_id)\n            if vm_info:\n                if vm_info['EC2']:\n                    self.ui_msg(\"This VM corresponds to an EC2 instance.\")\n                elif vm_info['Nimbus']:\n                    if vm_info['eprfile']:\n                        self.session.nimbus_query_workspace(\n                            cloud_index=vm_info['cloudindex'],\n                            eprfile=vm_info['eprfile'],\n                            vm_id=vm_info['vm_id'])\n                    else:\n                        self.logger.error((\"No EPR file defined in \"\n                            \"save.session.vms for VM %s\" % entered_vm_id))\n                else:\n                    self.logger.error(\"This state should never be reached :-)\")\n            return\n        self.ui_msg(\"better: e.g. 'query_workspace vm-0133'.\")\n\n    def nimbus_destroy_workspace(self, cmd):\n        words = [word.lower() for word in cmd.split()]\n        if len(words) == 2 and words[0] == 'destroy_workspace':\n            entered_vm_id = words[1]\n            vm_info = self.session.get_vm_info_from_file(vm_id=entered_vm_id)\n            if vm_info:\n                if vm_info['EC2']:\n                    self.ui_msg(\"This VM corresponds to an EC2 instance.\")\n                elif vm_info['Nimbus']:\n                    if vm_info['eprfile']:\n                        if self.yes_no((\"Really destroy %s (workspace on\"\n                        \" Nimbus cloud %s with EPR file %s)?\"\n                        % (entered_vm_id,vm_info['cloudindex'],\n                        vm_info['eprfile']))):\n                            self.session.nimbus_destroy_workspace(\n                                cloud_index=vm_info['cloudindex'],\n                                eprfile=vm_info['eprfile'],\n                                vm_id=vm_info['vm_id'])\n                        else:\n                            self.ui_msg('aborted.')\n                    else:\n                        self.logger.error((\"No EPR file defined in \"\n                            \"save.session.vms for VM %s\" % entered_vm_id))\n                else:\n                    self.logger.error(\"This state should never be reached :-)\")\n            return\n        self.ui_msg(\"better: e.g. 'query_workspace vm-0133'.\")\n\n    def show_vm_info(self, cmd):\n        \"\"\"\n        Try to read and display VM info from save.session.vms\n        \"\"\"\n        words = [word.lower() for word in cmd.split()]\n        if len(words) == 2 and words[0] == 'show_vm_info':\n            entered_vm_id = words[1]\n            vm_info = self.session.get_vm_info_from_file(vm_id=entered_vm_id)\n            if vm_info:\n                self.ui_msg('\\n'+repr(vm_info))\n            return\n        self.ui_msg(\"better: e.g. 'show_vm_info vm-0001'.\")\n\n    def softkill_vm(self, cmd):\n        \"\"\"\n        Instruct to set softkill flag for given VM ID. Check if VM is listed in\n        save.session.vms.\n        \"\"\"\n        words = [word.lower() for word in cmd.split()]\n        if len(words) == 2 and words[0] == 'softkill_vm':\n            entered_vm_id = words[1]\n            vm_info = self.session.get_vm_info_from_file(vm_id=entered_vm_id)\n            if vm_info:\n                if self.yes_no(\"Set softkill flag for VM %s?\" % entered_vm_id):\n                    self.session.softkill_vm(entered_vm_id)\n            return\n        self.ui_msg(\"better: e.g. 'softkill_vm vm-0001'.\")        \n        \n    def set_highest_prio(self, cmd):\n        \"\"\"\n        Validate input. Distinguish between higher/lower HP than before.\n        Instruct HP change.\n        \"\"\"\n        words = [word.lower() for word in cmd.split()]\n        if len(words) == 2 and words[0] == 'set_highest_prio':\n            try:\n                entered_new_hp = int(words[1])\n            except ValueError:\n                self.logger.error(\"entered value is not a number.\")\n                return\n            if entered_new_hp == self.session.sdb_session.highest_priority:\n                self.ui_msg((\"Highest Priority is already set to\"\n                    % entered_new_hp))\n                return\n            if entered_new_hp < self.session.sdb_session.highest_priority:\n                self.ui_msg((\"Entered value is smaller than current HP setting.\"\n                    \" Do you really want to delete (a) queue(s)? Deletion\"\n                    \" fails if a queue is not empty.\"))\n            if entered_new_hp > self.session.sdb_session.highest_priority:\n                self.ui_msg((\"Entered value is higher than current HP setting.\"\n                    \" Do you really want to create (a) queue(s)? This increases\"\n                    \" cost a bit.\"))\n            if self.yes_no(\"Please confirm\"):\n                self.session.change_highest_priority(entered_new_hp)\n            return\n        self.ui_msg(\"better: e.g. 'set_highest_prio 3'.\")\n\n    def run_vms(self, cmd):\n        \"\"\"\n        check for \"run_vms cloud_name number_of_vms\" structure.\n        E.g.: \"run_vms EC2 15\" or \"run_vms nb1 2\"\n        invoke run command after re-insurance.\n        \"\"\"\n        words = [word.lower() for word in cmd.split()]\n        nbcldidcs = self.session.nimbus_cloud_indices()\n        if len(words) == 3 and words[0] == 'run_vms':\n            cloud = words[1]                 # cloud name\n            number = words[2]                # number of vms\n            index = self.session.nbx(cloud)  # valid nimbus cloud index or False\n            if (cloud == 'ec2' or index) and number.isdigit():\n                number = int(number)\n                if cloud == 'ec2':\n                    if self.yes_no('Run %s VM(s) on EC2?' % number):\n                        self.session.run_vms_ec2(number)\n                    else:\n                        self.ui_msg('aborted.')\n                elif self.yes_no(('Run %s VM(s) on Nimbus Cloud %s?'\n                %(number,index))):\n                    self.session.run_vms_nimbus(index,number)\n                else:\n                    self.ui_msg('aborted.')\n                return\n        self.ui_msg((\"better: e.g. 'run_vms EC2 3' or 'run_vms Nb4 2'.\"\n                     \" Nimbus cloud indices available: %s\"%str(nbcldidcs)))\n\n    def yes_no(self, question):\n        \"\"\"\n        Ask `question`, read input until \"yes\" or \"no\" comes and return True,\n        False, respectively.\n        \"\"\"\n        while True: # wait for instring to be a known command\n            self.ui_msg(question+\" [yes/no]\")\n            uicmd = self.poll_command_queue(timeout=None) # infinite block\n            self.ui_msg('>>> '+uicmd)\n            if uicmd == 'yes': return True\n            if uicmd == 'no': return False\n\n    def do_sqs_sdb_update_if_necessary(self):\n        \"\"\"\n        Check current time against last checked times, using the user-given\n        poll intervals to decide if a new SQS / SDB check has to be performed.\n        \"\"\"\n        now = time.time()\n        next_sqs_check_in = min(0, (now -\n            (self.sqs_last_checked +\n                self.session.inicfg.sqs.monitor_queues_pollinterval)))\n        next_sdb_check_in = min(0, (now -\n            (self.sdb_last_checked +\n                self.session.inicfg.sdb.monitor_vms_pollinterval)))\n        self.request_update_uiinfo(dict(\n            txt_sqs_upd=str(int(round(next_sqs_check_in))).zfill(5)+\" s\",\n            txt_sdb_upd=str(int(round(next_sdb_check_in))).zfill(5)+\" s\"))\n        if next_sqs_check_in == 0:\n            self.logger.info(\"Automatic SQS job number update triggered.\")\n            self.sqs_check()\n        if next_sdb_check_in == 0:\n            self.logger.info(\"Automatic SDB monitoring data update triggered.\")\n            self.sdb_check()\n\n    def sdb_check(self):\n        \"\"\"\n        Query SDB for state of VMs / JAa\n        \"\"\"\n        self.logger.debug(\"ResourceManagerMainLoop.sdb_check() called\")\n        # instruct to receive total number of running job agents.\n        # if not False, then it is an integer :-)\n        nbr_running_jobagents = self.session.sdb_session.count_jobagents()\n        if nbr_running_jobagents is not False:\n            self.request_update_uiinfo(dict(\n                txt_total_nbr_jas=str(nbr_running_jobagents)))\n        self.sdb_last_checked = time.time()\n\n    def sqs_check(self):\n        \"\"\"\n        Query SQS queues to receive the approximate number of jobs contained in\n        them.\n        \"\"\"\n        self.logger.debug(\"ResourceManagerMainLoop.sqs_check() called\")\n        self.session.sqs_session.query_queues()\n        self.sqs_last_checked = time.time()\n        stringlist = []\n        for p, j in self.session.sqs_session.queue_jobnbrs_laststate.items():\n            stringlist.append(\"P\"+str(p).zfill(2)+\": %s job(s)\" % j)\n        self.request_update_uiinfo(dict(txt_sqs_jobs=\"\\n\".join(stringlist)))\n\n    def ui_msg(self, msg):\n        \"\"\"\n        Send message to UI\n        \"\"\"\n        os.write(self.pipe_cmdresp_write, (msg+\"\\n\").encode('UTF-8'))\n\n    def display_help_message(self):\n        \"\"\"poll\n        Write help message to `self.pipe_cmdresp_write` -> UI\n        \"\"\"\n        helpstring = (\"Available commands:\"\n            +\"\\n* help:                   Display this help message.\"\n            +\"\\n* quit:                   Quit Resource Manager.\"\n            +\"\\n* pause:                  Pause main loop (stop automatic operation).\"\n            +\"\\n* start:                  Start main loop / continue after break.\"\n            +\"\\n* run_vms cloud X:        Run X VMs on cloud (EC2|NbY). E.g. 'run_vms Nb1 2'.\"\n            +\"\\n* query_fact_rp NbX:      Query Nimbus Factory RP.\"\n            +\"\\n* query_workspace vm-X:   Query Nimbus workspace corresponding to VM ID.\"\n            +\"\\n* destroy_workspace vm-X: Destroy Nimbus workspace corresponding to VM ID.\"\n            +\"\\n* show_vm_info vm-X:      Show VM info as stored in save.session.vms.\"\n            +\"\\n* softkill_vm vm-X:       Set softkill flag for given VM.\"\n            +\"\\n* set_highest_prio HP:    Set highest priority to HP; create/delete queues.\"\n            +\"\\n* poll_sdb:               Update SDB monitoring data.\"\n            +\"\\n* poll_sqs:               Update SQS monitoring data.\"\n            +\"\\n* write_jmi_cfg_file:     Write Job Management Interface config file.\")\n        self.ui_msg(helpstring)\n\n    def poll_command_queue(self, timeout):\n        \"\"\"\n        Poll queue in a blocking manner and return received item.\n        If there is nothing within timeout, return None\n        \"\"\"\n        try:\n            return self.queue_uicmds.get(block=True, timeout=timeout)\n        except Queue.Empty:\n            # os.write(self.pipe_cmdresp_write, '1 sec and got nothing :-(')\n            return None\n\n    def request_update_uiinfo(self, update_dict):\n        \"\"\"\n        Make ConfigParser Config from update_dict, delimit with %% and && and\n        send via pipe to GUI. Section \"uiinfo\" is hardcoded on both sides.\n        \"\"\"\n        config = SafeConfigParserStringZip()\n        config.add_section('uiinfo')\n        for key, value in update_dict.items():\n            config.set(\n                'uiinfo'.encode('utf-8'),\n                key.encode('utf-8'),value.encode('utf-8'))\n        configstring = \"%%\"+config.write_to_string().decode('utf-8')+\"&&\"\n        os.write(self.pipe_uiinfo_update_write, configstring.encode('utf-8'))\n","repo_name":"jgehrcke/clobi","sub_path":"resourcemanager/components/rm_main_loop.py","file_name":"rm_main_loop.py","file_ext":"py","file_size_in_byte":19456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72885489850","text":"import os.path,math,sys\r\nfrom ..SlideBase import SlideBase\r\nimport cv2\r\nimport numpy as np\r\nimport json\r\nfrom . import kfbslide\r\nfrom .kfb_lowlevel import kfbslide_close\r\nfrom threading import Lock\r\ndel_lock=Lock()\r\n\r\nclass KfbSlide(SlideBase):\r\n\r\n    def __init__(self,filename):\r\n        self.filename = filename\r\n        self.slide = kfbslide.open_kfbslide(filename)\r\n        self.width, self.height = self.slide.dimensions\r\n        SlideBase.__init__(self)\r\n\r\n\r\n    def read(self, location=[0,0], size=None, scale=1, greyscale=False):\r\n        '''\r\n        :param location: (x, y) at level=0\r\n        :param size: (width, height)\r\n        :param scale: resize scale, scale>1 -> zoom out, scale<1 -> zoom in\r\n        :param greyscale: if True, convert image to greyscale\r\n        :return: a numpy image,  np_img.shape=[height, width, channel=1 or 3]\r\n        '''\r\n        if size == None:\r\n            width, height = self.width, self.height\r\n        else:\r\n            width, height = size\r\n\r\n        crop_start_x, crop_start_y = location\r\n        crop_level = self.slide.get_best_level_for_downsample(scale)\r\n        resize_ratio = self.slide.level_downsamples[crop_level]/scale\r\n\r\n        # make sure the crop region is inside the slide\r\n        crop_start_x, crop_start_y = min(max(crop_start_x, 0), self.width), min(max(crop_start_y, 0), self.height)\r\n        crop_end_x = math.ceil(min(max(width+crop_start_x, 0), self.width))\r\n        crop_end_y = math.ceil(min(max(height+crop_start_y, 0), self.height))\r\n\r\n        crop_width = math.ceil((crop_end_x - crop_start_x)/self.slide.level_downsamples[crop_level])\r\n        crop_height = math.ceil((crop_end_y - crop_start_y)/self.slide.level_downsamples[crop_level])\r\n\r\n        if crop_height == 0 or crop_width == 0:\r\n            return None\r\n\r\n        crop_region = self.slide.read_region(\r\n            location=(crop_start_x, crop_start_y),\r\n            level=crop_level,\r\n            size=(crop_width, crop_height))\r\n\r\n\r\n        if greyscale:\r\n            crop_region = 0.2989*crop_region[:,:,0] + 0.5870*crop_region[:,:,1] + 0.1140*crop_region[:,:,2]\r\n            crop_region = crop_region[:,:,np.newaxis]\r\n\r\n        crop_region = cv2.resize(crop_region, (math.ceil(crop_width*resize_ratio), math.ceil(crop_height*resize_ratio)))\r\n        return crop_region\r\n\r\n\r\n    def saveLabel(self,path):\r\n        try:\r\n            self.slide.associated_images[b'label'].save(path)\r\n        except:\r\n            pass\r\n\r\n    @property\r\n    def mpp(self):\r\n        mpp = None\r\n        try:\r\n            slide_properties = self.slide.properties\r\n            if 'openslide.mpp-x' in slide_properties:\r\n                mpp = float(slide_properties['openslide.mpp-x'])\r\n                return mpp\r\n            with open(os.path.join(os.path.dirname(self.filename), \"index.json\"), \"r\", encoding=\"utf-8\") as f:\r\n                slide_info = json.load(f)\r\n                mpp = slide_info.get(\"mppx\")\r\n                if mpp is not None:\r\n                    return float(mpp)\r\n        except:\r\n            pass\r\n\r\n        return mpp\r\n\r\n    def __del__(self):\r\n        with del_lock:\r\n            kfbslide_close(self.slide._osr)\r\n","repo_name":"dazhangyu123/ACMIL","sub_path":"wsi_core/KfbSlide/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3169,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"33509837809","text":"from isogenies.volcano_depth import find_descending_path\nfrom sage.rings.all import NumberField, GF\nfrom sage.arith.misc import factor\nfrom sage.databases.db_modular_polynomials import ClassicalModularPolynomialDatabase\n\n\n# Class implementing endomorphism ring of elliptic curves over finite fields\n# Computation is done using the isogeny volcanoes (libr.volcano_depth)\nclass Endomorphism_ring:\n\n    def __init__(self, E):\n        self._domain = E\n        self._j_invariant = E.j_invariant()\n        self._trace = None\n        self._order = None\n        self._index = None\n        self._field = None\n        self.endomorphism_ring()\n\n    def __repr__(self):\n        return \"Endomorphism ring of %r\" % self._domain\n\n    # Internal use only\n    def endomorphism_index(self):\n        j = self._j_invariant\n        field = j.parent()\n        t = self._domain.trace_of_frobenius()\n        self._trace = t\n        Dv = t ** 2 - 4 * self._domain.base_field().order()\n        D = Dv.squarefree_part()\n        v = Dv // D\n        if D % 4 != 1:\n            v = v // 4\n        ls = [(i[0], i[1] // 2) for i in list(factor(v)) if i[1] >= 2]\n        u = 1\n        for a in ls:\n            l = a[0]\n            Phi = ClassicalModularPolynomialDatabase()[l]\n            dist = len(find_descending_path(j, Phi, l, special=False)) - 1\n            ex = a[1] - dist\n            u *= l ** ex\n        self._index = u\n\n    # Internal use only\n    def endomorphism_ring(self):\n        k = NumberField(self._domain.frobenius().minpoly(), 'c')\n        self._field = k\n        self.endomorphism_index()\n        basis = k.maximal_order().basis()\n        basis = [basis[0] * self._index, basis[1] * self._index]\n        self._order = k.order(basis)\n\n    # Returns the imaginary quadratic field\n    def field(self):\n        return self._field\n\n    # Returns an instance of sage class Order\n    def order(self):\n        return self._order\n\n    # Returns the conductor of endomorphism ring\n    def conductor(self):\n        return self._index\n","repo_name":"vojtechsu/isogenies","sub_path":"implementation/isogenies/endomorphism_ring.py","file_name":"endomorphism_ring.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14986804465","text":"# Modify the LinkedQueue class (included in the public_files section of the problem statement) by implementing a new method called concatenate. \nfrom easyinput import read\n\nclass Empty(Exception):\n  \"\"\"Error attempting to access an element from an empty container\"\"\"\n  pass\n\nclass LinkedQueue:\n  \"\"\"FIFO queue implementation using a singly linked list for storage.\"\"\"\n\n  #-------------------------- nested _Node class -------------\n  # _ vol dir que és privat. El farem servir dintre de la classe però no fora.\n  class _Node:\n    \"\"\"Lightweight, nonpublic class for storing a singly linked node.\"\"\"\n    __slots__ = '_element', '_next'  # streamline memory usage\n\n    def __init__(self, element, next):\n      self._element = element\n      self._next = next\n      # Sol té un link, el guardo a next!\n\n  #------------------------------- queue methods -------------\n  def __init__(self):\n    \"\"\"Create an empty queue.\"\"\"\n    self._head = None\n    self._tail = None\n    self._size = 0                   \n\n  def __len__(self):\n    \"\"\"Return the number of elements in the queue.\"\"\"\n    return self._size\n\n  def is_empty(self):\n    \"\"\"Return True if the queue is empty.\"\"\"\n    return self._size == 0\n\n  def first(self):\n    \"\"\"Return (but do not remove) the element at the front of the queue. Raise Empty exception if the queue is empty. \"\"\"\n    if self.is_empty():\n      raise Empty('Queue is empty')\n    return self._head._element            \n\n  def dequeue(self):\n    \"\"\"Remove and return the first element of the queue (i.e., FIFO). Raise Empty exception if the queue is empty.\"\"\"\n    if self.is_empty():\n      raise Empty('Queue is empty')\n    answer = self._head._element\n    self._head = self._head._next         # El node que abans feia de head l'he tret. Tant el element com el link de head passen a ser els propis del següent node.\n    self._size -= 1\n    if self.is_empty():                   \n      self._tail = None                   \n    return answer\n\n  def enqueue(self, e):\n    \"\"\"Add an element to the back of queue.\"\"\"\n    newest = self._Node(e, None)          \n    if self.is_empty():\n      self._head = newest                 \n    else:\n      self._tail._next = newest\n    self._tail = newest                   \n    self._size += 1\n\n  def concatenate(self, q):\n    # Insert your implementation below\n    # Ho fare amb el primer node de q i ho generalitzare\n\n    while q._size>0 :\n      if q.is_empty():\n        raise Empty('Queue is empty')\n\n      newest_el = q._head._element\n      q._head = q._head._next\n      q._size -= 1\n      if q.is_empty():                   \n        q._tail = None\n\n    # Ara ja l'he tret de q, el vull ficar a self\n\n      newest = self._Node(newest_el, None)      \n      if self.is_empty():\n        self._head = newest                 \n      else:\n        self._tail._next = newest\n      self._tail = newest                   \n      self._size += 1\n    \n    \n    # Done el concatenament. Falta fer lo del output. \n\n  def __str__(self):\n    # In the implementation of this method, assume the queue instance\n    # can only contain integer numbers. This is only true in the context\n    # of this problem.\n    # Insert your implementation below \n      # Si està empty o no ho ficaré com una exception a main\n    elements = []\n    node_actual = self._head  # Primer node\n    for i in range(self._size):\n      node_actual_elem = node_actual._element\n      elements.append(node_actual_elem)     # Això és una llista de integers\n      node_actual = node_actual._next\n    return((' '.join(str(e) for e in elements)))            # Vull que surtin sense [] de la llista, i a més com a str\n\n\n\n# COMENTARI INDIRECT METHODS: el __ x __ adapta una acció comú a una classe determinada\n# així podem fer len() o str() de un mètode. No es .str()!\n\nif __name__ == '__main__':\n    n = read(int)\n    linkedques_list = []\n    for i in range(n):\n        linkedques_list.append(LinkedQueue())\n    \n        \n    q_n = read(int)          # La queue a la que es refereix. \n    ordre = read(str)           # Si aquesta ordre es enqueu o concatenate haurem de ficar un read(int) més!\n    while ordre is not None:\n\n        if ordre == 'enqueue':\n                e = read(int)       \n                linkedques_list[q_n - 1].enqueue(e)     # Li resto 1 perquè python indexa amb 0! La queue 1 correspon al index 0.\n                print('queue {}: {} enqueued'.format(q_n, e))\n\n        elif ordre == 'first':\n                answer = linkedques_list[q_n - 1].first()\n                print('queue {} first element: {}'.format(q_n, answer))\n                \n        elif ordre == 'len':\n                lenght = len(linkedques_list[q_n - 1])\n                print('queue {} has {} element(s)'.format(q_n, lenght))\n                \n        elif ordre == 'dequeue':\n            if len(linkedques_list[q_n - 1]) == 0:\n                print('queue {} is empty'.format(q_n))\n            else:\n                answer = linkedques_list[q_n - 1].dequeue()\n                print('queue {}: {} dequeued'.format(q_n, answer))\n                \n        elif ordre == 'print':\n                str_a_printejar = str(linkedques_list[q_n - 1])\n                print('queue {}: {}'.format(q_n, str_a_printejar))\n                \n        elif ordre == 'is_empty':\n            if len(linkedques_list[q_n - 1]) == 0:\n                print('queue {} is empty '.format(q_n))\n            else:\n                print('queue {} is not empty '.format(q_n))\n                \n        elif ordre == 'concatenate':\n            q_n_2 = read(int)\n            linkedques_list[q_n - 1].concatenate(linkedques_list[q_n_2 - 1])  # Li he de passar dos queues\n            str_a_printejar_1 = str(linkedques_list[q_n - 1])\n            str_a_printejar_2 = str(linkedques_list[q_n_2 - 1])     \n            print('queues {} and {} concatenated'.format(q_n, q_n_2))\n            print('queue {}: {}'.format(q_n, str_a_printejar_1))\n            print('queue {}: {}'.format(q_n_2, str_a_printejar_2))\n        \n        # Imprescindible per a continuar el bucle!\n\n        q_n = read(int)         # La queue a la que es refereix\n        ordre = read(str)       ","repo_name":"RaulAdSe/DS-ALG-problems","sub_path":"Stacks, Queues, and Linked Lists/Queue Concatenation/simp_linked_queue_concatenation.py","file_name":"simp_linked_queue_concatenation.py","file_ext":"py","file_size_in_byte":6131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26164698544","text":"from aiogram import types, Dispatcher\nfrom create_bot import bot, dp\nfrom keyboard import kb_client\nfrom aiogram.types import ReplyKeyboardRemove\n\n\n@dp.message_handler(commands=['start', 'help'])\nasync def commandsStart(message: types.Message):\n    try:\n        await bot.send_message(message.from_user.id, \"Здрасте, я ожил\", reply_markup=kb_client)\n        await message.delete()\n    except:\n        await message.reply('Общение с ботом через ЛС, напишите ему: https://t.me/zetnisbot')\n\n\n@dp.message_handler(commands=['reply'])\nasync def reply(message: types.Message):\n    try:\n        await bot.send_message(message.from_user.id, \"Здрасте, я ожил\", reply_markup=ReplyKeyboardRemove())\n        await message.delete()\n    except:\n        await message.reply('Общение с ботом через ЛС, напишите ему: https://t.me/zetnisbot')\n\n\ndef registerHandlersClient(disp: Dispatcher):\n    disp.register_message_handler(commandsStart, commands=['start', 'help'])\n    disp.register_message_handler(reply, commands=['reply'])\n\n","repo_name":"MCtop4ik/pinkiTelebotio","sub_path":"handlers/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44718914425","text":"class Solution(object):\n    \n    __author__ = 'tangjialiang'\n    __V__ = 'success_V'\n    \n    def intersection(self, nums1, nums2):\n        \"\"\"\n        :type nums1: List[int]\n        :type nums2: List[int]\n        :rtype: List[int]\n        \"\"\"\n        \n        ans = []\n        \n        nums1 = list(set(nums1))\n        nums2 = list(set(nums2))\n        \n        for num in nums1:\n            if num in nums2:\n                ans.append(num) \n            \n        return ans \n        \n","repo_name":"tjlcast/leetcode","sub_path":"Intersection_of_Two_Arrays_349.py","file_name":"Intersection_of_Two_Arrays_349.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74442606647","text":"from random import randint\r\n\r\n\r\nclass Casino:\r\n    def __init__(self, name: str):\r\n        self.name = name\r\n        self.customers = []\r\n        self.balance = 1_500_000_000\r\n\r\n    def show_info(self):\r\n        print(self.name, \"has\", len(self.customers), \"customers, and the balance is\", self.balance, \"USD\")\r\n\r\n    def new_customers(self, amount=100):\r\n        self.customers = []\r\n\r\n        for _ in range(0, amount):\r\n            name = names[randint(0, 4)]\r\n            second_name = second_names[randint(0, 4)]\r\n            balance = randint(50, 1000)\r\n\r\n            client = Customer(name, second_name, balance)\r\n\r\n            self.customers.append(client)\r\n\r\n    def check_customer(self, customer, fee):\r\n        if customer.balance < fee:\r\n            # print(\"Not enough money!\")\r\n            return False\r\n\r\n        self.get_money(customer, fee)\r\n\r\n        return True\r\n\r\n    def get_money(self, customer, fee):\r\n        customer.balance -= fee\r\n        self.balance += fee\r\n\r\n    def give_money(self, customer, amount):\r\n        self.balance -= amount\r\n        customer.balance += amount\r\n\r\n    def slots(self, customer):\r\n        allowed = self.check_customer(customer, 5)\r\n\r\n        if not allowed:\r\n            return\r\n\r\n        if randint(0, 4) == 0:\r\n            #print(\"You won 3x times the money you put in\")\r\n            self.give_money(customer, 15)\r\n        else:\r\n            #print(\"You lost!\")\r\n            pass\r\n\r\n    def cups(self, customer):\r\n        allowed = self.check_customer(customer, 10)\r\n\r\n        if not allowed:\r\n            return\r\n\r\n        if randint(0, 2) == 0:\r\n            #print(\"You won 2x times the money you put in\")\r\n            self.give_money(customer, 20)\r\n        else:\r\n            #print(\"You lost!\")\r\n            pass\r\n\r\n    def plinko(self, customer):\r\n        allowed = self.check_customer(customer, 20)\r\n\r\n        if not allowed:\r\n            return\r\n\r\n        index = 0\r\n\r\n        for _ in range(0, 16):\r\n            if randint(0, 1):\r\n                index += 1\r\n            else:\r\n                index -= 1\r\n\r\n        index = abs(index)\r\n\r\n        multipliers = [0.1, 0.2, 0.3, 0.5, 0.75, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\r\n\r\n        self.give_money(customer, 20 * multipliers[index])\r\n\r\n    def saper(self, customer):\r\n        allowed = self.check_customer(customer, 50)\r\n\r\n        if not allowed:\r\n            return\r\n\r\n        bombs = [0, 1, 2]\r\n\r\n        dead = False\r\n\r\n        for _ in range(0, 15):\r\n            if randint(0, 24) in bombs:\r\n                dead = True\r\n                break\r\n\r\n        if not dead:\r\n            self.give_money(customer, 250)\r\n\r\n\r\nclass Customer:\r\n    def __init__(self, name: str, second_name: str, balance: int):\r\n        self.name = name\r\n        self.secondName = second_name\r\n        self.balance = balance\r\n        self.first_balance = balance\r\n\r\n    def show_info(self):\r\n        print(self.name, self.secondName, \"went from\", self.first_balance, \"to\", self.balance)\r\n\r\n\r\n# Constants\r\nAMOUNT_OF_CLIENTS = 100\r\nAMOUNT_OF_DAYS_TO_SIMULATE = 30\r\nAMOUNT_OF_SLOTS_SIMULATIONS = 1000\r\nAMOUNT_OF_CUPS_SIMULATIONS = 1000\r\nAMOUNT_OF_PLINKO_SIMULATIONS = 1000\r\nAMOUNT_OF_SAPER_SIMULATIONS = 1000\r\nAMOUNT_OF_DAYS_FOR_NEW_CLIENTS = 5\r\n\r\ncasino = Casino(\"Golden grin\")\r\n\r\n# Client generator\r\nnames = [\"John\", \"Max\", \"Caroline\", \"Andrew\", \"Donald\"]\r\nsecond_names = [\"Bon Jovi\", \"Kolonko\", \"Kozub\", \"Tate\", \"Trump\"]\r\n\r\ncasino.new_customers(AMOUNT_OF_CLIENTS)\r\n\r\n# Casino simulation\r\nfor i in range(0, AMOUNT_OF_DAYS_TO_SIMULATE):\r\n    if i and i % AMOUNT_OF_DAYS_FOR_NEW_CLIENTS == 0:\r\n        casino.new_customers(AMOUNT_OF_CLIENTS)\r\n\r\n    for _ in range(0, AMOUNT_OF_SLOTS_SIMULATIONS):\r\n        casino.slots(casino.customers[randint(0, len(casino.customers) - 1)])\r\n\r\n    for _ in range(0, AMOUNT_OF_CUPS_SIMULATIONS):\r\n        casino.cups(casino.customers[randint(0, len(casino.customers) - 1)])\r\n\r\n    for _ in range(0, AMOUNT_OF_PLINKO_SIMULATIONS):\r\n        casino.plinko(casino.customers[randint(0, len(casino.customers) - 1)])\r\n\r\n    for _ in range(0, AMOUNT_OF_SAPER_SIMULATIONS):\r\n        casino.saper(casino.customers[randint(0, len(casino.customers) - 1)])\r\n\r\nfor client in casino.customers:\r\n    client.show_info()\r\n\r\ncasino.show_info()\r\n","repo_name":"PyzMenPL/Casino-simulation","sub_path":"Casino.py","file_name":"Casino.py","file_ext":"py","file_size_in_byte":4255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38638892968","text":"import string\r\n\r\nclass Solver:\r\n    def __init__(self, word_list, alphabet=string.ascii_lowercase):\r\n        self.master_word_list = set(word_list)\r\n        self.word_length = len(word_list[0])\r\n        self.alphabet = alphabet\r\n\r\n    def available_letters(self, warm_letters, cold_letters):\r\n        all_available_letters = set(self.alphabet) - cold_letters\r\n        letters_by_slot = [all_available_letters.copy() for _ in range(self.word_length)]\r\n        for i, to_remove_from_slot in enumerate(warm_letters):\r\n            letters_by_slot[i] -= to_remove_from_slot\r\n        return letters_by_slot\r\n\r\n    def transition(self, state, letter, index):\r\n        new_state = state[:]\r\n        new_state[index] = letter\r\n        return new_state\r\n\r\n    def successors(self, state, available_letters):\r\n        if None not in state:\r\n            return []\r\n\r\n        slot_to_fill = state.index(None)\r\n        for letter in available_letters[slot_to_fill]:\r\n            yield self.transition(state, letter, slot_to_fill)\r\n\r\n    def is_valid(self, state, warm_letters):\r\n        all_warm_letters = set().union(*warm_letters)\r\n        return all_warm_letters.issubset(set(state))\r\n\r\n    def is_terminal(self, state):\r\n        return all(state)\r\n\r\n    def search(self, initial_state, warm_letters, cold_letters):\r\n        possible_words = set()\r\n        available_letters = self.available_letters(warm_letters, cold_letters)\r\n        frontier = [initial_state]\r\n        while frontier:\r\n            current = frontier.pop(0)\r\n            for state in self.successors(current, available_letters):\r\n                if self.is_terminal(state):\r\n                    if self.is_valid(state, warm_letters):\r\n                        possible_words.add(''.join(state))\r\n                else:\r\n                    frontier.append(state)\r\n        return possible_words\r\n\r\n    def possible_answers(self, initial_state, warm_letters, cold_letters):\r\n        search_results = self.search(initial_state, warm_letters, cold_letters)\r\n        return search_results & self.master_word_list\r\n","repo_name":"jakeoeding/wordle-solver","sub_path":"wordle_solver/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32967945328","text":"import os\n\nfrom audiomentations import Compose, AddGaussianNoise, PitchShift, Reverse, Shift, TimeStretch\nimport librosa\nimport numpy as np\n\n\nnp.random.seed(42)\n\n\ndef truncate_or_fill(input_waveform: np.ndarray, samplerate: int, target_waveform_duration: float) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the alraedy loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n    :param target_waveform_duration: float, the duration all waveforms should have.\n\n    :return truncated_waveform or filled_waveform: np.ndarray, the truncated or filled waveform.\n\n    Either truncates the wavefrom when it is longer than the target duration or fills the waveform if it is shorter.\n    Filling is done by repeating the waveform.\n    \"\"\"\n    target_waveform_length = target_waveform_duration * samplerate\n\n    if len(input_waveform) >= target_waveform_length:\n        # truncate\n        truncated_waveform = input_waveform[0:target_waveform_length]\n        return truncated_waveform\n    else:\n        # fill\n        # divide target by current length and add 1 to make sure that the new length is higher than the target length\n        repitition_times = int(target_waveform_length/len(input_waveform) + 1)\n        repeated_waveform = np.repeat(input_waveform, repitition_times)\n\n        filled_waveform = repeated_waveform[0:target_waveform_length]\n        return filled_waveform\n\n\ndef _add_gaussian_noise(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Adds gaussain noise onto the waveform.\n    \"\"\"\n    augment_with_gaussian_noise = Compose([\n        AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=1.0)\n    ])\n    transformed_wav = augment_with_gaussian_noise(samples=input_waveform, sample_rate=samplerate)\n\n    return transformed_wav\n\n\ndef _pitch_shift(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Applies a pitch to the waveform.\n    \"\"\"\n    augment_with_pitch_shift = Compose([\n        PitchShift(min_semitones=-4, max_semitones=4, p=1.0)\n    ])\n    transformed_wav = augment_with_pitch_shift(samples=input_waveform, sample_rate=samplerate)\n\n    return transformed_wav\n\n\ndef _reverse(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Reverses the complete waveform.\n    \"\"\"\n    augment_with_reverse = Compose([\n        Reverse(p=1.0)\n    ])\n    transformed_wav = augment_with_reverse(samples=input_waveform, sample_rate=samplerate)\n\n    return transformed_wav\n\n\ndef _shift(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Shifts a part of the waveform.\n    \"\"\"\n    augment_with_shift = Compose([\n        Shift(min_fraction=-0.5, max_fraction=0.5, fade=True, p=1.0)\n    ])\n    transformed_wav = augment_with_shift(samples=input_waveform, sample_rate=samplerate)\n\n    return transformed_wav\n\n\ndef _time_stretch(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Stretches the waveform in time. Makes the waveform either playing faster or slower.\n    \"\"\"\n    augment_with_time_stretch = Compose([\n        TimeStretch(min_rate=0.8, max_rate=1.25, p=1.0)\n    ])\n    transformed_wav = augment_with_time_stretch(samples=input_waveform, sample_rate=samplerate)\n\n    return transformed_wav\n\n\ndef _shift_in_time(input_waveform: np.ndarray, samplerate: int) -> np.ndarray:\n    \"\"\"\n    :param input_waveform: np.ndarray, the already loaded waveform as a numpy array.\n    :param samplerate: int, the sample rate of the waveform.\n\n    :return transformed_wav: np.ndarray, the transfomred waveform.\n\n    Shifts the input waveform by between 1 and up to 9 seconds. The second amount is chosen\n    randomly.\n    \"\"\"\n    shift_steps = np.random.randint(1, 10)\n    shifted_wav = np.roll(input_waveform, int(shift_steps * samplerate))\n    \n    return shifted_wav\n\n\navailable_functions = {\n    \"add_gaussian_noise\": _add_gaussian_noise,\n    \"pitch_shift\": _pitch_shift,\n    \"reverse\": _reverse,\n    \"shift\": _shift,\n    \"time_stretch\": _time_stretch,\n    \"shift_in_time\": _shift_in_time\n}\n","repo_name":"Glassmasters/BirdCLEF_Glassmasters","sub_path":"src/preprocess/audio_waveform/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":5127,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"2473330658","text":"\"\"\"LFFD Demo.\"\"\"\nimport os, sys\nimport argparse\nimport cv2\nimport time\nimport mxnet as mx\nimport numpy as np\n\nsys.path.append(\"..\")\nfrom accuracy_evaluation import predict\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='LFFD Demo.')\n    parser.add_argument('--version', type=str, default='v2',\n                        help='The version of pretrained model, now support \"v1\" and \"v2\".')\n    parser.add_argument('--mode', type=str, default='image',\n                        help='The format of input data, now support \"image\" of jpg and \"video\" of mp4.')\n    parser.add_argument('--use-gpu', type=bool, default=False,\n                        help='Default is cpu.')\n    parser.add_argument('--data', type=str, default='./data',\n                        help='The path of input and output file.')\n    args = parser.parse_args()\n    return args\n\n\ndef main():\n    args = parse_args()\n    # context list\n    if args.use_gpu:\n        ctx = mx.gpu(0)\n    else:\n        ctx = mx.cpu()\n\n    if args.version == 'v1':\n        from config_farm import configuration_10_320_20L_5scales_v1 as cfg\n\n        symbol_file_path = '../symbol_farm/symbol_10_560_25L_8scales_v1_deploy.json'\n        model_file_path = '../saved_model/configuration_10_560_25L_8scales_v1/train_10_560_25L_8scales_v1_iter_1400000.params'\n    elif args.version == 'v2':\n        from config_farm import configuration_10_320_20L_5scales_v2 as cfg\n\n        symbol_file_path = '../symbol_farm/symbol_10_320_20L_5scales_v2_deploy.json'\n        model_file_path = '../saved_model/configuration_10_320_20L_5scales_v2/train_10_320_20L_5scales_v2_iter_1800000.params'\n    else:\n        raise TypeError('Unsupported LFFD Version.')\n\n    face_predictor = predict.Predict(mxnet=mx,\n                                     symbol_file_path=symbol_file_path,\n                                     model_file_path=model_file_path,\n                                     ctx=ctx,\n                                     receptive_field_list=cfg.param_receptive_field_list,\n                                     receptive_field_stride=cfg.param_receptive_field_stride,\n                                     bbox_small_list=cfg.param_bbox_small_list,\n                                     bbox_large_list=cfg.param_bbox_large_list,\n                                     receptive_field_center_start=cfg.param_receptive_field_center_start,\n                                     num_output_scales=cfg.param_num_output_scales)\n\n    if args.mode == 'image':\n        data_folder = args.data\n        file_name_list = [file_name for file_name in os.listdir(data_folder) \\\n                          if file_name.lower().endswith('jpg')]\n\n        for file_name in file_name_list:\n            im = cv2.imread(os.path.join(data_folder, file_name))\n\n            bboxes, infer_time = face_predictor.predict(im, resize_scale=1, score_threshold=0.6, top_k=10000, \\\n                                                        NMS_threshold=0.4, NMS_flag=True, skip_scale_branch_list=[])\n\n            for bbox in bboxes:\n                cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)\n\n            # if max(im.shape[:2]) > 1600:\n            #     scale = 1600/max(im.shape[:2])\n            #     im = cv2.resize(im, (0, 0), fx=scale, fy=scale)\n            cv2.imshow('im', im)\n            cv2.waitKey(5000)\n            cv2.imwrite(os.path.join(data_folder, file_name.replace('.jpg', '_result.png')), im)\n    elif args.mode == 'video':\n        # win_name = 'LFFD DEMO'\n        # cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)\n        data_folder = args.data\n        file_name_list = [file_name for file_name in os.listdir(data_folder) \\\n                          if file_name.lower().endswith('mp4')]\n        for file_name in file_name_list:\n            out_file = os.path.join(data_folder, file_name.replace('.mp4', '_v2_gpu_result.avi'))\n            cap = cv2.VideoCapture(os.path.join(data_folder, file_name))\n            vid_writer = cv2.VideoWriter(out_file, \\\n                                         cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 60, \\\n                                         (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), \\\n                                          int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))\n            while cv2.waitKey(1) < 0:\n                ret, frame = cap.read()\n                if ret:\n                    h, w, c = frame.shape\n\n                if not ret:\n                    print(\"Done processing of %s\" % file_name)\n                    print(\"Output file is stored as %s\" % out_file)\n                    cv2.waitKey(3000)\n                    break\n\n                tic = time.time()\n                bboxes, infer_time = face_predictor.predict(frame, resize_scale=1, score_threshold=0.6, top_k=10000, \\\n                                                            NMS_threshold=0.4, NMS_flag=True, skip_scale_branch_list=[])\n                toc = time.time()\n                detect_time = (toc - tic) * 1000\n\n                face_num = 0\n                for bbox in bboxes:\n                    face_num += 1\n                    cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)\n\n                computing_platform = 'Computing platform: NVIDIA GPU FP32'\n                cv2.putText(frame, computing_platform, (5, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n                input_resolution = 'Network input resolution: %sx%s' % (w, h)\n                cv2.putText(frame, input_resolution, (5, 65), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n                infer_time_info = 'Inference time: %.2f ms' % (infer_time)\n                cv2.putText(frame, infer_time_info, (5, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n                infer_speed = 'Inference speed: %.2f FPS' % (1000 / infer_time)\n                cv2.putText(frame, infer_speed, (5, 135), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n                face_num_info = 'Face num: %d' % (face_num)\n                cv2.putText(frame, face_num_info, (5, 170), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n\n                vid_writer.write(frame.astype(np.uint8))\n                # cv2.imshow(win_name, frame)\n\n                if cv2.waitKey(1) & 0xFF == ord('q'):\n                    break\n\n            cap.release()\n            cv2.destroyAllWindows()\n    else:\n        raise TypeError('Unsupported File Format.')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"YonghaoHe/LFFD-A-Light-and-Fast-Face-Detector-for-Edge-Devices","sub_path":"face_detection/demo/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":6442,"program_lang":"python","lang":"en","doc_type":"code","stars":1298,"dataset":"github-code","pt":"77"}
+{"seq_id":"73050968570","text":"from django.urls import path\nfrom . import views\n\napp_name = 'finance'\n\nurlpatterns = [\n    path('', views.seeSalary , name='see_salary'),\n    path('see_salaries_in_details/', views.seeSalaryInDetails , name='see_salaries_in_details'),\n    path('update_salary/', views.updateSalary , name='update_salary'),\n    path('create_monthly_salary/', views.createSelaryStatus , name='create_monthly_salary'),\n    path('make_employee_salary_paid/<int:id>', views.makeEmployeeSalaryPaid , name='make_employee_salary_paid'),\n    path('accounts/', views.accountsMainView , name='accounts'),\n    path('accounts_spend_money/', views.addSpendMoney , name='accounts_spend_money'),\n    path('edit_spend_entry/<int:id>', views.editSpendMoney , name='edit_spend_entry'),\n    path('edit_recived_entry/<int:id>', views.editRecivedMoney , name='edit_recived_entry'),\n    path('delete_spend_entry/<int:id>', views.deleteSpendMoney , name='delete_spend_entry'),\n    path('delete_recived_entry/<int:id>', views.deleteRecivedMoney , name='delete_recived_entry'),\n    path('entity_register/', views.registerEntity , name='entity_register'),\n    path('see_entity_to_edit/', views.seeAllEntry , name='see_entity_to_edit'),\n    path('entity_edit/<int:id>', views.editEntity , name='entity_edit'),\n    \n]","repo_name":"mahadirazib/erp_for_startup","sub_path":"finance/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19688894874","text":"from collections import OrderedDict\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom efficientnet_pytorch import EfficientNet\nfrom torchvision.models.resnet import resnet18, resnet50\nfrom tool import gen_dx_bx, cumsum_trick, QuickCumsum\n\nclass Up(nn.Module):\n    def __init__(self, in_channels, out_channels, scale_factor=2):\n        super().__init__()\n\n        self.up = nn.Upsample(scale_factor=scale_factor, mode='bilinear',\n                              align_corners=True)\n\n        self.conv = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True)\n        )\n\n    def forward(self, x1, x2):\n        x1 = self.up(x1)\n        x1 = torch.cat([x2, x1], dim=1)\n        return self.conv(x1)\n\n\nclass CamEncode(nn.Module):\n    def __init__(self, D, C, downsample):\n        super(CamEncode, self).__init__()\n        self.D = D\n        self.C = C\n\n        self.trunk = EfficientNet.from_pretrained(\"efficientnet-b0\")\n\n        self.up1 = Up(320+112, 512)\n        self.depthnet = nn.Conv2d(512, self.D + self.C, kernel_size=1, padding=0)\n\n    def get_depth_dist(self, x, eps=1e-20):\n        return x.softmax(dim=1)\n\n    def get_depth_feat(self, x):\n        x = self.get_eff_depth(x)\n        # Depth\n        x = self.depthnet(x)\n\n        depth = self.get_depth_dist(x[:, :self.D])\n        new_x = depth.unsqueeze(1) * x[:, self.D:(self.D + self.C)].unsqueeze(2)\n\n        return depth, new_x\n\n    def get_eff_depth(self, x):\n        # adapted from https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py#L231\n        endpoints = dict()\n\n        # Stem\n        x = self.trunk._swish(self.trunk._bn0(self.trunk._conv_stem(x)))\n        prev_x = x\n\n        # Blocks\n        for idx, block in enumerate(self.trunk._blocks):\n            drop_connect_rate = self.trunk._global_params.drop_connect_rate\n            if drop_connect_rate:\n                drop_connect_rate *= float(idx) / len(self.trunk._blocks) # scale drop connect_rate\n            x = block(x, drop_connect_rate=drop_connect_rate)\n            if prev_x.size(2) > x.size(2):\n                endpoints['reduction_{}'.format(len(endpoints)+1)] = prev_x\n            prev_x = x\n\n        # Head\n        endpoints['reduction_{}'.format(len(endpoints)+1)] = x\n        x = self.up1(endpoints['reduction_5'], endpoints['reduction_4'])\n        return x\n\n    def forward(self, x):\n        depth, x = self.get_depth_feat(x)\n\n        return x\n\n\nclass BevEncode(nn.Module):\n    def __init__(self, inC, outC):\n        super(BevEncode, self).__init__()\n\n        trunk = resnet18(weights=None, zero_init_residual=True)\n        self.conv1 = nn.Conv2d(inC, 64, kernel_size=7, stride=2, padding=3,\n                               bias=False)\n        self.bn1 = trunk.bn1    # batchnorm\n        self.relu = trunk.relu\n\n        self.layer1 = trunk.layer1\n        self.layer2 = trunk.layer2\n        self.layer3 = trunk.layer3\n\n        self.up1 = Up(64+256, 256, scale_factor=4)\n        self.up2 = nn.Sequential(\n            nn.Upsample(scale_factor=2.56, mode='bilinear',\n                              align_corners=True),\n            nn.Conv2d(256, 128, kernel_size=3, padding=1, bias=False),\n            nn.BatchNorm2d(128),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(128, outC, kernel_size=1, padding=0),\n        )\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x1 = self.layer1(x) # x1: torch.Size([4, 64, 100, 100])\n        x = self.layer2(x1) \n        x = self.layer3(x) # x: torch.Size([4, 256, 25, 25])\n\n        x = self.up1(x, x1) # x up1: torch.Size([4, 256, 100, 100])\n        x = self.up2(x) # x up2: torch.Size([4, 4, 200, 200]) (if outC = 4)\n        return x\n\n    def features(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x1 = self.layer1(x) # x1: torch.Size([4, 64, 100, 100])\n        x = self.layer2(x1) \n        x = self.layer3(x) # x: torch.Size([4, 256, 25, 25])\n\n        # x = self.up1(x, x1) # x up1: torch.Size([4, 256, 100, 100])\n\n        return x1, x\n\nclass IntermediateLayerGetter(nn.ModuleDict):\n    def __init__(self, model, return_layers, hrnet_flag=False):\n        if not set(return_layers).issubset([name for name, _ in model.named_children()]):\n            print([(name , _) for name, _ in model.named_children()])\n            raise ValueError(\"return_layers are not present in model\")\n        \n        self.hrnet_flag = hrnet_flag\n        \n        orig_return_layers = return_layers\n        return_layers = {k: v for k, v in return_layers.items()}\n        layers = OrderedDict()\n        for name, module in model.named_children():\n            layers[name] = module\n            if name in return_layers:\n                del return_layers[name]\n            if not return_layers:\n                break\n        super(IntermediateLayerGetter, self).__init__(layers)\n        self.return_layers = orig_return_layers\n    \n    def forward(self, x):\n        out = OrderedDict()\n        for name, module in self.named_children():\n            if self.hrnet_flag and name.startswith('transition'):\n                if name == 'transition1':\n                    x = [trans(x) for trans in module]\n                else:\n                    x.append(module(x[-1]))\n            else:\n                x = module(x)\n            \n            if name in self.return_layers:\n                out_name = self.return_layers[name]\n                if name == 'stage4' and self.hrnet_flag:\n                    output_h, output_w = x[0].size(2), x[0].size(3)\n                    x1 = F.interpolate(x[1], size=(output_h, output_w), mode='bilinear', align_corners=False)\n                    x2 = F.interpolate(x[2], size=(output_h, output_w), mode='bilinear', align_corners=False)\n                    x3 = F.interpolate(x[3], size=(output_h, output_w), mode='bilinear', align_corners=False)\n                    x = torch.cat([x[0], x1, x2, x3], dim=1)\n                    out[out_name] = x\n                else:\n                    out[out_name] = x\n                    \n        return out\n    \nclass ASPPConv(nn.Sequential):\n    def __init__(self, in_channels, out_channels, dilation):\n        modules = [\n            nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True)\n        ]\n        super(ASPPConv, self).__init__(*modules)\n        \nclass ASPPPooling(nn.Sequential):\n    def __init__(self, in_channels, out_channels):\n        super(ASPPPooling, self).__init__(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True)\n        )\n        \n    def forward(self, x):\n        size = x.shape[-2:]\n        x = super(ASPPPooling, self).forward(x)\n        return F.interpolate(x, size=size, mode='bilinear', align_corners=False)\n\nclass ASPP(nn.Module):\n    def __init__(self, in_channels, atrous_rates):\n        super(ASPP, self).__init__()\n        out_channels = 256\n        modules = []\n        modules.append(nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True)\n        ))\n        \n        rate1, rate2, rate3 = tuple(atrous_rates)\n        modules.append(ASPPConv(in_channels, out_channels, rate1))\n        modules.append(ASPPConv(in_channels, out_channels, rate2))\n        modules.append(ASPPConv(in_channels, out_channels, rate3))\n        modules.append(ASPPPooling(in_channels, out_channels))\n        \n        self.convs = nn.ModuleList(modules)\n        \n        self.project = nn.Sequential(\n            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.1)\n        )\n        \n    def forward(self, x):\n        res = []\n        for conv in self.convs:\n            res.append(conv(x))\n        res = torch.cat(res, dim=1)\n        return self.project(res)\n    \nclass Deeplabv3Plus(nn.Module):\n    def __init__(self, in_channels, low_level_channels, outC, aspp_dilate):\n        super(Deeplabv3Plus, self).__init__()\n        self.project = nn.Sequential(\n            nn.Conv2d(low_level_channels, 48, 1, bias=False),\n            nn.BatchNorm2d(48),\n            nn.ReLU(inplace=True)\n        )\n        \n        self.aspp = ASPP(in_channels, aspp_dilate)\n        \n        self.classifier = nn.Sequential(\n            nn.Conv2d(304, 256, 3, padding=1, bias=False),\n            nn.BatchNorm2d(256),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(256, outC, 1)\n        )\n        self._init_weight()\n        \n    def forward(self, feature):\n        low_level_feature = self.project(feature['low_level'])\n        output_feature = self.aspp(feature['out'])\n        output_feature = F.interpolate(output_feature, size=low_level_feature.shape[2:], mode='bilinear', align_corners=False)\n        out = self.classifier(torch.cat([low_level_feature, output_feature], dim=1))\n        return out\n    \n    def _init_weight(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight)\n            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n                \nclass Deeplabv3PlusEncode(nn.Module):\n    def __init__(self, inC, outC):\n        super(Deeplabv3PlusEncode, self).__init__()\n        \n        self.aspp_dilate = [12, 24, 36]\n        \n        trunk = resnet50(weights=None, zero_init_residual=True, replace_stride_with_dilation=[False, True, True])\n        self.trunk = nn.Sequential(OrderedDict([\n            ('conv1', nn.Conv2d(inC, 64, kernel_size=7, stride=2, padding=3, bias=False)),\n            ('bn1', trunk.bn1), \n            ('relu', trunk.relu),\n            ('maxpool', trunk.maxpool),\n            ('layer1', trunk.layer1),\n            ('layer2', trunk.layer2),\n            ('layer3', trunk.layer3),\n            ('layer4', trunk.layer4)           \n        ]))\n        self.return_layers = {'layer4': 'out', 'layer1': 'low_level'}\n        self.inplanes = 2048\n        self.low_level_planes = 256\n        self.backbone = IntermediateLayerGetter(self.trunk, self.return_layers)    \n        self.classifier = Deeplabv3Plus(self.inplanes, self.low_level_planes, outC, self.aspp_dilate)\n\n    def forward(self, x):\n        feature = self.backbone(x)\n        x = self.classifier(feature)\n        x = F.interpolate(x, scale_factor=5.12, mode='bilinear', align_corners=False)\n        return x\n        \nclass LiftSplatShoot(nn.Module):\n    def __init__(self, grid_conf, data_aug_conf, outC, is_deeplab=False):\n        super(LiftSplatShoot, self).__init__()\n        self.grid_conf = grid_conf\n        self.data_aug_conf = data_aug_conf\n\n        dx, bx, nx = gen_dx_bx(self.grid_conf['xbound'],\n                                              self.grid_conf['ybound'],\n                                              self.grid_conf['zbound'],\n                                              )\n        self.dx = nn.Parameter(dx, requires_grad=False)\n        self.bx = nn.Parameter(bx, requires_grad=False)\n        self.nx = nn.Parameter(nx, requires_grad=False)\n\n        self.downsample = 16\n        self.camC = 64\n        self.frustum = self.create_frustum()\n        self.D, _, _, _ = self.frustum.shape   # torch.Size([41, 8, 22, 3])\n        self.camencode = CamEncode(self.D, self.camC, self.downsample)\n        if is_deeplab:\n            self.bevencode = Deeplabv3PlusEncode(inC=self.camC, outC=outC)\n        else:\n            self.bevencode = BevEncode(inC=self.camC, outC=outC)\n\n        # toggle using QuickCumsum vs. autograd\n        self.use_quickcumsum = True\n\n    def create_frustum(self):\n        # make grid in image plane\n        ogfH, ogfW = self.data_aug_conf['final_dim']\n        fH, fW = ogfH // self.downsample, ogfW // self.downsample\n        ds = torch.arange(*self.grid_conf['dbound'], dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW)\n        D, _, _ = ds.shape\n        xs = torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(1, 1, fW).expand(D, fH, fW)\n        ys = torch.linspace(0, ogfH - 1, fH, dtype=torch.float).view(1, fH, 1).expand(D, fH, fW)\n\n        # D x H x W x 3\n        frustum = torch.stack((xs, ys, ds), -1)\n        return nn.Parameter(frustum, requires_grad=False)\n\n    def get_geometry(self, rots, trans, intrins, post_rots, post_trans):\n        \"\"\"Determine the (x,y,z) locations (in the ego frame)\n        of the points in the point cloud.\n        Returns B x N x D x H/downsample x W/downsample x 3\n        \"\"\"\n        B, N, _ = trans.shape\n\n        # undo post-transformation\n        # B x N x D x H x W x 3\n        points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)\n        points = torch.inverse(post_rots).view(B, N, 1, 1, 1, 3, 3).matmul(points.unsqueeze(-1))\n\n        # cam_to_ego\n        points = torch.cat((points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3],\n                            points[:, :, :, :, :, 2:3]\n                            ), 5)\n        combine = rots.matmul(torch.inverse(intrins))\n        points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)\n        points += trans.view(B, N, 1, 1, 1, 3)\n\n        return points\n\n    def get_cam_feats(self, x):\n        \"\"\"Return B x N x D x H/downsample x W/downsample x C\n        \"\"\"\n        B, N, C, imH, imW = x.shape\n\n        x = x.view(B*N, C, imH, imW)\n        x = self.camencode(x)\n        x = x.view(B, N, self.camC, self.D, imH//self.downsample, imW//self.downsample)\n        x = x.permute(0, 1, 3, 4, 5, 2)\n\n        return x\n\n    def voxel_pooling(self, geom_feats, x):\n        B, N, D, H, W, C = x.shape\n        Nprime = B*N*D*H*W\n\n        # flatten x\n        x = x.reshape(Nprime, C)\n\n        # flatten indices\n        geom_feats = ((geom_feats - (self.bx - self.dx/2.)) / self.dx).long()\n        geom_feats = geom_feats.view(Nprime, 3)\n        batch_ix = torch.cat([torch.full([Nprime//B, 1], ix,\n                             device=x.device, dtype=torch.long) for ix in range(B)])\n        geom_feats = torch.cat((geom_feats, batch_ix), 1)\n\n        # filter out points that are outside box\n        kept = (geom_feats[:, 0] >= 0) & (geom_feats[:, 0] < self.nx[0])\\\n            & (geom_feats[:, 1] >= 0) & (geom_feats[:, 1] < self.nx[1])\\\n            & (geom_feats[:, 2] >= 0) & (geom_feats[:, 2] < self.nx[2])\n        x = x[kept]\n        geom_feats = geom_feats[kept]\n\n        # get tensors from the same voxel next to each other\n        ranks = geom_feats[:, 0] * (self.nx[1] * self.nx[2] * B)\\\n            + geom_feats[:, 1] * (self.nx[2] * B)\\\n            + geom_feats[:, 2] * B\\\n            + geom_feats[:, 3]\n        sorts = ranks.argsort()\n        x, geom_feats, ranks = x[sorts], geom_feats[sorts], ranks[sorts]\n\n        # cumsum trick\n        if not self.use_quickcumsum:\n            x, geom_feats = cumsum_trick(x, geom_feats, ranks)\n        else:\n            x, geom_feats = QuickCumsum.apply(x, geom_feats, ranks)\n\n        # griddify (B x C x Z x X x Y)\n        final = torch.zeros((B, C, self.nx[2], self.nx[0], self.nx[1]), device=x.device)\n        final[geom_feats[:, 3], :, geom_feats[:, 2], geom_feats[:, 0], geom_feats[:, 1]] = x\n\n        # collapse Z\n        final = torch.cat(final.unbind(dim=2), 1)\n\n        return final\n\n    def get_voxels(self, x, rots, trans, intrins, post_rots, post_trans):\n        geom = self.get_geometry(rots, trans, intrins, post_rots, post_trans) # torch.Size([4, 5, 41, 8, 22, 3])\n        x = self.get_cam_feats(x)   # torch.Size([4, 5, 41, 8, 22, 64])\n\n        x = self.voxel_pooling(geom, x) # torch.Size([4, 64, 200, 200])\n\n        return x\n\n    def forward(self, imgs, rots, trans, intrins, post_rots, post_trans):\n        # BEV segmentation\n        imgs = self.get_voxels(imgs, rots, trans, intrins, post_rots, post_trans)\n        segs = self.bevencode(imgs)\n\n        return segs\n    def features(self, imgs, rots, trans, intrins, post_rots, post_trans):\n                # BEV segmentation\n        imgs = self.get_voxels(imgs, rots, trans, intrins, post_rots, post_trans)\n        x1, x = self.bevencode.features(imgs)\n\n        return x1, x\n\ndef compile_model(grid_conf, data_aug_conf, outC, is_deeplab=False):\n    return LiftSplatShoot(grid_conf, data_aug_conf, outC, is_deeplab)\n\n\n","repo_name":"HCIS-Lab/Interaction-benchmark","sub_path":"datasets/model_lss.py","file_name":"model_lss.py","file_ext":"py","file_size_in_byte":16795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72380676090","text":"from bs4 import BeautifulSoup\nimport requests\n\n\ndef get_content(url):\n    html = requests.get(url).content\n    soup = BeautifulSoup(html, 'html.parser')\n    return soup\n\n\ndef get_texts():\n    links = get_content('https://www.grimmstories.com/pt/grimm_contos/list')\n    links = links.find('ul', class_='bluelink').find_all('a')\n    all_texts = []\n    count = 0\n    total = len(links)\n    for link in links:\n        count+=1\n        print(count, ' de ', total)\n        page_text = get_content(link['href'])\n        page_text = page_text.find('div', class_='text').text.replace('\\n\\n', '')\n        all_texts.append(page_text)   \n        print(f\"Added link: {link['href']}\")\n    return all_texts\n\n\ndef export_content(list):        \n    with open('input.txt', 'w') as f:\n        f.write(\"\\n\\n\\n\".join(list))\n    \n\ndef main():\n    listTexts = get_texts()\n    export_content(listTexts)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"andersoncordeiro/contos-rnn","sub_path":"crawlers/crawler_contos2.py","file_name":"crawler_contos2.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72358213688","text":"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndf = pd.read_csv(\"C:/Users/SANTHOSH KUMAR/Documents/machine learning/csv files/LSM1.csv\")\nprint(df)\n\n# x = df.iloc[:,0].values\n# opp = x.shape\n# print(opp)\nx = df.iloc[:,0].values.reshape(5,1)\nopp = x.shape\nprint(opp)\n\nprint(x)\n\ny = df.iloc[:,1].values\n\nprint(y)\n\n# from sklearn.linear_model import LinearRegression\n# lin = LinearRegression()\n# print(lin)\n\n# from sklearn import linear_model\n# reg = linear_model.LinearRegression()\n#1\nfrom sklearn import linear_model\nreg = linear_model.LinearRegression()\noppp = reg.fit(x,y)    #train the model\nlin = reg.predict([[2.5]])   #testing the model \nprint(oppp)\nprint(lin)\nram = reg.coef_  #for checking the coefficient of x\nprint(ram)\nsan = reg.intercept_  #for print the intercept of c     #equqtion is y = mx+c\nprint(san)\n\n\n# plt.scatter(x,y)\n# ren = plt.show()\n# print(ren)\n\npred_y = reg.predict(x)\n# print(pred_y)\nplt.scatter(x,y)\nplt.plot(x,pred_y,color = 'g',marker = \"*\",markersize = 10)\nrav = plt.show()\nprint(rav)\n\n\n#2\n\n\n# from sklearn.linear_model import LinearRegression\n# lin = LinearRegression()\n# lin.fit(x,y)\n# lin.predict([[2.5]])\n# print(lin)\n\n\n\n#example for linearregression with comment for understand perpose\n\n# import matplotlib.pyplot as plt\n# import numpy as np\n# from sklearn import datasets, linear_model\n# from sklearn.metrics import mean_squared_error, r2_score\n\n# # Load the diabetes dataset\n# diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n\n# # Use only one feature\n# diabetes_X = diabetes_X[:, np.newaxis, 2]\n\n# # Split the data into training/testing sets\n# diabetes_X_train = diabetes_X[:-20]\n# diabetes_X_test = diabetes_X[-20:]\n\n# # Split the targets into training/testing sets\n# diabetes_y_train = diabetes_y[:-20]\n# diabetes_y_test = diabetes_y[-20:]\n\n# # Create linear regression object\n# regr = linear_model.LinearRegression()\n\n# # Train the model using the training sets\n# regr.fit(diabetes_X_train, diabetes_y_train)\n\n# # Make predictions using the testing set\n# diabetes_y_pred = regr.predict(diabetes_X_test)\n\n# # The coefficients\n# print('Coefficients: \\n', regr.coef_)\n# # The mean squared error\n# print('Mean squared error: %.2f'\n#       % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# # The coefficient of determination: 1 is perfect prediction\n# print('Coefficient of determination: %.2f'\n#       % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# # Plot outputs\n# plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n# plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\n# plt.xticks(())\n# plt.yticks(())\n\n# plt.show()\n","repo_name":"santhosh-karamthot/machine_learnig_basic","sub_path":"csv_files/lms1.py","file_name":"lms1.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8090435277","text":"from flask.views import MethodView\nfrom flask_smorest import abort, Blueprint\nfrom sqlalchemy.exc import SQLAlchemyError\n\nfrom models import AddressModel, CountryModel\nfrom schemas.response_schema import responseSchema, BaseResponseSchema\nfrom schemas.address_schema import PlainAddressSchema, AddressSchema\n\nblp = Blueprint(\"Addresses\", __name__, description=\"Operations on Addresses.\")\n\nINVALID_COUNTRY_ID = \"Invalid Country ID\"\nINVALID_ADDRESS_ID = \"Invalid Address ID\"\n\nSELECT_ERROR = \"An error occurred while fetching address.\"\nINSERT_ERROR = \"An error occurred while creating address.\"\nUPDATE_ERROR = \"An error occurred while updating address.\"\nDELETE_ERROR = \"An error occurred while deleting address.\"\n\nDELETE_SUCCESS = \"Address deleted successfully.\"\n\n@blp.route('/address')\nclass AddressList(MethodView):\n    @blp.response(200, responseSchema(PlainAddressSchema, many=True))\n    @blp.alt_response(500, example={\"code\": 500, \"message\": SELECT_ERROR, \"status\": \"Internal Server Error\"})\n    def get(self):\n        \"\"\"Get List of Addresses with associated Country\"\"\"\n        #TODO: remove list of users also return in the response\n        try:\n            # Equivalent to AddressModel.query.all() \n            addresses = AddressModel.find_all()\n        except SQLAlchemyError:\n            abort(500, message=SELECT_ERROR)\n        else:\n            res = {\n                \"code\": 200,\n                \"status\": \"OK\",\n                \"message\": \"Query was successful\",\n                \"data\": addresses\n            }\n            return res\n\n    @blp.arguments(AddressSchema)\n    @blp.response(201, responseSchema(PlainAddressSchema))\n    @blp.alt_response(400, example={\"code\": 400, \"message\": INVALID_COUNTRY_ID, \"status\": \"Bad Request\"})\n    @blp.alt_response(500, example={\"code\": 500, \"message\": INSERT_ERROR, \"status\": \"Internal Server Error\"})\n    def post(self, address_data):\n        \"\"\"Add new Address into the database\"\"\"\n        country = CountryModel.query.filter_by(id=address_data['country_id']).first()\n        if country is None:\n            abort(400, message=INVALID_COUNTRY_ID.format(address_data['country_id']))\n        \n        address = AddressModel(**address_data)\n        try:\n            address.save_to_db()\n        except SQLAlchemyError:\n            abort(500, message=INSERT_ERROR)\n        else:\n            res = {\n                \"code\": 201,\n                \"status\": \"Created\",\n                \"message\": \"New addresss added successfully\",\n                \"data\": address,\n            }\n            return res\n\n@blp.route('/address/<int:address_id>')\nclass AddressItem(MethodView):\n    @blp.response(200, responseSchema(AddressSchema))\n    @blp.alt_response(404, example={\"code\": 404, \"message\": INVALID_ADDRESS_ID, \"status\": \"Not Found\"})\n    @blp.alt_response(500, example={\"code\": 500, \"message\": SELECT_ERROR, \"status\": \"Internal Server Error\"})\n    def get(self, address_id):\n        \"\"\"Get Address information based on address_id\"\"\"\n        try:\n            address = AddressModel.find_by_id(id=address_id)\n        except SQLAlchemyError:\n            abort(500, message=SELECT_ERROR)\n        else:\n            res = {\n                \"code\": 200,\n                \"status\": \"OK\",\n                \"message\": \"Query was successful\",\n                \"data\": address\n            }\n            return res\n\n    @blp.arguments(AddressSchema)\n    @blp.response(200, responseSchema(AddressSchema))\n    @blp.alt_response(400, example={\"code\": 400, \"message\": INVALID_COUNTRY_ID, \"status\": \"Bad Request\"})\n    @blp.alt_response(404, example={\"code\": 404, \"message\": INVALID_ADDRESS_ID, \"status\": \"Not Found\"})\n    @blp.alt_response(500, example={\"code\": 500, \"message\": UPDATE_ERROR, \"status\": \"Internal Server Error\"})\n    def put(self, address_data, address_id):\n        \"\"\"Update Address information based on address_id\"\"\"\n        country = CountryModel.query.filter_by(id=address_data['country_id']).first()\n        if country is None:\n            abort(400, message=INVALID_COUNTRY_ID)\n\n        address = AddressModel.find_by_id(id=address_id)\n        address.address_line1 = address_data['address_line1']\n        address.address_line2 = address_data['address_line2']\n        address.city = address_data['city']\n        address.country_id = address_data['country_id']\n        address.postal_code = address_data['postal_code']\n        address.region = address_data['region']\n        address.street_number = address_data['street_number']\n        try:\n            address.save_to_db()\n        except SQLAlchemyError:\n            abort(500, message=UPDATE_ERROR)\n        else:\n            res = {\n                \"code\": 200,\n                \"status\": \"OK\",\n                \"message\": \"Update successful on address_id = {addr_id}.\".format(addr_id=address.id),\n                \"data\": address\n            }\n            return res\n\n    @blp.response(200, responseSchema(PlainAddressSchema))\n    @blp.alt_response(404, example={\"code\": 404, \"message\": INVALID_ADDRESS_ID, \"status\": \"Not Found\"})\n    @blp.alt_response(500, example={\"code\": 500, \"message\": DELETE_ERROR, \"status\": \"Internal Server Error\"})\n    def delete(self, address_id):\n        \"\"\"Delete Address Record from database based on address_id\"\"\"\n        address = AddressModel.find_by_id(id=address_id)\n        try:\n            address.delete_from_db()\n        except SQLAlchemyError:\n            abort(500, message=DELETE_ERROR)\n        else:\n            res = {\n                \"code\": 200,\n                \"status\": \"OK\",\n                \"message\": \"Address Deleted Successfully.\",\n                \"data\": address,\n            }\n            return res","repo_name":"Socheapheaktra/ecommerce_app","sub_path":"resources/address.py","file_name":"address.py","file_ext":"py","file_size_in_byte":5650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30940564888","text":"from Infections.Agent import Category\nfrom scipy.stats import nbinom, poisson, lognorm\nimport math\n\n# ------------------------- overall parameters -------------------------------------\n\n# plot graphics at the end\nplotting = True\n# output single run statistics\noutput_singleruns = True\n# output summary statistics\noutput_summary = True\n\n# times to run simulation\nNsim = 10\n# days simulation\nT = 31*3\n# T = 15\n\n# each person names the persons it interact with the most\n# at the moment say all\n# op 7\n# meeting 5\n# station some\n# nursing staff 6\n# meds 10\n# overall contact estimate >= 30 but < 40\n# contacts_perday = 30\n# -- look only at close contacts at the moment\n# close risk contacts / day and person [pers comm, Barbara]\ncontacts_perday = 4\n\n# -- hospital --\n# N = 1300\n# beds = 1050 # (at moment half used)\n\n# -- station --\n#N = 20 + 40  # 20 meds, 40 nursing staff\n# beds = 176 # (at moment half used)\n\nnb_agents = {\n    Category.PATIENT:29,\n    Category.NURSE: 27,\n    Category.DOCTOR: 11,\n    Category.ADMIN: 8,\n}\n\n# --------- smaller tests ----------\n\n# --- enable for N = 2\n\n\"\"\"\nnb_agents = {\n    Category.PATIENT: 0,\n    Category.NURSE: 0,\n    Category.DOCTOR: 2,\n    Category.ADMIN: 0,\n}\n\"\"\"\n\n# --- enable for N = 4\n\n\"\"\"\nnb_agents = {\n    Category.PATIENT: 0,\n    Category.NURSE: 2,\n    Category.DOCTOR: 2,\n    Category.ADMIN: 0,\n}\n\"\"\"\n\n# --- enable for N = 10\n\n\nnb_agents = {\n   Category.PATIENT: 5,\n   Category.NURSE: 2,\n   Category.DOCTOR: 2,\n   Category.ADMIN: 1,\n}\n\n\n\n# --------- end tests ------------\n\n\n\nN = sum(nb_agents.values())\n\n# P that someone is infected from outside (includes infection by patients)\n# per day\npext = 0.01 * 0.05  # ?\n\n# P that someone is infected by an infected contact\n# per contact and day\npint = 0.002  # ?\n\n# source: How best to use limited tests? Improving COVID-19 surveillance in long-term care (doi:10.1101/2020.04.19.20071639)\npinf_rate_permin = 0.0014\npinf_max_duration = 60\n# P of internal infection, given the contact duration\ndef pinf(duration_min):\n    return 1 - math.exp(-pinf_rate_permin*min(duration_min, pinf_max_duration))\n\n\n# durration in min from where on contacts\n# are considered as such (e.g. in contact tracing)\nDURATION_TH = 15\n\n# choose test:\nmytests = {}\nmytests[0] = {\n    'type': 'TestAll',\n    'parameters': {}\n}\nmytests[1] = {\n    'type': 'TestJustLook',\n    'parameters': {}\n}\nmytests[2] = {\n    'type': 'TestPool',\n    'parameters': {\n            'individual_quarantine_days': 2*7,  # quarantine for days if individual pos\n            #\n            'group_quarantine_days': 2,        # quarantine group for days if group pos\n            'poolsize': 5,                     # uniform poolsize\n            'k_group': 1,                      # k for group test\n            #\n            'days_if_positive': 7,             # if positive again, schedule next test in days\n            'k_release': 2,                    # k for release test\n        'days_after_negative': 2}          # days after negative test still in quarantine\n}\nmytests[3] = {\n    'type': 'TestContacts',\n    'parameters': {\n        'individual_quarantine_days': 2*7,\n        # contact of positives are quarantined for 14 days AND no symptoms\n        'contact_quarantine_days': 2*7,\n        'lookback_days': 2,                 # 2 days before symptom onset\n        #\n        'contacts_check': False,            # don't check these right away\n        'contacts_poolsize': 5,             #\n            'contacts_k_group': 1,              #\n            #\n        'working_check': False,             # don't check all others-> ignore next\n        'working_quarantine_days': 2*7,\n        'working_poolsize': 5,              #\n            'working_k_group': 1,               #\n            #\n        # if positive in quarantine, retest in 7 days (check!)\n        'days_if_positive': 7,\n        'k_release': 2,                     # 2 negative tests required before release\n        'days_after_negative': 2}           # after tests are negative, wait another 2 days (check!)\n}\n\n# chose among above or new ones\nusetest = mytests[3]\n\n# -------------------------------------------------------------------------------\n\n# number staff (dont change)\nperson_ids = list(range(N))\n\n# kernel distributions\nkerneldist = {\n    Category.DOCTOR: {\n        Category.DOCTOR: lambda: nbinom.rvs(**{'n': 0.639455389646031, 'p': 0.33441115478175726}),\n        Category.ADMIN: lambda: nbinom.rvs(**{'n': 10.000001921571586, 'p': 0.990990726325269}),\n        Category.NURSE: lambda: nbinom.rvs(**{'n': 10.0, 'p': 1.0}),\n        Category.PATIENT: lambda: nbinom.rvs(**{'n': 10.0, 'p': 1.0}),\n    },\n    Category.NURSE: {\n        Category.DOCTOR: lambda: nbinom.rvs(**{'n': 10.000004070316532, 'p': 1.0}),\n        Category.ADMIN: lambda: nbinom.rvs(**{'n': 0.15085122247570856, 'p': 0.44891340197014196}),\n        Category.NURSE: lambda: nbinom.rvs(**{'n': 0.4451399065297341, 'p': 0.30032845382338796}),\n        Category.PATIENT: lambda: nbinom.rvs(**{'n': 0.11391011540643767, 'p': 0.2352151512186682}),\n    },\n}\n\ncontactsdist = {\n    Category.DOCTOR: {\n        Category.DOCTOR: lambda: nbinom.rvs(**{'n': 10.979419508176825, 'p': 0.7434157920988455}),\n        Category.ADMIN: lambda: nbinom.rvs(**{'n': 198.4879963236352, 'p': 0.9939559641105772}),\n        Category.NURSE: lambda: poisson.rvs(**{'mu': 2.002999988953658}),\n        Category.PATIENT: lambda: nbinom.rvs(**{'n': 134.7317660804801, 'p': 0.987906385925085}),\n    },\n    Category.NURSE: {\n        Category.DOCTOR: lambda: nbinom.rvs(**{'n': 143.0480506968438, 'p': 0.9896228228165873}),\n        Category.ADMIN: lambda: nbinom.rvs(**{'n': 174.4605547357785, 'p': 0.9920733339719905}),\n        Category.NURSE: lambda: nbinom.rvs(**{'n': 3.551790950529168, 'p': 0.45740247406029183}),\n        Category.PATIENT: lambda: nbinom.rvs(**{'n': 5.456145786428396, 'p': 0.6579607647746138}),\n    }\n}\n\ncontact_length_dist = lambda: lognorm.rvs(*(1.4171168017880222, 4.812060883472516, 4.940292742938027))","repo_name":"Butanium/simpool","sub_path":"station_simulation/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"7131557928","text":"import json\nimport yaml\n\nfrom knotify.main import get_results, argument_parser\nfrom knotify import scoring\n\n\ndef main():\n    parser = argument_parser()\n    parser.add_argument(\"--cases\", required=True)\n    parser.add_argument(\"--only\", type=int, nargs=\"*\")\n    parser.add_argument(\"--correct-stems-slack\", type=int, default=0)\n    args = parser.parse_args()\n\n    with open(args.cases, \"r\") as fin:\n        cases = yaml.safe_load(fin.read())\n\n    only = args.only or list(range(0, len(cases)))\n\n    out = {\n        \"results\": [],\n        \"totals\": {\n            \"new_correct\": 0,\n            \"correct\": 0,\n            \"same_prediction\": 0,\n            \"correct_core_stems\": 0,\n            \"count\": len(only),\n        },\n    }\n\n    for idx in only:\n        case = cases[idx]\n        results = get_results(\n            case[\"case\"].lower(),\n            grammar=args.grammar,\n            save_csv=args.csv,\n            allow_ug=args.allow_ug,\n            max_loop_size=args.max_loop_size,\n            max_stem_allow_smaller=args.max_stem_allow_smaller,\n            prune_early=args.prune_early,\n        )\n        predictions = results[[\"dot_bracket\", \"energy\", \"stems\"]].to_dict(\n            orient=\"records\"\n        )\n        dot_bracket = predictions[0][\"dot_bracket\"]\n\n        new_correct = case[\"truth\"] == dot_bracket\n        correct = case[\"pred\"] == case[\"truth\"]\n        same_prediction = case[\"pred\"] == dot_bracket\n\n        correct_core_stems = scoring.get_correct_core_stems(\n            case[\"truth\"],\n            dot_bracket,\n            slack=args.correct_stems_slack,\n        )\n\n        out[\"totals\"][\"new_correct\"] += new_correct\n        out[\"totals\"][\"correct\"] += correct\n        out[\"totals\"][\"same_prediction\"] += same_prediction\n        out[\"totals\"][\"correct_core_stems\"] += correct_core_stems == 2\n\n        out[\"results\"].append(\n            {\n                # case data\n                **case,\n                \"new_pred\": dot_bracket,\n                # checks\n                \"new_correct\": new_correct,\n                \"correct\": correct,\n                \"same_prediction\": same_prediction,\n                # results\n                \"results\": predictions,\n                # scores\n                \"correct_core_stems\": correct_core_stems,\n                \"confusion_matrix\": \", \".join(\n                    str(x)\n                    for x in scoring.get_confusion_matrix(\n                        case[\"truth\"],\n                        dot_bracket,\n                        slack=0,\n                    )\n                ),\n            }\n        )\n\n    print(json.dumps(out, indent=2))\n","repo_name":"ntua-dslab/rna-pseudoknot-detection","sub_path":"knotify/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13511309761","text":"#!/usr/bin/env python\nimport os\nimport sys\nimport subprocess\nimport time\nimport copy\nimport re\nimport json\nfrom collections import OrderedDict\nfrom BiochemPy import Reactions, Compounds\n\narguments = list(sys.argv)\n#Pop filename\narguments = arguments[1:]\nif(len(arguments) != 1 or os.path.isfile(arguments[0]) is False):\n    print(\"Error: script must be initiated with the path to the edited json file from Print_Compound_to_Disambiguate.py\")\n    sys.exit()\n\nFile=arguments[0]\nDry_Run=0\n\n##########################################################\n#\n# Load disambiguation details\n#\n##########################################################\n\nDisambiguated_Compound=None\nwith open(File) as jf:\n    Disambiguated_Compound = json.load(jf)\nOld_Cpd_ID = Disambiguated_Compound['from']['id']\n\n#Requires choice of one compound, whether new or existing\nif(len(Disambiguated_Compound['to'])>1):\n    print(\"Error: must pick one exisiting compound from list:\")\n    for cpd in Disambiguated_Compound['to']:\n        print(\"\\t\",cpd)\n    sys.exit()\n\ncompounds_helper = Compounds()\ncompounds_dict = compounds_helper.loadCompounds()\nAliases_Dict = compounds_helper.loadMSAliases()\nNames_Dict = compounds_helper.loadNames()\nStructures_Dict = compounds_helper.loadStructures([\"InChI\",\"SMILE\"],[\"KEGG\",\"MetaCyc\"])\n\n##########################################################\n#\n# Create new compound object\n# If the newly disambiguated compound is new to the biochemistry\n# Else we create a whole new compound\n#\n##########################################################\n\nNew_Cpd=None\nif(len(Disambiguated_Compound['to'])==0 or Disambiguated_Compound['to'][0]['id'] is None):\n\n    #Create New Compound!\n    #Find last identifier and increment\n    last_identifier = list(sorted(compounds_dict))[-1]\n    next_identifier = 'cpd'+str(int(re.sub('^cpd','',last_identifier))+1)\n\n    New_Cpd = OrderedDict({ \"id\":next_identifier,\"name\":\"null\",\"abbreviation\":\"null\",\"aliases\":\"null\",\n                            \"formula\":\"null\",\"mass\":\"10000000\",\"charge\":\"0\",\n                            \"deltag\":\"10000000\",\"deltagerr\":\"10000000\",\"pka\":\"\",\"pkb\":\"\",\n                            \"inchikey\":\"\",\"smiles\":\"\",\n                            \"is_cofactor\":0,\"is_core\":0,\"is_obsolete\":0,\n                            \"abstract_compound\":\"null\",\"comprised_of\":\"null\",\"linked_compound\":\"null\",\n                            \"source\":\"\",\"ontology\":\"class:null|context:null\"})\n\n    New_Cpd['formula']=Disambiguated_Compound['to'][0]['formula']\n    New_Cpd['charge']=Disambiguated_Compound['to'][0]['charge']\n    New_Cpd['mass']=Disambiguated_Compound['to'][0]['mass']\n\nelse:\n\n    #Merge with Exisiting Compound!\n    New_Cpd = compounds_dict[Disambiguated_Compound['to'][0]['id']]\n\ncompounds_dict[New_Cpd['id']]=New_Cpd\n\n##########################################################\n#\n# Handle names and aliases\n#\n##########################################################\n\n#Names\nOld_Names=list()\nNew_Names=list()\nnames_dict = Disambiguated_Compound['from']['names']\nfor name in sorted(names_dict):\n    if(names_dict[name] is True):\n        Old_Names.append(name)\n    else:\n        if(New_Cpd['name'] == \"null\"):\n            New_Cpd['name']=name\n            New_Cpd['abbreviation']=name\n\n        New_Names.append(name)\n\nNames_Dict[Old_Cpd_ID]=Old_Names\nif(compounds_dict[Old_Cpd_ID]['name'] not in Old_Names):\n    compounds_dict[Old_Cpd_ID]['name'] = sorted(Old_Names)[0]\n    compounds_dict[Old_Cpd_ID]['abbreviation'] = sorted(Old_Names)[0]\n\n#Retain names if they exist\nif New_Cpd['id'] in Names_Dict:\n    for name in Names_Dict[New_Cpd['id']]:\n        #Check if they haven't already been retained\n        if(name in Old_Names or name in New_Names):\n            continue\n\n        New_Names.append(name)\n\n#Aliases\n#Structures don't need to changing, as they can be re-listed once aliases are updated\naliases_dict = Disambiguated_Compound['from']['aliases']\nOld_Aliases=dict()\nNew_Aliases=dict()\nfor alias in aliases_dict:\n    for source in aliases_dict[alias]:\n        if(aliases_dict[alias][source] is True):\n            if(source not in Old_Aliases):\n                Old_Aliases[source]=list()\n            Old_Aliases[source].append(alias)\n        else:\n            if(source not in New_Aliases):\n                New_Aliases[source]=list()\n\n            if(New_Cpd['name']==\"null\"):\n                New_Cpd['name']=alias\n                New_Cpd['abbreviation']=alias\n\n            New_Aliases[source].append(alias)\n\n#Retain aliases if they exist\nif New_Cpd['id'] in Aliases_Dict:\n    for source in Aliases_Dict[New_Cpd['id']]:\n        for alias in Aliases_Dict[New_Cpd['id']][source]:\n            #Check if they haven't already been retained\n            if(source in Old_Aliases and alias in Old_Aliases[source]):\n                continue\n            if(source in New_Aliases and alias in New_Aliases[source]):\n                continue\n\n            #Warn if source still exists\n            if(source in Old_Aliases):\n                print(\"Warning: Alias \"+alias+\" for \"+source+\" is unresolved\")\n\n            if(source not in New_Aliases):\n                New_Aliases[source]=list()\n            New_Aliases[source].append(alias)\n\nAliases_Dict[Old_Cpd_ID]=Old_Aliases\nAliases_Dict[New_Cpd['id']]=New_Aliases\n\n##########################################################\n#\n# Save names, aliases, and compounds\n# These are written to separate files\n# If the aliases are updated, then the list of structures\n# has to be re-composed\n#\n##########################################################\n\nif(len(New_Names)>0):\n    if(New_Cpd['name'] not in New_Names):\n        New_Cpd['name']=New_Names[0]\n        New_Cpd['abbreviation']=New_Names[0]\n    Names_Dict[New_Cpd['id']]=New_Names\n    if(Dry_Run==0):\n        print(\"Saving update to names\")\n        compounds_helper.saveNames(Names_Dict)\n\nif(len(New_Aliases)>0):\n    if(Dry_Run==0):\n        print(\"Saving update to aliases\")\n        compounds_helper.saveAliases(Aliases_Dict)\n\nif(Dry_Run==0):\n    print(\"Saving disambiguation of \"+Old_Cpd_ID+\" as \"+New_Cpd['id'])\n    compounds_helper.saveCompounds(compounds_dict)\n\n##########################################################\n#\n# Find reactions to disambiguate\n#\n##########################################################\n\n#Reactions\n#Read all Provenance\nProvenance_Root=os.path.dirname(__file__)+\"/../../../Biochemistry/Aliases/Provenance/\"\nProv_Rxns=dict()\nwith open(Provenance_Root+\"Original_Reaction_Compound_Links.tsv\") as pfh:\n    for line in pfh.readlines():\n        line=line.strip()\n        (model,rxn,cpds)=line.split(\"\\t\")\n\n        Found_Cpd=\"\"\n        for cpd in cpds.split(\"|\"):\n            if(cpd in aliases_dict and model in aliases_dict[cpd]):\n                Found_Cpd=cpd\n\n        if(Found_Cpd == \"\"):\n            continue\n\n        Is_Old=False\n        if(Found_Cpd != \"\" and model in Old_Aliases and Found_Cpd in Old_Aliases[model]):\n            Is_Old=True\n\n        if(model not in Prov_Rxns):\n            Prov_Rxns[model]=dict()\n        Prov_Rxns[model][rxn]=Is_Old\n        \n#Load Reactions\n#Using reaction provenance and disambiguation details\n#We find the list of reactions that belong to either\n#the original compound or the newly disambiguated compound\nreactions_helper = Reactions()\nreaction_aliases_dict = reactions_helper.loadMSAliases()\nreaction_names_dict = reactions_helper.loadNames()\nreaction_ecs_dict = reactions_helper.loadECs()\nreactions_dict = reactions_helper.loadReactions()\n\n#Three types of reactions:\n# 1) Retain original/old reaction\nkeep_reactions=dict()\n# 2) Retain but change compound in stoichiometry\nupdate_reactions=dict()\n# 3) Split into new reaction with updated stoichiometry\ndisambiguate_reactions=dict()\nfor rxn in reaction_aliases_dict:\n    Is_Old_Dict=dict()\n    Source_Alias=dict()\n\n    for source in reaction_aliases_dict[rxn]:\n        if(source not in Prov_Rxns):\n            continue\n\n        for alias in reaction_aliases_dict[rxn][source]:\n            if(alias not in Prov_Rxns[source]):\n                continue\n\n            #This is key, and it happens because we're using external aliases\n            #It's entirely possible that:\n            #    (1) the reaction alias is associated with multiple distinct ModelSEED reactions\n            #    (2) the compound alias, for a given source, was not associated with the compound in question\n            if(Old_Cpd_ID not in reactions_dict[rxn]['stoichiometry']):\n                continue\n\n            Is_Old_Dict[Prov_Rxns[source][alias]]=1\n\n    if(len(Is_Old_Dict)==1):\n        if(list(Is_Old_Dict)[0] is True):\n            #This means the reaction is not changed\n            keep_reactions[rxn]=1\n        else:\n            #This means that the reaction's stoichiometry must be updated (in place)\n            update_reactions[rxn]=1\n    elif(len(Is_Old_Dict)==2):\n        #This means that the reaction has previously incorrectly merged reactions\n        #And the reaction needs to be split into two new reactions\n        disambiguate_reactions[rxn]=dict()\n\nif(len(update_reactions)==0 and len(disambiguate_reactions)==0):\n    print(\"No reactions need updating\")\n    sys.exit()\n\n##########################################################\n#\n# Handle reactions to update\n#\n##########################################################\n\nprint(\"Updating \"+str(len(update_reactions))+\" reactions\")\nfor rxn in sorted(update_reactions):\n\n    #Parse old stoichiometry into array\n    old_stoichiometry=reactions_dict[rxn][\"stoichiometry\"]\n    rxn_cpds_array=reactions_helper.parseStoich(old_stoichiometry)\n\n    #Adjust for new compound\n    reactions_helper.replaceCompound(rxn_cpds_array,Old_Cpd_ID,New_Cpd['id'])\n\n    #Rebuild with old compound\n    new_stoichiometry = reactions_helper.buildStoich(rxn_cpds_array)\n    reactions_helper.rebuildReaction(reactions_dict[rxn],new_stoichiometry)\n\n##########################################################\n#\n# Handle reactions to disambiguate\n# Names and ECs must be disambiguated further in:\n# \n##########################################################\n\n#Find last identifier\nlast_identifier = list(sorted(reactions_dict))[-1]\nidentifier_count = int(re.sub('^rxn','',last_identifier))\n\n#Generate codes for matching\nreactions_codes = reactions_helper.generateCodes(reactions_dict)\n\nprint(\"Disambiguating \"+str(len(disambiguate_reactions))+\" reactions\")\nnew_reaction_count=dict()\nmetadata=Disambiguated_Compound['metadata']\ndisambiguation_object = {'metadata':metadata,\n                         'reactions':[],\n                         'method':\"disambiguation\"}\n\nfor original_rxn in sorted(disambiguate_reactions):\n\n    #Parse old stoichiometry into array\n    old_stoichiometry=reactions_dict[original_rxn][\"stoichiometry\"]\n    rxn_cpds_array=reactions_helper.parseStoich(old_stoichiometry)\n    old_rxn_code = reactions_helper.generateCode(rxn_cpds_array)\n\n    #Adjust for new compound and retrieve code for checking against database\n    reactions_helper.replaceCompound(rxn_cpds_array,Old_Cpd_ID,New_Cpd['id'])\n    rxn_code = reactions_helper.generateCode(rxn_cpds_array)\n\n    disambig_rxn = None\n    already_in_database = False\n    if(rxn_code in reactions_codes):\n        disambig_rxn = sorted(list(reactions_codes[rxn_code]))[0]\n        already_in_database = True\n\n    #If there is a match, we assume nothing needs changing\n    #Other than aliases. Move aliases from 'new' to 'matched'\n    if(disambig_rxn is None):\n\n        #Increment identifier\n        identifier_count+=1\n        next_identifier = 'rxn'+str(identifier_count)\n\n        #Copy reaction\n        new_rxn = copy.deepcopy(reactions_dict[original_rxn])\n        new_rxn['id']=next_identifier\n        disambig_rxn = new_rxn['id']\n\n        reactions_dict[new_rxn['id']]=new_rxn\n        new_reaction_count[new_rxn['id']]=1\n\n        #Rebuild with old compound\n        new_stoichiometry = reactions_helper.buildStoich(rxn_cpds_array)\n        reactions_helper.rebuildReaction(new_rxn,new_stoichiometry)\n\n        #Finally, because several new reactions may share equations\n        if(rxn_code not in reactions_codes):\n            reactions_codes[rxn_code]=dict()\n        reactions_codes[rxn_code][new_rxn['id']]=1\n\n    #Handle Aliases\n    keep_reaction_aliases = dict()\n    new_reaction_aliases = dict()\n    for source in reaction_aliases_dict[original_rxn]:\n        for alias in reaction_aliases_dict[original_rxn][source]:\n            if(source in Prov_Rxns and alias in Prov_Rxns[source]):\n                #Need to find and handle aliases that are in loaded provenance\n                if(Prov_Rxns[source][alias] is True):\n                    if(source not in keep_reaction_aliases):\n                        keep_reaction_aliases[source]=list()\n                    keep_reaction_aliases[source].append(alias)\n                else:\n                    if(source not in new_reaction_aliases):\n                        new_reaction_aliases[source]=list()\n                    new_reaction_aliases[source].append(alias)\n\n    #Need to find and retain aliases that are not in loaded provenance\n    #This occurs for one or two reasons:\n    #    (i) original provenance is missing\n    #    (ii) source is an abstraction (i.e. BiGG)\n    #Here, we check to see if the alias itself has been moved, regardless of source\n    for original_source in reaction_aliases_dict[original_rxn]:\n        for original_alias in reaction_aliases_dict[original_rxn][original_source]:\n            if(original_source not in Prov_Rxns or original_alias not in Prov_Rxns[original_source]):\n                alias_moved=False\n                for moved_source in new_reaction_aliases:\n                    if(original_alias in new_reaction_aliases[moved_source]):\n                        alias_moved=True\n\n                if(alias_moved==True):\n                    if(original_source not in new_reaction_aliases):\n                        new_reaction_aliases[original_source]=list()\n                    new_reaction_aliases[original_source].append(original_alias)\n                else:\n                    if(original_source not in keep_reaction_aliases):\n                        keep_reaction_aliases[original_source]=list()\n                    if(original_alias not in keep_reaction_aliases[original_source]):\n                        keep_reaction_aliases[original_source].append(original_alias)\n\n    if(already_in_database is True):\n#        print(original_rxn,keep_reaction_aliases)\n#        print(disambig_rxn,new_reaction_aliases)\n#        print(\"================================\")\n        pass\n\n    reaction_aliases_dict[original_rxn]=keep_reaction_aliases\n    reaction_aliases_dict[disambig_rxn]=new_reaction_aliases\n\n    #Collect names and EC numbers for disambiguation\n    ecname_disambig_dict={'from':{'id':original_rxn,\n                                  'def':reactions_dict[original_rxn]['definition']},\n                          'to':{'id':disambig_rxn,\n                                'def':reactions_dict[disambig_rxn]['definition']},\n                          'names':{},'ecs':{}}\n    \n    for name in reaction_names_dict[original_rxn]:\n        ecname_disambig_dict['names'][name]=\"true\"\n    for ec in reaction_ecs_dict[original_rxn]:\n        ecname_disambig_dict['ecs'][ec]=\"true\"\n    \n    if(disambig_rxn in reaction_names_dict):\n        for name in reaction_names_dict[original_rxn]:\n            if(name in ecname_disambig_dict):\n                ecname_disambig_dict['names'][name]=\"both\"\n            else:\n                ecname_disambig_dict['names'][name]=\"false\"\n        for ec in reaction_ecs_dict[original_rxn]:\n            if(ec in ecname_disambig_dict):\n                ecname_disambig_dict['ecs'][ec]=\"both\"\n            else:\n                ecname_disambig_dict['ecs'][ec]=\"false\"\n\n    disambiguation_object['reactions'].append(ecname_disambig_dict)\n\nif(Dry_Run==0):\n    print(\"Saving update to aliases\")\n    reactions_helper.saveAliases(reaction_aliases_dict)\n    print(\"Saving disambiguated reactions\")\n    reactions_helper.saveReactions(reactions_dict)\n\nif(len(disambiguation_object['reactions'])>0):\n    print(\"Saving reaction names and ECs for disambiguation\")\n    with open('Objects/'+Old_Cpd_ID+'_'+metadata['user']+'_Reactions_Object.json','w') as f:\n        json_string = json.dumps(disambiguation_object,\n                                 indent=4, sort_keys=True,\n                                 separators=(',', ': '), ensure_ascii=True)\n        f.write(json_string)\n\n#Scripts to run afterwards\n#../Structures/List_ModelSEED_Structures.py\n#../Structures/Update_Structure_Formula_Charge.py\n#../Biochemistry/Update_Compound_Aliases.py\n#../Biochemistry/Rebalance_Reactions.py (very important)\n#../Biochemistry/Adjust_Reaction_Protons.py\n#../Biochemistry/Adjust_Reaction_Water.py\n#../Biochemistry/Merge_Reactions.py (merges may happen because of water)\n","repo_name":"ModelSEED/ModelSEEDDatabase","sub_path":"Scripts/Curation/Disambiguation/Integrate_Disambiguated_Compound.py","file_name":"Integrate_Disambiguated_Compound.py","file_ext":"py","file_size_in_byte":16800,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"77"}
+{"seq_id":"35615758057","text":"def max_min_index(a):\n    maxi = 0\n    mini = 101\n    ans_max = -1\n    ans_min = -1\n    for i in range(len(a)):\n        if maxi < a[i]:\n            maxi = a[i]\n            ans_max = i\n        if mini >= a[i]:\n            mini = a[i]\n            ans_min = i\n    return ans_max, ans_min\n\n\nn = int(input())\na = [int(i) for i in input().split()]\ni, j = max_min_index(a)\ndi, dj = i, n - j - 1\nif j < i:\n    print(di+dj-1)\nelse:\n    print(di + dj)\n","repo_name":"ciberdiego123/python-coding","sub_path":"arrival_general.py","file_name":"arrival_general.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28111586109","text":"'''\r\n--------------------------------------------------------------------\r\nEdyBot - Virtual assistant for teachers of Ciudad Educativa\r\nDeveloped by Camilo Rivillas - camilorivillas@nusoft.com.co\r\nNúcleo Software SAS\r\nLast update 2022-09-06\r\n\r\nFinal Project of the Master in Artificial Intelligence UNIR 2022.\r\nThe partial or total reproduction and use of this code is permitted\r\nas long as the author is credited\r\n--------------------------------------------------------------------\r\nEdyBot - Asistente virtual para maestros de Ciudad Educativa\r\nDesarrollado por Camilo Rivillas - camilorivillas@nusoft.com.co\r\nNúcleo Software SAS\r\nÚltima actualización 2022-09-06\r\n\r\nTrabajo Final de la Maestría en Inteligencia Artificial UNIR 2022.\r\nLa reproducción y uso parcial o total de este código está permitido\r\nsiempre y cuando se de crédito al autor\r\n--------------------------------------------------------------------\r\n'''\r\n\r\nfrom typing import Any, Text, Dict, List\r\nimport actions.conversations as cnv\r\nimport actions.db_connection as dbc\r\nfrom sqlalchemy import null\r\nfrom datetime import datetime\r\n\r\n'''\r\nThis class handles all processes related to conversations \r\n'''\r\nclass replies_handler():\r\n\t\r\n\t'''\r\n\tClass constructor\r\n\t'''\r\n\tdef __init__(\r\n\t\tself,\r\n\t\t_logger,\r\n\t\t_tracker,\r\n\t\t_db\r\n\t):\r\n\t\tself._logger \t= _logger\r\n\t\tself._tracker \t= _tracker\r\n\t\tself._db \t\t= _db\r\n\r\n\t'''\r\n\tStores the conversation into core_support.bot_cnv_conversations\r\n\t'''\r\n\tdef log_usefulness(\r\n\t\tself,\r\n\t\tuseful \t\t= 'calculate',\r\n\t\tquestion \t= '',\r\n\t\tquestion_position = 2\r\n\t) -> List:\r\n\r\n\t\t_logger \t\t\t= self._logger\r\n\t\t_tracker \t\t\t= self._tracker\r\n\t\t_db \t\t\t\t= self._db\r\n\r\n\t\t# Get last utterance\r\n\r\n\t\tlatest_user_intent \t= _tracker.latest_message[\"intent\"][\"name\"]\r\n\t\tlatest_user_msg \t= _tracker.latest_message[\"text\"]\r\n\t\tcnv_id \t\t\t\t= _tracker.sender_id\r\n\t\tslots \t\t\t\t= _tracker.slots\r\n\t\ts_id \t\t\t\t= slots[\"school.s_id\"] \tif (\"school.s_id\" in slots and slots[\"school.s_id\"] is not None) \telse '0'\r\n\t\tu_id \t\t\t\t= slots[\"user.u_id\"] \tif (\"user.u_id\" in slots and slots[\"user.u_id\"] is not None) \t\telse '0'\r\n\r\n\t\t# Get conversation history\r\n\t\t_cnv \t\t\t\t= cnv.conversations_handler(_logger,_tracker,_db)\r\n\t\tcnv_history \t\t= _cnv.get_history()\r\n\r\n\t\treply_date \t\t\t= null\r\n\t\tcnv_recent_history \t= cnv_history[1:7]\r\n\r\n\t\tif(\r\n\t\t\tquestion == ''\r\n\t\t\tand\r\n\t\t\tcnv_recent_history[question_position] is not None\r\n\t\t):\r\n\t\t\tif(\"text\" in cnv_recent_history[question_position]):\r\n\t\t\t\tquestion = cnv_recent_history[question_position]['text']\r\n\r\n\t\t#_logger.warning('question: ' + question)\r\n\r\n\t\tstr_cnv_recent_history \t= str(cnv_recent_history)\r\n\r\n\t\t'''\r\n\t\tEmoticons must be removed from cnv history. Otherwise, mysql will raise an error: 'Incorrect string value...'\r\n\t\t@todo: It can be fixed with utf8mb4\r\n\t\thttps://mathiasbynens.be/notes/mysql-utf8mb4#character-sets\r\n\t\t'''\r\n\r\n\t\tstr_cnv_recent_history \t= str_cnv_recent_history.encode(\"ascii\", \"ignore\")\r\n\t\tstr_cnv_recent_history \t= str_cnv_recent_history.decode()\r\n\r\n\t\tintent              = null\r\n\t\tconfidence          = null\r\n\r\n\t\tif(useful == 'calculate'):\r\n\t\t\tuseful              = latest_user_intent\r\n\r\n\t\t#_logger.warning('useful: ' + useful)\r\n\r\n\t\tfor evt in cnv_recent_history:\r\n\t\t\t\r\n\t\t\tfaq_or_chitchat = False\r\n\r\n\t\t\tif(\r\n\t\t\t\t\"intent\" in evt\r\n\t\t\t\tand\r\n\t\t\t\t(\r\n\t\t\t\t\tstr(evt[\"intent\"]).startswith(\"faq/\")\r\n\t\t\t\t\tor\r\n\t\t\t\t\tstr(evt[\"intent\"]).startswith(\"chitchat/\")\r\n\t\t\t\t)\r\n\t\t\t):\r\n\t\t\t\tfaq_or_chitchat = True\r\n\r\n\t\t\tif(\r\n\t\t\t\treply_date is null \r\n\t\t\t\tand \"timestamp\" in evt\r\n\t\t\t):\r\n\t\t\t\treply_date = datetime.fromtimestamp(evt[\"timestamp\"])\r\n\t\t\t\t#_logger.warning('reply_date: ' + str(reply_date))\r\n\r\n\t\t\tif(\r\n\t\t\t\tintent is null \r\n\t\t\t\tand \"intent\" in evt\r\n\t\t\t\tand faq_or_chitchat\r\n\t\t\t):\r\n\t\t\t\tintent = evt[\"intent\"]\r\n\t\t\t\t#_logger.warning('intent: ' + str(intent))\r\n\t\t\t\r\n\t\t\tif(\r\n\t\t\t\tconfidence is null\r\n\t\t\t\tand \"intent\" in evt\r\n\t\t\t\tand faq_or_chitchat\r\n\t\t\t):\r\n\t\t\t\tconfidence = evt[\"confidence\"]\r\n\t\t\t\t#_logger.warning('confidence: ' + str(confidence))\r\n\t\t\t\tbreak\r\n\r\n\t\t# Get or create the conversation\r\n\t\t_cnv.get_or_create()\r\n\r\n\t\t#_logger.warning('conversation created')\r\n\r\n\t\tif(\r\n\t\t\tintent is not null\r\n\t\t\tand confidence is not null\r\n\t\t):\r\n\t\t\t#_logger.warning('inserting into bot_cnv_replies')\r\n\t\t\t# Store the conversation latest replies and usefulness into core_support.bot_cnv_replies\r\n\t\t\trow = (cnv_id, s_id, u_id, reply_date, question, str_cnv_recent_history, intent, confidence, useful)\r\n\r\n\t\t\tquery = f\"\"\"insert into bot_cnv_replies \r\n\t\t\t(cnv_id, s_id, u_id, reply_date, question, cnv_history, intent, confidence, useful) \r\n\t\t\tvalues \r\n\t\t\t(%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\r\n\r\n\t\t\t_db.insert(query, row)\r\n\t\t\r\n\t\t#_logger.warning('end')\r\n\r\n\r\n\t\treturn []","repo_name":"camilorivillas/edybot__tfm_ia_unir_2022","sub_path":"actions/replies.py","file_name":"replies.py","file_ext":"py","file_size_in_byte":4655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27982277744","text":"import os\nimport logging\nfrom dotenv import load_dotenv\nfrom inspect import getmembers, isfunction\n\nfrom telegram.ext import Defaults, Updater, CommandHandler, MessageHandler, \\\n    Filters, CallbackQueryHandler, InlineQueryHandler\nfrom telegram import ParseMode\n\nfrom config import Config\nfrom Bot import command\nfrom Bot.errors import unknown\nfrom Bot.buttons import button\nfrom Bot.photo import paymentScreen\nfrom Bot.message import accountMessage\nfrom Bot.database.models import Users, db\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\nload_dotenv(os.path.join(basedir, '.env'))\n\nif __name__ == \"__main__\":\n    print(\"loading...\")\n\n    TOKEN = os.environ.get('TOKEN')\n    defaults = Defaults(parse_mode=ParseMode.HTML)\n    updater = Updater(token=TOKEN, defaults=defaults, use_context=True)\n    dispatcher = updater.dispatcher\n\n    # Commands #\n    for name, handler in [o for o in getmembers(command) if isfunction(o[1])]:\n        dispatcher.add_handler(CommandHandler(name, handler))\n\n    # Buttons #\n    updater.dispatcher.add_handler(CallbackQueryHandler(button))\n\n    # Unknown Command #\n    dispatcher.add_handler(\n        MessageHandler(Filters.command & Filters.private, unknown)\n    )\n\n    ### IMAGE ####\n    dispatcher.add_handler(\n        MessageHandler(Filters.photo, paymentScreen)\n    )\n\n    ### MESSAGE ###\n    dispatcher.add_handler(\n        MessageHandler(Filters.text, accountMessage)\n    )\n\n    updater.start_polling()\n    logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n                        level=logging.INFO)\n\n    jobs = updater.job_queue\n\n    # Check Admin #\n    user = db.session.query(Users).filter_by(chat_id=Config.admin_chat_id).first()\n    if not user:\n        user = Users(chat_id=Config.admin_chat_id)\n        db.session.add(user)\n        db.session.commit()\n\n    if not user.admin:\n        user.admin = True\n        user.save()\n\n    print(\"Bot started\")\n","repo_name":"Nikappa57/account-shop-bot","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1057510981","text":"import torch\r\nimport torch.nn as nn\r\nfrom ..utils.utils import Eval, join_opts, stateful_zip\r\nfrom .gan import Gan, hinge_loss_D, hinge_loss_G, cGan\r\nfrom torch.nn.functional import l1_loss as L1\r\n\r\nclass CycleGan(Gan):\r\n    def __init__(self,*args,cycle_strength=10,**kwargs):\r\n        super().__init__(*args,**kwargs)\r\n        self.gan1 = cGan(*args,**kwargs)\r\n        self.gan2 = cGan(*args,**kwargs)\r\n        # The join needs to be stateful so that gopt and dopt loads/saves work\r\n        # or we just add the state in the state dict explicitly\r\n        self.g_optimizer = join_opts(self.gan1.g_optimizer,self.gan2.g_optimizer)\r\n        self.d_optimizer = join_opts(self.gan1.d_optimizer,self.gan2.d_optimizer)\r\n        self.dataloaders['train'] = stateful_zip(self.dataloaders['A'],self.dataloaders['B'])\r\n\r\n\r\n    def discLoss(self, data):\r\n        return self.gan1.disLoss(data) + self.gan2.disloss(data[::-1])\r\n\r\n    def genLoss(self,data):\r\n        \"\"\" Adversarial and cycle loss\"\"\"\r\n        xa,xb = data\r\n        adversarial_loss = self.gan1.genLoss(data[::-1]) + self.gan2.genLoss(data)\r\n        G1,G2 = self.gan1.G, self.gan2.G \r\n        cycle_loss = L1(G2(G1(xa)),xa) + L1(G1(G2(xb)),xb)\r\n        return adversarial_loss + self.hypers['cycle_strength']*cycle_loss\r\n\r\n    def logStuff(self, i, minibatch=None):\r\n        raise NotImplementedError\r\n\r\n    def state_dict(self):\r\n        extra_state = {\r\n            'gan1_state':self.gan1.state_dict(),\r\n            'gan2_state':self.gan2.state_dict(),\r\n            'AB_loader_state':self.dataloaders['train'].state_dict(),\r\n        }\r\n        return {**super(cGan,self).state_dict(),**extra_state}\r\n\r\n    def load_state_dict(self,state):\r\n        super(cGan,self).load_state_dict(state)\r\n        self.gan1.load_state_dict(state['gan1_state'])\r\n        self.gan2.load_state_dict(state['gan2_state'])\r\n        self.dataloaders['train'].load_state_dict()","repo_name":"mfinzi/olive-oil-ml","sub_path":"oil/model_trainers/cycleGan.py","file_name":"cycleGan.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"29311944859","text":"#Juego adivina mi número\nimport random\ndef resultado(x,y):\n    if x < y:\n        print(\"mi número es menor\")\n    elif x > y:\n        print(\"mi número es mayor\")\n    elif x == y:\n        print(\"Adivinaste, mi número era: \", x)\n    return\ncontador = 0\nA = random.randint(0,20)\nprint(A)\nwhile True:\n    contador = contador + 1\n    if contador <6 :\n        B = int(input(\"Adivine el Número del 1 al 20: \"))\n        c=resultado(A,B)\n        if A==B:\n            break\n    else:\n        print(\"No adivinaste, mi número era: \",A)\n        break","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej12/hito1_ej12_ca3ce0a8227f8dbab2c6f24899d5b78b.py","file_name":"hito1_ej12_ca3ce0a8227f8dbab2c6f24899d5b78b.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41436570982","text":"from django.views import View\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\nfrom testingformapp.models import Menu, Option\nfrom testingformapp.forms import OptionFormSet, MenuForm, OrderForm\n\n\nclass HomeView(LoginRequiredMixin, View):\n    def get(self, request):\n        has_daily_menu = Menu.objects.has_daily_menu()\n        daily_menu = Menu.objects.get_daily_menu()\n\n        context = {\n            'has_daily_menu': has_daily_menu,\n            'daily_menu': daily_menu,\n        }\n        return render(request, 'testingformapp/home.html', context)\n\n\nclass MenuListView(LoginRequiredMixin, View):\n    def get(self, request):\n        list = Menu.objects.order_by('-day')\n        context = { 'list': list }\n        return render(request, 'testingformapp/menu_list.html', context)\n\n\nclass MenuCreateView(LoginRequiredMixin, View):\n    def get(self, request):\n        menu_form = MenuForm()\n        options_form = OptionFormSet()\n\n        context = {\n            'menu_form': menu_form,\n            'options_form': options_form,\n        }\n\n        return render(request, 'testingformapp/menu_create.html', context)\n\n    def post(self, request):\n        menu_form = MenuForm(request.POST)\n        options_form = OptionFormSet(request.POST)\n\n        if menu_form.is_valid():\n            menu = menu_form.save()\n            options_form = OptionFormSet(request.POST, instance=menu)\n\n            if options_form.is_valid():\n                options_form.save()\n\n                return redirect('menu_detail', menu_id=menu.id)\n        \n        context = {\n            'menu_form': menu_form,\n            'options_form': options_form,\n        }\n\n        return render(request, 'testingformapp/menu_create.html', context)\n\n\nclass MenuUpdateView(LoginRequiredMixin, View):\n    def get(self, request, menu_id):\n        menu = get_object_or_404(Menu, pk=menu_id)\n        menu_form = MenuForm(instance=menu)\n        options_form = OptionFormSet(instance=menu)\n\n        context = {\n            'menu_form': menu_form,\n            'options_form': options_form,\n        }\n\n        return render(request, 'testingformapp/menu_create.html', context)\n\n    def post(self, request, menu_id):\n        menu = get_object_or_404(Menu, pk=menu_id)\n        menu_form = MenuForm(request.POST, instance=menu)\n\n        if menu_form.is_valid():\n            menu = menu_form.save()\n            options_form = OptionFormSet(request.POST, instance=menu)\n\n            if options_form.is_valid():\n                options_form.save()\n\n                return redirect('menu_detail', menu_id=menu.id)\n        \n        context = {\n            'menu_form': menu_form,\n            'options_form': options_form,\n        }\n\n        return render(request, 'testingformapp/menu_create.html', context)\n\n\nclass MenuDetailView(LoginRequiredMixin, View):\n    def get(self, request, menu_id):\n        menu = get_object_or_404(Menu, pk=menu_id)\n        context = { 'menu': menu }\n        return render(request, 'testingformapp/menu_detail.html', context)\n\n\nclass OrderCreateView(View):\n    def get(self, request, menu_id):\n        menu = get_object_or_404(Menu, pk=menu_id)\n        form = OrderForm(menu=menu)\n        context = {\n            'menu': menu,\n            'form': form,\n        }\n        return render(request, 'testingformapp/order_create.html', context)\n    \n    def post(self, request, menu_id):\n        menu = get_object_or_404(Menu, pk=menu_id)\n        form = OrderForm(request.POST, menu=menu)\n\n        if form.is_valid():\n            order = form.save()\n\n            context = {\n                'menu': menu,\n                'order': order,\n            }\n\n            return render(request, 'testingformapp/order_create_success.html', context)\n\n        context = {\n            'menu': menu,\n            'form': form,\n        }\n        return render(request, 'testingformapp/order_create.html', context)","repo_name":"jesusgoku/meal-delivery","sub_path":"testingformapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14623877692","text":"# A RegEx, or Regular Expression, is a sequence of characters that forms a search pattern.\nimport re\n\ntxt = \"NGuyen Van Dat. I am styding Science of university\"\n# Check if the string starts with \"NGuyen\" and ends with \"sity\":\nx = re.search(\"^NGuyen .*sity$\", txt)\nif (x):\n    print(\"YES! We have a match!\")\nelse:\n    print(\"No match\")\n# The findall() Function\n# The findall() function returns a list containing all matches.\n\ntxt = \"The rain in Spain\"\nx = re.findall(\"ai\", txt)\nprint(x)\n\nx = re.findall(\"Portugal\", txt)\nprint(x)\n\ntxt = \"The rain in Spain\"\nx = re.search(\"\\s\", txt)\ntxt = \"The rain in Spain\"\ny = re.search(\"Portugal\", txt)\nprint(y)\nprint(\"The first white-space character is located in position:\", x.start())\nx = re.split(\"\\s\", txt)\nprint(x)\n\nx = re.split(\"\\s\", txt, 1)\nprint(x)\n\n# The sub() function replaces the matches with the text of your choice:\ntxt = \"The rain in Spain\"\nx = re.sub(\"\\s\", \"9\", txt)\nprint(x)\n\n# You can control the number of replacements by specifying the count parameter:\nx = re.sub(\"\\s\", \"9\", txt, 2)\nprint(x)\n\n# Match Object\nx = re.search(\"ai\", txt)\nprint(x) #this will print an object\n\n\ntxt = \"The rain in Spain\"\nx = re.search(r\"\\bS\\w+\", txt)\nprint(x.span())\nx = re.search(r\"\\bS\\w+\", txt)\nprint(x.string)\nx = re.search(r\"\\bS\\w+\", txt)\nprint(x.group())\n\n","repo_name":"DatPHP/python_basic","sub_path":"RegEx.py","file_name":"RegEx.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15747923712","text":"import json\n\nif __name__ == \"__main__\":\n    dict0 = {\n            \"session_id\": 1,\n            \"responses\": \"response_list\"}\n\n    name_emb = {'a': '1111', 'b': '2222', 'c': '3333', 'd': dict0}\n\n    # xx = json.dumps(dict0, indent=4)\n    # print(xx)\n    # 🔥🔥 原来是让 dict 显示的更加漂亮, 对齐缩进 4 个空格\n    yy = json.dumps(name_emb, indent=4)\n    print(yy)","repo_name":"fly2rain/try_git_pycharm","sub_path":"flask_exp/try_json.py","file_name":"try_json.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37149275946","text":"from numpy import tile\nfrom numpy.ma import array, zeros, shape\n\n'''\n读取数据\n\n'''\ndef createDataSet():\n    '''\n    准备数据\n\n    :return:\n    '''\n    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1], [0.5, 0.6]])\n    labels = ['A', 'A', 'B', 'B', 'C']\n    return group, labels\n\n\ndef file2matrix(file):\n    '''\n    从文件中读取数据\n\n    :param file: 文件名称\n    :return:\n    '''\n    fr = open(file)\n    lines = fr.readlines()\n    numberOfLines = len(lines)\n    # 初始化数组  numberOfLines 行，3 列\n    returnMat = zeros((numberOfLines, 3))\n    # print(returnMat)\n    classLabelVector = []\n    index = 0\n    for line in lines:\n        line = line.strip()\n        listFromLine = line.split(' ')\n        # print(type(listFromLine))\n        # print(len(listFromLine))\n        # print((listFromLine))\n        # 将读到的数据写入数组中\n        returnMat[index, :] = listFromLine[0:3]\n        # 将最后一个数据当做标签\n        classLabelVector.append(int(listFromLine[-1]))\n        index += 1\n    return returnMat, classLabelVector\n\n\ndef autoNorm(dataSet):\n    '''\n    归一化数值\n\n    :param dataSet: 集合\n    :return:\n    '''\n    minVals = dataSet.min(0)\n    maxVals = dataSet.max(0)\n    ranges = maxVals - minVals\n    # 获取dataset的维度\n    normDataSet = zeros(shape(dataSet))\n    # 集合的行数\n    m = dataSet.shape[0]\n    normDataSet = dataSet - tile(minVals, (m, 1))\n    normDataSet = normDataSet / tile(ranges, (m, 1))\n    return normDataSet, ranges, minVals\n","repo_name":"chenxk/StudyDemo","sub_path":"com/study/ai/ms/knn/KNNData.py","file_name":"KNNData.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73795656248","text":"from __future__ import absolute_import\n\nimport zlib\n\n\ndef compress_file(fp, level=6):\n    compressor = zlib.compressobj(level)\n    z_chunks = []\n    chunks = []\n    for chunk in fp.chunks():\n        chunks.append(chunk)\n        z_chunks.append(compressor.compress(chunk))\n    return (b''.join(z_chunks) + compressor.flush(), b''.join(chunks))\n","repo_name":"NetEaseGame/Sentry","sub_path":"src/sentry/utils/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"19457966402","text":"from chardet.universaldetector import UniversalDetector\nimport csv\nimport os\nimport pandas as pd\nimport magic\nimport xlrd\n\n\n\n\nclass import_file:\n    \n    def __init__ (self, file):\n        self.file = file\n    \n    def __detect_format (self):\n        #mime = MimeTypes()\n        #mime_type = mime.guess_type(self.file)[0]\n        mime_type = magic.from_file(self.file, mime=True)\n        return mime_type\n        \n    def __fabric (self, format_file):\n        if format_file == 'text/plain':\n            CSV = CSV_reader(self.file)\n            DF = CSV.parse()\n            return DF\n        elif format_file == 'application/vnd.ms-excel' or format_file == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':\n            XLS = XLS_reader(self.file)\n            DF = XLS.parse()\n            return DF\n        else:\n            raise Exception(format_file, ' - format is not supported')\n    def read (self):\n        format_file = self.__detect_format()\n        return self.__fabric(format_file)\n\nclass CSV_reader (import_file):\n    \n    def __init__ (self, file):\n        self.file = file\n        \n    def __find_encoding (self):\n        detector = UniversalDetector()\n        with open(self.file, 'rb') as fh:\n            for i,line in enumerate(fh):\n                detector.feed(line)\n                if i>10:\n                    break\n        detector.close()\n        encoding = detector.result['encoding']\n        return encoding   \n    \n    def __find_sep (self):\n        encoding = self.__find_encoding()\n        SEPARATORS=['\\x00', '\\x01',  '^', ':', ',', '\\t', ':', ';', '|', '~', ' ']\n        with open(self.file, 'r', encoding=encoding) as csvfile:\n            line = next(csvfile)\n            try:\n                dialect = csv.Sniffer().sniff(line, SEPARATORS)\n                sep = dialect.delimiter\n            except:\n                sep = '\\t'\n            return sep, encoding\n    \n    def parse (self):\n        sep, encoding = self.__find_sep()\n        try:\n            DF = pd.read_csv(self.file, sep=sep, encoding=encoding)\n            return DF\n        except Exception as E:\n            raise Exception('Bad, format error:', E)      \n\nclass XLS_reader (import_file):\n    \n    def __init__ (self, file):\n        self.file = file\n    \n    def count_sheets (self):\n        wb = xlrd.open_workbook(self.file, on_demand=True)\n        count_st = len(wb.sheet_names())\n        if count_st > 1:\n            print('WARNING: where ', count_st, ' in file. Names of sheets', wb.sheet_names())\n    \n    def parse (self, sheet_name=0):\n        self.count_sheets()\n        try:\n            DF = pd.read_excel(self.file, sheet_name=sheet_name)\n            return DF\n        except Exception as E:\n            raise Exception('Bad, format error:', E)      \n","repo_name":"vipksmosar/scanner","sub_path":"support/import_file.py","file_name":"import_file.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17298528551","text":"import os\n\nfrom flask import Flask\nfrom .database import db\nfrom flask_login import LoginManager\nfrom flask_socketio import SocketIO\n\n\ndef create_app():\n    app = Flask(__name__)  # создание экземпляра приложения\n\n    # конфигурация приложения\n    app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///project.db'  # назначение пути к базе данных\n    app.config['SECRET_KEY'] = 'TSSiteFlask'  # секретный ключ приложения\n    app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False  # отключение предупреждений о модификации путей\n\n    # настройки для S3-хранилища\n    app.config['AWS_ACCESS_KEY_ID'] = os.environ.get('AWS_ACCESS_KEY_ID')\n    app.config['AWS_SECRET_ACCESS_KEY'] = os.environ.get('AWS_SECRET_ACCESS_KEY')\n    app.config['AWS_REGION_NAME'] = os.environ.get('AWS_REGION_NAME', 'eu-central-1')\n    app.config['S3_BUCKET_NAME'] = os.environ.get('AWS_REGION_NAME', 'haraka-local')\n    app.config[\"MEDIA_PATH\"] = \"/static/pic/avatars\"\n\n    # конфигурация oauth провайдеров\n    app.config['OAUTH2_PROVIDERS'] = {\n        'telegram': {\n            'client_id': \"6266128295\",\n            'client_secret': 'AAHmsbw16Cm11NtgDtZ_NZShdupIRWhw_Hw',\n            'authorize_url': 'https://oauth.telegram.org/authorize',\n            'token_url': 'https://oauth.telegram.com/access_token',\n            'userinfo': {\n                'url': 'https://api.github.com/user/emails',\n            },\n            'scopes': [],\n        },\n\n        # https://dev.vk.com/api/access-token/implicit-flow-user\n        'vk': {\n            'client_id': '51679319',\n            'client_secret': 'URCsP1omgcBo0XRiBl5J',\n            'authorize_url': 'https://oauth.vk.com/authorize',\n            'token_url': 'https://oauth.vk.com/access_token',\n            'userinfo': {\n                'url': 'https://oauth.vk.com/blank.html',\n            },\n            'scopes': ['email'],\n        },\n    }\n\n    lm = LoginManager(app)  # создание экземпляра логин-менеджера\n\n    sio = SocketIO(app, cors_allowed_origins='*')  # создание экземпляра socket.io\n\n    db.init_app(app)  # создание экземпляра базы данных\n    with app.test_request_context():\n        db.create_all()\n    # current_page = \"/\"\n\n    import app.modules.views as views\n    import app.modules.items as items\n    import app.modules.ORM as ORM\n    import app.modules.login as login\n    import app.modules.chat as chat\n\n    app.register_blueprint(views.module)\n    app.register_blueprint(items.module)\n    app.register_blueprint(ORM.module)\n    app.register_blueprint(login.module)\n    app.register_blueprint(chat.module)\n\n    #    app.run(host='192.168.3.2', port=5000, debug=True, threaded=False)\n    return app\n","repo_name":"All-C-6/Dproject","sub_path":"tourismex/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19697995098","text":"#!/usr/bin/env python3\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument('--logfile', default=\"computed/tmp.log\")\nargs = parser.parse_args()\n\nwith open(args.logfile, \"r\") as f:\n    DATA = eval(f.read())\nDIMS = list(range(768))\nBASELINE = [x for x in DATA if x[\"dim\"] == False][0]\nDATA = [x for x in DATA if x[\"dim\"] != False]\n# IMPR_IP = [x[\"dim\"] for x in sorted(DATA, key=lambda x: x[\"val_ip\"], reverse=True) if x[\"val_ip\"] > BASELINE[\"val_ip\"]]\n# IMPR_L2 = [x[\"dim\"] for x in sorted(DATA, key=lambda x: x[\"val_l2\"], reverse=True) if x[\"val_l2\"] > BASELINE[\"val_l2\"]]\nprint([x[\"dim\"] for x in sorted(DATA, key=lambda x: x[\"val_ip\"], reverse=True)])\nprint([x[\"dim\"] for x in sorted(DATA, key=lambda x: x[\"val_l2\"], reverse=True)])\nprint(\"improvement IP\", len([x for x in DATA if x[\"val_ip\"] >= BASELINE[\"val_ip\"]]))\nprint(\"improvement L2\", len([x for x in DATA if x[\"val_l2\"] >= BASELINE[\"val_l2\"]]))","repo_name":"zouharvi/kb-shrink","sub_path":"src/reduce_dim/figures/dimension_drop_single.py","file_name":"dimension_drop_single.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"42338065371","text":"import xml.dom.minidom\nimport logging\nimport os\n\n\nclass XMLObject:\n    \"\"\"\n    Class to easily parse XML documents\n    \"\"\"\n    tuples = {}\n    tuples_lists = {}\n    attributes = []\n    values = []\n    values_lists = []\n    numeric = []\n    noneval = None\n\n    def to_xml(self, node_name=None):\n        if node_name is None:\n            node_name = self.__class__.__name__\n        res = \"<\" + node_name\n\n        for tag in self.__class__.attributes:\n            if self.__dict__[tag] is not None and len(str(self.__dict__[tag])) > 0:\n                res += ' ' + tag + ' = \"' + self.__dict__[tag] + '\"'\n\n        res += \">\\n\"\n\n        for tag, _ in self.__class__.tuples.items():\n            if self.__dict__[tag] is not None:\n                res += self.__dict__[tag].to_xml(tag)\n\n        for tag, _ in self.__class__.tuples_lists.items():\n            if self.__dict__[tag] is not None:\n                obj_list = self.__dict__[tag]\n                for obj in obj_list:\n                    res += obj.to_xml(tag)\n\n        for tag in self.__class__.values_lists:\n            if self.__dict__[tag] is not None:\n                obj_list = self.__dict__[tag]\n                for value in obj_list:\n                    if value is not None and len(str(value)) > 0:\n                        res += \"<\" + tag + \">\" + value + \"</\" + tag + \">\\n\"\n\n        for tag in self.__class__.values:\n            if self.__dict__[tag] is not None and len(str(self.__dict__[tag])) > 0:\n                res += \"<\" + tag + \">\" + str(self.__dict__[tag]) + \"</\" + tag + \">\\n\"\n\n        res += \"</\" + node_name + \">\\n\"\n\n        return res\n\n    @staticmethod\n    def getChildByTagName(node, tagname):\n        objs = []\n        for e in node.childNodes:\n            if e.nodeType == e.ELEMENT_NODE and e.tagName == tagname:\n                objs.append(e)\n        return objs\n\n    @staticmethod\n    def getText(nodelist):\n        rc = []\n        for node in nodelist:\n            if node.nodeType == node.TEXT_NODE or node.nodeType == node.CDATA_SECTION_NODE:\n                rc.append(node.data)\n        return ''.join(rc)\n\n    @staticmethod\n    def handleField(fieldName, VM):\n        try:\n            fieldElements = VM.getElementsByTagName(fieldName)[0]\n            return XMLObject.getText(fieldElements.childNodes)\n        except Exception:\n            return None\n\n    @staticmethod\n    def handleFieldAsList(fieldName, VM):\n        try:\n            fieldElements = VM.getElementsByTagName(fieldName)\n            local_list = []\n            for fieldElement in fieldElements:\n                local_list.append(XMLObject.getText(fieldElement.childNodes))\n            return local_list\n        except Exception:\n            return []\n\n    def __setattr__(self, name, value):\n        self.__dict__[name] = value\n\n    def __init__(self, input_str):\n        if isinstance(input_str, xml.dom.minidom.Element):\n            dom = input_str\n        else:\n            if os.path.isfile(input_str):\n                f = open(input_str)\n                xml_str = \"\"\n                for line in f.readlines():\n                    xml_str += line\n            else:\n                xml_str = input_str\n\n            dom = xml.dom.minidom.parseString(xml_str).documentElement  # nosec\n\n        for tag, className in self.__class__.tuples.items():\n            objs = self.getChildByTagName(dom, tag)\n            if (len(objs) > 0):\n                newObj = className(objs[0])\n                try:\n                    dom.removeChild(objs[0])\n                except Exception:\n                    pass\n            else:\n                newObj = None\n            self.__setattr__(tag, newObj)\n\n        for tag, className in self.__class__.tuples_lists.items():\n            objs = self.getChildByTagName(dom, tag)\n            obj_list = []\n            for obj in objs:\n                newObj = className(obj)\n                dom.removeChild(obj)\n                obj_list.append(newObj)\n            self.__setattr__(tag, obj_list)\n\n        for tag in self.__class__.values_lists:\n            self.__setattr__(tag, XMLObject.handleFieldAsList(tag, dom))\n\n        for tag in self.__class__.values:\n            value = XMLObject.handleField(tag, dom)\n            if (value is None):\n                value = self.noneval\n            if (tag in self.__class__.numeric and value is not None):\n                try:\n                    value = float(value)\n                    if (value == int(value)):\n                        value = int(value)\n                except Exception:\n                    logging.error(\n                        \"Incorrect type for %s i must be numeric but it is %s\" % (tag, value))\n            self.__setattr__(tag, value)\n\n        for tag in self.__class__.attributes:\n            self.__setattr__(tag, dom.getAttribute(tag))\n","repo_name":"grycap/im","sub_path":"IM/xmlobject.py","file_name":"xmlobject.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"77"}
+{"seq_id":"38055744011","text":"import os\nimport csv\nimport copy\nfrom prettytable import PrettyTable\n\n# ====================================================\n# Bonus analysis: Tally votes by county and candidate\n# ====================================================\n\n# Input file has columns: Voter ID, County, Candidate\n# Each record represents one vote for the identified candidate\n# Set path to input file\ncsvpath = os.path.join(\"Resources\", \"election_data.csv\")\n\n# Open filehandle\nwith open(csvpath, 'r') as csvfile:\n    \n    # Convert filehandle to csv.reader object\n    csvreader = csv.reader(csvfile, delimiter=',')\n\n    # Skip header row\n    next(csvreader)\n\n    # Initialize target variables\n    vote_count2 = {}\n\n    # Read and process each row\n    for row in csvreader:\n\n        # Count votes for each county by candidate\n        if row[1] not in vote_count2.keys():           # Check if county is in the keys\n            vote_count2[row[1]] = {}                   # Add new county to keys\n        if row[2] not in vote_count2[row[1]].keys():   # Check if candidate is in subkeys for county\n            vote_count2[row[1]][row[2]] = 1            # Add new candidate to county subkeys and count first vote\n        else:                                          # County key and candidate subkey exist\n            vote_count2[row[1]][row[2]] += 1           # Tally vote\n\n# Create independent copy of dictionary\ntest_dict = copy.deepcopy(vote_count2)\n\n# Throw a wrench in the works. Delete some items so a few county-candidate \n#    combinations are undefined. This forces the analysis to deal with \n#    undefined combinations of county/candidte.\ndel test_dict['Trandee'][\"O'Tooley\"]\ndel test_dict['Trandee']['Li']\ndel test_dict['Queen']['Khan']\ndel test_dict['Bamoo']['Khan']\n\n# List of unique counties\ncounties = sorted(test_dict.keys())\n\n# Make list of unique candidates and tally total votes by county\ncandidate_set = set()\nvotes_bycounty = {}\n# Note that 'candidate_dict' is a dictionary, not a value\nfor county, candidate_dict in test_dict.items():\n    # Add only unique items to set 'candidates_set'\n    candidate_set.update(candidate_dict.keys())\n    # candidate_dict.values converted to list to allowing summing values\n    votes_bycounty[county] = sum(list(candidate_dict.values()))\n\n# Convert 'candidate_set' to sorted list\ncandidates = sorted(candidate_set)\n\n# Tally total votes for each candidate\n#    Initialize dictionary with count=0 for each candidate\nvotes_bycandidate = {key: 0 for key in candidates}\n# Note that 'candidate_dict' is a dictionary, not a value\nfor candidate_dict in test_dict.values():\n    for candidate in candidate_dict.keys():\n        votes_bycandidate[candidate] += candidate_dict[candidate]\n\n# Sum total votes in election\ntotal_votes = sum(votes_bycounty.values())\n\n# PrettyTable - \"A simple Python library for easily displaying tabular data \n#    in a visually appealing ASCII table format\"\nPTab = PrettyTable()\n\n# Set field names \nfields = ['County', 'Total Votes']\nfields.extend(candidates)\nPTab.field_names = fields\n\n# Add row for each county with results by candidate\nfor county in counties:\n    row = [f\"{county}\", f\"{votes_bycounty[county]:,}\"]\n    results = []\n    for candidate in candidates:\n        # use dict.get to trap cases where vote=0 for county-candidate \n        count = test_dict[county].get(candidate, 0)\n        percent = count / votes_bycounty[county]\n        results.append(f\"{count:,} ({percent:.1%})\")\n    row.extend(results)\n    PTab.add_row(row)\n\n# Add row with totals\nrow = ['Total', f\"{total_votes:,}\"]\nresults = []\nfor candidate in candidates:\n    count = votes_bycandidate[candidate]\n    percent = count / total_votes\n    results.append(f\"{count:,} ({percent:.1%})\")\nrow.extend(results)\nPTab.add_row(row)\n\n# Right align all fields, then left align 'County' column\nPTab.align = 'r'\nPTab.align['County'] = 'l'\n\n# Modify PrettyTable output string to separate Total row and show winner\n# ----------------------------------------------------------------------\ntext_table = PTab.get_string()\n# convert 'text_table' to list of lines\nrows = text_table.split(\"\\n\")\n# Extract a \"border\" row of dashes to use for separating sections of output\ndashrow = rows[0]\n# Insert 'dashrow' to separate Total row from previous results by county\nrows.insert(len(rows) - 2, dashrow)\n# Identify winner name and winner percent of total votes\nwinner_name = max(votes_bycandidate, key=votes_bycandidate.get)\nwinner_pcnt = f\"{(votes_bycandidate[winner_name] / total_votes):.1%}\"\n# Start building winner string for insertion\nwinner_str = f\"| The winner is {winner_name} with {winner_pcnt} of the total vote\"\n# Count spaces needed for padding\nspaces = len(dashrow) - len(winner_str) - 1\n# Finish building winner row\nwinner_str = winner_str + \" \"*spaces + \"|\"\n# Insert winner row and another 'dashrow\" at the end\nrows.extend([winner_str, dashrow])\n\n# Print output to console\nfor row in rows:\n    print(row)\n\n\n\n\n\n\n\n\n","repo_name":"Earthscott/python-challenge","sub_path":"PyPoll/bonus.py","file_name":"bonus.py","file_ext":"py","file_size_in_byte":4930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42492054421","text":"var('x')\n\ndef riemann_sum(f,interval,number_of_subdivisions,endpoint_rule):\n    a, b = map(QQ, interval)\n    t = sage.calculus.calculus.var('t')\n    func = fast_callable(f(x=t), RDF, vars=[t])\n    dx = ZZ(b-a)/ZZ(number_of_subdivisions)\n\n    xs = []\n    ys = []\n    for q in range(number_of_subdivisions):\n        if endpoint_rule == 'Left':\n            xs.append(q*dx + a)\n        elif endpoint_rule == 'Midpoint':\n            xs.append(q*dx + a + dx/2)\n        elif endpoint_rule == 'Right':\n            xs.append(q*dx + a + dx)\n        elif endpoint_rule == 'Upper':\n            x = find_local_maximum(func, q*dx + a, q*dx + dx + a)[1]\n            xs.append(x)\n        elif endpoint_rule == 'Lower':\n            x = find_local_minimum(func, q*dx + a, q*dx + dx + a)[1]\n            xs.append(x)\n    ys = [ func(x) for x in xs ]\n\n    rects = Graphics()\n    for q in range(number_of_subdivisions):\n        xm = q*dx + dx/2 + a\n        x = xs[q]\n        y = ys[q]\n        rects += polygon([(xm-dx/2,0), (xm-dx/2,y), (xm+dx/2,y), (xm+dx/2,0)], color=\"#90CAF9\", edgecolor=\"#2196F3\", thickness=2)\n        rects += point((x, y), marker='o', rgbcolor='#FFE082', markeredgecolor='#FFC107', size=40, zorder=3)\n    min_y = min(0, find_local_minimum(func,a,b)[0])\n    max_y = max(0, find_local_maximum(func,a,b)[0])\n\n    numerical_answer = integral_numerical(func,a,b,max_points = 200)[0]\n    estimated_answer = dx * sum([ ys[q] for q in range(number_of_subdivisions)])\n\n    show(LatexExpr(r'''\n        \\int_{a}^{b} f(x)\\mathrm{d}x = %s\\\\\\ \\sum_{i=1}^{ %s} {f(x_i) \\Delta x} = %s ''' % (numerical_answer, number_of_subdivisions, estimated_answer)))\n    \n    show(plot(func,(x,a,b), thickness=4, color='#1565C0') + rects, xmin = a, xmax = b, ymin = min_y, ymax = max_y)\n        \n@interact\ndef _(f = input_box(default = sin(x) + 2, type = SR),\n    interval=range_slider(0, 10, 1, default=(0, 4), label=\"Interval\"),\n    number_of_subdivisions = slider(1, 20, 1, default=4, label=\"Number of boxes\"),\n    endpoint_rule = selector(['Midpoint', 'Left', 'Right', 'Upper', 'Lower'], nrows=1, label=\"Endpoint rule\")):\n    \n    riemann_sum(f,interval,number_of_subdivisions,endpoint_rule)","repo_name":"TungstenHub/tngt-sage","sub_path":"scripts/riemann_sums.py","file_name":"riemann_sums.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"3382225166","text":"import pyautogui as pg\nimport time\ntime.sleep(3)\n# pg.drag(100, 0, duration=0.5, button='left')\n# pg.drag(0, 100, duration=0.5, button='left')\n# pg.drag(-100, 0, duration=0.5, button='left')\n# pg.drag(0, -100, duration=0.5, button='left')\n\npg.hotkey(\"win\", \"r\")\npg.write(\"mspaint\", interval=0.2)\npg.press(\"enter\")\ntime.sleep(5)\n\npg.moveTo(x=2119, y=278, duration=0.5)\n\ndistance = 200\nchange = 20\nwhile distance > 0:\n    pg.drag(distance, 0, duration=0.2, button='left')\n    distance = distance - change\n    pg.drag(0, distance, duration=0.2, button='left')\n    pg.drag(-1 * distance, 0, duration=0.2, button='left')\n    distance = distance - change\n    pg.drag(0, -1 * distance, duration=0.2, button='left')","repo_name":"iamdami/POSCO-AI-BigData-Acadamy","sub_path":"python/code/autoPainter.py","file_name":"autoPainter.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"12481136714","text":"#  This file is part of Lazylibrarian.\n#  Lazylibrarian is free software':'you can redistribute it and/or modify\n#  it under the terms of the GNU General Public License as published by\n#  the Free Software Foundation, either version 3 of the License, or\n#  (at your option) any later version.\n#  Lazylibrarian is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#  You should have received a copy of the GNU General Public License\n#  along with Lazylibrarian.  If not, see <http://www.gnu.org/licenses/>.\n\n\nimport os\nimport re\nimport time\n\n# noinspection PyUnresolvedReferences\nfrom lib.six.moves.urllib_parse import quote_plus, quote, urlencode\n\nimport lazylibrarian\nfrom lazylibrarian import logger, database\nfrom lazylibrarian.cache import fetchURL, gr_xml_request, gb_json_request\nfrom lazylibrarian.common import proxyList\nfrom lazylibrarian.formatter import safe_unicode, plural, cleanName, unaccented, formatAuthorName, \\\n    check_int, replace_all, check_year, getList\nfrom lib.fuzzywuzzy import fuzz\nfrom lib.six import PY2\n\ntry:\n    import urllib3\n    import requests\nexcept ImportError:\n    import lib.requests as requests\n\n\ndef setAllBookAuthors():\n    myDB = database.DBConnection()\n    myDB.action('drop table if exists bookauthors')\n    myDB.action('create table bookauthors (AuthorID TEXT, BookID TEXT, Role TEXT, UNIQUE (AuthorID, BookID, Role))')\n    books = myDB.select('SELECT AuthorID,BookID from books')\n    for item in books:\n        myDB.action('insert into bookauthors (AuthorID, BookID, Role) values (?, ?, ?)',\n                    (item['AuthorID'], item['BookID'], ''), suppress='UNIQUE')\n    totalauthors = 0\n    totalrefs = 0\n    books = myDB.select('select bookid,bookname,authorid from books where workpage is not null and workpage != \"\"')\n    for book in books:\n        newauthors, newrefs = setBookAuthors(book)\n        totalauthors += newauthors\n        totalrefs += newrefs\n    msg = \"Added %s new authors to database, %s new bookauthors\" % (totalauthors, totalrefs)\n    logger.debug(msg)\n    return totalauthors, totalrefs\n\n\ndef setBookAuthors(book):\n    myDB = database.DBConnection()\n    newauthors = 0\n    newrefs = 0\n    try:\n        authorlist = getBookAuthors(book['bookid'])\n        for author in authorlist:\n            role = ''\n            if 'id' in author:\n                # it's a goodreads data source\n                authorname = author['name']\n                exists = myDB.match('select authorid from authors where authorid=?', (author['id'],))\n                if 'role' in author:\n                    role = author['role']\n            else:\n                # it's a librarything data source\n                authorname = formatAuthorName(author['name'])\n                exists = myDB.match('select authorid from authors where authorname=?', (authorname,))\n                if 'type' in author:\n                    authtype = author['type']\n                    if authtype in ['primary author', 'main author', 'secondary author']:\n                        role = authtype\n                    elif author['role'] in ['Author', '&mdash;'] and author['work'] == 'all editions':\n                        role = 'Author'\n            if exists:\n                authorid = exists['authorid']\n            else:\n                # try to add new author to database by name\n                authorname, authorid, new = lazylibrarian.importer.addAuthorNameToDB(authorname, False, False)\n                if new and authorid:\n                    newauthors += 1\n            if authorid:\n                myDB.action('INSERT into bookauthors (AuthorID, BookID, Role) VALUES (?, ?, ?)',\n                            (authorid, book['bookid'], role), suppress='UNIQUE')\n                newrefs += 1\n    except Exception as e:\n        logger.error(\"Error parsing authorlist for %s: %s %s\" % (book['bookname'], type(e).__name__, str(e)))\n    return newauthors, newrefs\n\n\ndef setAllBookSeries():\n    \"\"\" Try to set series details for all books \"\"\"\n    myDB = database.DBConnection()\n    books = myDB.select('select BookID,WorkID,BookName from books where Manual is not \"1\"')\n    counter = 0\n    if books:\n        logger.info('Checking series for %s book%s' % (len(books), plural(len(books))))\n        for book in books:\n            if lazylibrarian.CONFIG['BOOK_API'] == 'GoodReads':\n                workid = book['WorkID']\n                if not workid:\n                    logger.debug(\"No workid for book %s: %s\" % (book['BookID'], book['BookName']))\n            else:\n                workid = book['BookID']\n                if not workid:\n                    logger.debug(\"No bookid for book: %s\" % book['BookName'])\n            if workid:\n                serieslist = getWorkSeries(workid)\n                if serieslist:\n                    counter += 1\n                    setSeries(serieslist, book['BookID'])\n    deleteEmptySeries()\n    msg = 'Updated %s book%s' % (counter, plural(counter))\n    logger.info('Series check complete: ' + msg)\n    return msg\n\n\ndef setSeries(serieslist=None, bookid=None, authorid=None, workid=None):\n    \"\"\" set series details in series/member tables from the supplied dict\n        and a displayable summary in book table\n        serieslist is a tuple (SeriesID, SeriesNum, SeriesName)\n        Return how many api hits and the original publication date if known \"\"\"\n    myDB = database.DBConnection()\n    api_hits = 0\n    originalpubdate = ''\n    if bookid:\n        # delete any old series-member entries\n        myDB.action('DELETE from member WHERE BookID=?', (bookid,))\n        for item in serieslist:\n            match = myDB.match('SELECT SeriesID from series where SeriesName=? COLLATE NOCASE', (item[2],))\n            if match:\n                seriesid = match['SeriesID']\n                members, _api_hits = getSeriesMembers(seriesid, item[2])\n                api_hits += _api_hits\n            else:\n                # new series, need to set status and get SeriesID\n                if item[0]:\n                    seriesid = item[0]\n                    members, _api_hits = getSeriesMembers(seriesid, item[2])\n                    api_hits += _api_hits\n                else:\n                    # no seriesid so generate it (row count + 1)\n                    cnt = myDB.match(\"select count(*) as counter from series\")\n                    res = check_int(cnt['counter'], 0)\n                    seriesid = str(res + 1)\n                    members = []\n                myDB.action('INSERT into series VALUES (?, ?, ?, ?, ?)',\n                            (seriesid, item[2], \"Active\", 0, 0), suppress='UNIQUE')\n\n            if not workid or not authorid:\n                book = myDB.match('SELECT AuthorID,WorkID from books where BookID=?', (bookid,))\n                if book:\n                    authorid = book['AuthorID']\n                    workid = book['WorkID']\n            if seriesid and authorid and workid:\n                for member in members:\n                    if member[3] == workid:\n                        if check_year(member[5], past=1800, future=0):\n                            controlValueDict = {\"BookID\": bookid}\n                            newValueDict = {\"BookDate\": member[5], \"OriginalPubDate\": member[5]}\n                            myDB.upsert(\"books\", newValueDict, controlValueDict)\n                            originalpubdate = member[5]\n                        break\n\n                controlValueDict = {\"BookID\": bookid, \"SeriesID\": seriesid}\n                newValueDict = {\"SeriesNum\": item[1], \"WorkID\": workid}\n                myDB.upsert(\"member\", newValueDict, controlValueDict)\n                myDB.action('INSERT INTO seriesauthors (\"SeriesID\", \"AuthorID\") VALUES (?, ?)',\n                            (seriesid, authorid), suppress='UNIQUE')\n            else:\n                if not authorid:\n                    logger.debug('Unable to set series for book %s, no authorid' % bookid)\n                elif not workid:\n                    logger.debug('Unable to set series for book %s, no workid' % bookid)\n                elif not seriesid:\n                    logger.debug('Unable to set series for book %s, no seriesid' % bookid)\n                return api_hits, originalpubdate\n\n        series = ''\n        for item in serieslist:\n            newseries = \"%s %s\" % (item[2], item[1])\n            newseries.strip()\n            if series and newseries:\n                series += '<br>'\n            series += newseries\n        myDB.action('UPDATE books SET SeriesDisplay=? WHERE BookID=?', (series, bookid))\n        return api_hits, originalpubdate\n\n\ndef setStatus(bookid=None, serieslist=None, default=None):\n    \"\"\" Set the status of a book according to series/author/newbook/newauthor preferences\n        return default if unchanged, default is passed in as newbook or newauthor status \"\"\"\n    myDB = database.DBConnection()\n    if not bookid:\n        return default\n\n    match = myDB.match('SELECT Status,AuthorID,BookName from books WHERE BookID=?', (bookid,))\n    if not match:\n        return default\n\n    # Don't update status if we already have the book but allow status change if ignored\n    # might be we had ignore author set, but want to allow this series\n    current_status = match['Status']\n    if current_status in ['Have', 'Open']:\n        return current_status\n\n    new_status = ''\n    authorid = match['AuthorID']\n    bookname = match['BookName']\n    # Is the book part of any series we want or don't want?\n    for item in serieslist:\n        match = myDB.match('SELECT Status from series where SeriesName=? COLLATE NOCASE', (item[2],))\n        if match:\n            if match['Status'] == 'Wanted':\n                new_status = 'Wanted'\n                logger.debug('Marking %s as %s, series %s' % (bookname, new_status, item[2]))\n                break\n            if match['Status'] == 'Skipped':\n                new_status = 'Skipped'\n                logger.debug('Marking %s as %s, series %s' % (bookname, new_status, item[2]))\n                break\n\n    if not new_status:\n        # Author we want or don't want?\n        match = myDB.match('SELECT Status from authors where AuthorID=?', (authorid,))\n        if match:\n            if match['Status'] in ['Paused', 'Ignored']:\n                new_status = 'Skipped'\n                logger.debug('Marking %s as %s, author %s' % (bookname, new_status, match['Status']))\n            if match['Status'] == 'Wanted':\n                new_status = 'Wanted'\n                logger.debug('Marking %s as %s, author %s' % (bookname, new_status, match['Status']))\n\n    # If none of these, leave default \"newbook\" or \"newauthor\" status\n    if new_status:\n        myDB.action('UPDATE books SET Status=? WHERE BookID=?', (new_status, bookid))\n        return new_status\n\n    return default\n\n\ndef deleteEmptySeries():\n    \"\"\" remove any series from series table that have no entries in member table, return how many deleted \"\"\"\n    myDB = database.DBConnection()\n    series = myDB.select('SELECT SeriesID,SeriesName from series')\n    count = 0\n    for item in series:\n        match = myDB.match('SELECT BookID from member where SeriesID=?', (item['SeriesID'],))\n        if not match:\n            logger.debug('Deleting empty series %s' % item['SeriesName'])\n            count += 1\n            myDB.action('DELETE from series where SeriesID=?', (item['SeriesID'],))\n    return count\n\n\ndef setWorkPages():\n    \"\"\" Set the workpage link for any books that don't already have one \"\"\"\n\n    myDB = database.DBConnection()\n    cmd = 'select BookID,AuthorName,BookName from books,authors where length(WorkPage) < 4'\n    cmd += ' and books.AuthorID = authors.AuthorID'\n    books = myDB.select(cmd)\n    if books:\n        logger.debug('Setting WorkPage for %s book%s' % (len(books), plural(len(books))))\n        counter = 0\n        for book in books:\n            bookid = book['BookID']\n            worklink = getWorkPage(bookid)\n            if worklink:\n                controlValueDict = {\"BookID\": bookid}\n                newValueDict = {\"WorkPage\": worklink}\n                myDB.upsert(\"books\", newValueDict, controlValueDict)\n                counter += 1\n            else:\n                logger.debug('No WorkPage found for %s: %s' % (book['AuthorName'], book['BookName']))\n        msg = 'Updated %s page%s' % (counter, plural(counter))\n        logger.debug(\"setWorkPages complete: \" + msg)\n    else:\n        msg = 'No missing WorkPages'\n        logger.debug(msg)\n    return msg\n\n\ndef setWorkID(books=None):\n    \"\"\" Set the goodreads workid for any books that don't already have one\n        books is a comma separated list of bookids or if empty, select from database\n        Paginate requests to reduce api hits \"\"\"\n\n    myDB = database.DBConnection()\n    pages = []\n    if books:\n        page = books\n        pages.append(page)\n    else:\n        cmd = \"select BookID,BookName from books where WorkID='' or WorkID is null\"\n        books = myDB.select(cmd)\n        if books:\n            counter = 0\n            logger.debug('Setting WorkID for %s book%s' % (len(books), plural(len(books))))\n            page = ''\n            for book in books:\n                bookid = book['BookID']\n                if not bookid:\n                    logger.debug(\"No bookid for %s\" % book['BookName'])\n                else:\n                    if page:\n                        page = page + ','\n                    page = page + bookid\n                    counter += 1\n                    if counter == 50:\n                        counter = 0\n                        pages.append(page)\n                        page = ''\n            if page:\n                pages.append(page)\n\n    counter = 0\n    params = {\"key\": lazylibrarian.CONFIG['GR_API']}\n    for page in pages:\n        URL = 'https://www.goodreads.com/book/id_to_work_id/' + page + '?' + urlencode(params)\n        try:\n            rootxml, in_cache = gr_xml_request(URL, useCache=False)\n            if rootxml is None:\n                logger.debug(\"Error requesting id_to_work_id page\")\n            else:\n                resultxml = rootxml.find('work-ids')\n                if len(resultxml):\n                    ids = resultxml.getiterator('item')\n                    books = getList(page)\n                    cnt = 0\n                    for item in ids:\n                        workid = item.text\n                        if not workid:\n                            logger.debug(\"No workid returned for %s\" % books[cnt])\n                        else:\n                            counter += 1\n                            controlValueDict = {\"BookID\": books[cnt]}\n                            newValueDict = {\"WorkID\": workid}\n                            myDB.upsert(\"books\", newValueDict, controlValueDict)\n                        cnt += 1\n\n        except Exception as e:\n            logger.error(\"%s parsing id_to_work_id page: %s\" % (type(e).__name__, str(e)))\n\n    msg = 'Updated %s id%s' % (counter, plural(counter))\n    logger.debug(\"setWorkID complete: \" + msg)\n    return msg\n\n\ndef librarything_wait():\n    \"\"\" Wait for a second between librarything api calls \"\"\"\n    time_now = time.time()\n    delay = time_now - lazylibrarian.LAST_LIBRARYTHING\n    if delay < 1.0:\n        sleep_time = 1.0 - delay\n        lazylibrarian.LT_SLEEP += sleep_time\n        logger.debug(\"LibraryThing sleep %.3f, total %.3f\" % (sleep_time, lazylibrarian.LT_SLEEP))\n        time.sleep(sleep_time)\n    lazylibrarian.LAST_LIBRARYTHING = time_now\n\n\n# Feb 2018 librarything have disabled \"whatwork\"\n# might only be temporary, but for now disable looking for new workpages\n# and do not expire cached ones\nALLOW_NEW = False\nLAST_NEW = 0\n\n\ndef getBookWork(bookID=None, reason=None, seriesID=None):\n    \"\"\" return the contents of the LibraryThing workpage for the given bookid, or seriespage if seriesID given\n        preferably from the cache. If not already cached cache the results\n        Return None if no workpage/seriespage available \"\"\"\n    global ALLOW_NEW, LAST_NEW\n    if not bookID and not seriesID:\n        logger.error(\"getBookWork - No bookID or seriesID\")\n        return None\n\n    if not reason:\n        reason = \"\"\n\n    myDB = database.DBConnection()\n    if bookID:\n        cmd = 'select BookName,AuthorName,BookISBN from books,authors where bookID=?'\n        cmd += ' and books.AuthorID = authors.AuthorID'\n        cacheLocation = \"WorkCache\"\n        item = myDB.match(cmd, (bookID,))\n    else:\n        cmd = 'select SeriesName from series where SeriesID=?'\n        cacheLocation = \"SeriesCache\"\n        item = myDB.match(cmd, (seriesID,))\n    if item:\n        cacheLocation = os.path.join(lazylibrarian.CACHEDIR, cacheLocation)\n        if bookID:\n            workfile = os.path.join(cacheLocation, str(bookID) + '.html')\n        else:\n            workfile = os.path.join(cacheLocation, str(seriesID) + '.html')\n\n        # does the workpage need to expire? For now only expire if it was an error page\n        # (small file) or a series page as librarything might get better info over time, more series members etc\n        if os.path.isfile(workfile):\n            if seriesID or os.path.getsize(workfile) < 500:\n                cache_modified_time = os.stat(workfile).st_mtime\n                time_now = time.time()\n                expiry = lazylibrarian.CONFIG['CACHE_AGE'] * 24 * 60 * 60  # expire cache after this many seconds\n                if cache_modified_time < time_now - expiry:\n                    # Cache entry is too old, delete it\n                    if ALLOW_NEW:\n                        os.remove(workfile)\n\n        if os.path.isfile(workfile):\n            # use cached file if possible to speed up refreshactiveauthors and librarysync re-runs\n            lazylibrarian.CACHE_HIT = int(lazylibrarian.CACHE_HIT) + 1\n            if bookID:\n                if reason:\n                    logger.debug(\"getBookWork: Returning Cached entry for %s %s\" % (bookID, reason))\n                else:\n                    logger.debug(\"getBookWork: Returning Cached workpage for %s\" % bookID)\n            else:\n                logger.debug(\"getBookWork: Returning Cached seriespage for %s\" % item['seriesName'])\n\n            if PY2:\n                with open(workfile, \"r\") as cachefile:\n                    source = cachefile.read()\n            else:\n                # noinspection PyArgumentList\n                with open(workfile, \"r\", errors=\"backslashreplace\") as cachefile:\n                    source = cachefile.read()\n            return source\n        else:\n            lazylibrarian.CACHE_MISS = int(lazylibrarian.CACHE_MISS) + 1\n            if not ALLOW_NEW:\n                # don't nag. Show message no more than every 12 hrs\n                timenow = int(time.time())\n                if check_int(LAST_NEW, 0) + 43200 < timenow:\n                    logger.warn(\"New WhatWork is disabled\")\n                    LAST_NEW = timenow\n                return None\n            if bookID:\n                title = safe_unicode(item['BookName'])\n                author = safe_unicode(item['AuthorName'])\n                if PY2:\n                    title = title.encode(lazylibrarian.SYS_ENCODING)\n                    author = author.encode(lazylibrarian.SYS_ENCODING)\n                URL = 'http://www.librarything.com/api/whatwork.php?author=%s&title=%s' % \\\n                      (quote_plus(author), quote_plus(title))\n            else:\n                seriesname = safe_unicode(item['seriesName'])\n                if PY2:\n                    seriesname = seriesname.encode(lazylibrarian.SYS_ENCODING)\n                URL = 'http://www.librarything.com/series/%s' % quote_plus(seriesname)\n\n            librarything_wait()\n            result, success = fetchURL(URL)\n            if bookID and success:\n                # noinspection PyBroadException\n                try:\n                    workpage = result.split('<link>')[1].split('</link>')[0]\n                    librarything_wait()\n                    result, success = fetchURL(workpage)\n                except Exception:\n                    try:\n                        errmsg = result.split('<error>')[1].split('</error>')[0]\n                    except IndexError:\n                        errmsg = \"Unknown Error\"\n                    # if no workpage link, try isbn instead\n                    if item['BookISBN']:\n                        URL = 'http://www.librarything.com/api/whatwork.php?isbn=' + item['BookISBN']\n                        librarything_wait()\n                        result, success = fetchURL(URL)\n                        if success:\n                            # noinspection PyBroadException\n                            try:\n                                workpage = result.split('<link>')[1].split('</link>')[0]\n                                librarything_wait()\n                                result, success = fetchURL(workpage)\n                            except Exception:\n                                # no workpage link found by isbn\n                                try:\n                                    errmsg = result.split('<error>')[1].split('</error>')[0]\n                                except IndexError:\n                                    errmsg = \"Unknown Error\"\n                                # still cache if whatwork returned a result without a link, so we don't keep retrying\n                                logger.debug(\"Librarything: [%s] for ISBN %s\" % (errmsg, item['BookISBN']))\n                                success = True\n                    else:\n                        # still cache if whatwork returned a result without a link, so we don't keep retrying\n                        msg = \"Librarything: [\" + errmsg + \"] for \"\n                        logger.debug(msg + item['AuthorName'] + ' ' + item['BookName'])\n                        success = True\n            if success:\n                with open(workfile, \"w\") as cachefile:\n                    cachefile.write(result)\n                    if bookID:\n                        logger.debug(\"getBookWork: Caching workpage for %s\" % workfile)\n                    else:\n                        logger.debug(\"getBookWork: Caching series page for %s\" % workfile)\n                    # return None if we got an error page back\n                    if '</request><error>' in result:\n                        return None\n                return result\n            else:\n                if bookID:\n                    logger.debug(\"getBookWork: Unable to cache workpage, got %s\" % result)\n                else:\n                    logger.debug(\"getBookWork: Unable to cache series page, got %s\" % result)\n            return None\n    else:\n        if bookID:\n            logger.debug('Get Book Work - Invalid bookID [%s]' % bookID)\n        else:\n            logger.debug('Get Book Work - Invalid seriesID [%s]' % seriesID)\n        return None\n\n\ndef getWorkPage(bookID=None):\n    \"\"\" return the URL of the LibraryThing workpage for the given bookid\n        or an empty string if no workpage available \"\"\"\n    if not bookID:\n        logger.error(\"getWorkPage - No bookID\")\n        return ''\n    work = getBookWork(bookID, \"Workpage\")\n    if work:\n        try:\n            page = work.split('og:url')[1].split('=\"')[1].split('\"')[0]\n        except IndexError:\n            return ''\n        return page\n    return ''\n\n\ndef getAllSeriesAuthors():\n    \"\"\" For each entry in the series table, get a list of authors contributing to the series\n        and import those authors (and their books) into the database \"\"\"\n    myDB = database.DBConnection()\n    series = myDB.select('select SeriesID from series')\n    if series:\n        logger.debug('Getting series authors for %s series' % len(series))\n        counter = 0\n        total = 0\n        for entry in series:\n            seriesid = entry['SeriesID']\n            result = getSeriesAuthors(seriesid)\n            if result:\n                counter += 1\n                total += result\n            else:\n                logger.debug('No series info found for series %s' % seriesid)\n        msg = 'Updated authors for %s series, added %s new author%s' % (counter, total, plural(total))\n        logger.debug(\"Series pages complete: \" + msg)\n    else:\n        msg = 'No entries in the series table'\n        logger.debug(msg)\n    return msg\n\n\ndef getBookAuthors(bookid):\n    \"\"\" Get a list of authors contributing to a book from the goodreads bookpage or the librarything bookwork file \"\"\"\n    authorlist = []\n    if lazylibrarian.CONFIG['BOOK_API'] == 'GoodReads':\n        params = {\"key\": lazylibrarian.CONFIG['GR_API']}\n        URL = 'https://www.goodreads.com/book/show/' + bookid + '?' + urlencode(params)\n        try:\n            rootxml, in_cache = gr_xml_request(URL)\n            if rootxml is None:\n                logger.debug(\"Error requesting book %s\" % bookid)\n                return []\n        except Exception as e:\n            logger.error(\"%s finding book %s: %s\" % (type(e).__name__, bookid, str(e)))\n            return []\n\n        book = rootxml.find('book')\n        authors = book.find('authors')\n        anames = authors.getiterator('author')\n        if anames is None:\n            logger.warn('No authors found for %s' % bookid)\n            return []\n        for aname in anames:\n            author = {}\n            if aname.find('id') is not None:\n                author['id'] = aname.find('id').text\n            if aname.find('name') is not None:\n                author['name'] = aname.find('name').text\n            if aname.find('role') is not None:\n                role = aname.find('role').text\n                if not role:\n                    role = ''\n                author['role'] = role\n            if author:\n                authorlist.append(author)\n    else:\n        data = getBookWork(bookid, \"Authors\")\n        if data:\n            try:\n                data = data.split('otherauthors_container')[1].split('</table>')[0].split('<table')[1].split('>', 1)[1]\n            except IndexError:\n                data = ''\n\n        authorlist = []\n        if data and 'Work?' in data:\n            try:\n                rows = data.split('<tr')\n                for row in rows[2:]:\n                    author = {}\n                    col = row.split('<td>')\n                    author['name'] = col[1].split('\">')[1].split('<')[0]\n                    author['role'] = col[2].split('<')[0]\n                    author['type'] = col[3].split('<')[0]\n                    author['work'] = col[4].split('<')[0]\n                    author['status'] = col[5].split('<')[0]\n                    authorlist.append(author)\n            except IndexError:\n                logger.debug('Error parsing authorlist for %s' % bookid)\n    return authorlist\n\n\ndef getSeriesAuthors(seriesid):\n    \"\"\" Get a list of authors contributing to a series\n        and import those authors (and their books) into the database\n        Return how many authors you added \"\"\"\n    myDB = database.DBConnection()\n    result = myDB.match(\"select count(*) as counter from authors\")\n    start = int(result['counter'])\n    result = myDB.match('select SeriesName from series where SeriesID=?', (seriesid,))\n    seriesname = result['SeriesName']\n    members, api_hits = getSeriesMembers(seriesid, seriesname)\n    dic = {u'\\u2018': \"\", u'\\u2019': \"\", u'\\u201c': '', u'\\u201d': '', \"'\": \"\", '\"': ''}\n\n    if members:\n        myDB = database.DBConnection()\n        for member in members:\n            # order = member[0]\n            bookname = member[1]\n            authorname = member[2]\n            # workid = member[3]\n            authorid = member[4]\n            # pubyear = member[5]\n            bookname = replace_all(bookname, dic)\n            if not authorid:\n                # goodreads gives us all the info we need, librarything/google doesn't\n                base_url = 'https://www.goodreads.com/search.xml?q='\n                params = {\"key\": lazylibrarian.CONFIG['GR_API']}\n                searchname = bookname + ' ' + authorname\n                searchname = cleanName(unaccented(searchname))\n                if PY2:\n                    searchname = searchname.encode(lazylibrarian.SYS_ENCODING)\n                searchterm = quote_plus(searchname)\n                set_url = base_url + searchterm + '&' + urlencode(params)\n                try:\n                    rootxml, in_cache = gr_xml_request(set_url)\n                    if not in_cache:\n                        api_hits += 1\n                    if rootxml is None:\n                        logger.warn('Error getting XML for %s' % searchname)\n                    else:\n                        resultxml = rootxml.getiterator('work')\n                        for item in resultxml:\n                            try:\n                                booktitle = item.find('./best_book/title').text\n                                booktitle = replace_all(booktitle, dic)\n                            except (KeyError, AttributeError):\n                                booktitle = \"\"\n                            book_fuzz = fuzz.token_set_ratio(booktitle, bookname)\n                            if book_fuzz >= 98:\n                                try:\n                                    author = item.find('./best_book/author/name').text\n                                except (KeyError, AttributeError):\n                                    author = \"\"\n                                # try:\n                                #     workid = item.find('./work/id').text\n                                # except (KeyError, AttributeError):\n                                #     workid = \"\"\n                                try:\n                                    authorid = item.find('./best_book/author/id').text\n                                except (KeyError, AttributeError):\n                                    authorid = \"\"\n                                logger.debug(\"Author Search found %s %s, authorid %s\" %\n                                             (author, booktitle, authorid))\n                                break\n                    if not authorid:  # try again with title only\n                        searchname = cleanName(unaccented(bookname))\n                        if PY2:\n                            searchname = searchname.encode(lazylibrarian.SYS_ENCODING)\n                        searchterm = quote_plus(searchname)\n                        set_url = base_url + searchterm + '&' + urlencode(params)\n                        rootxml, in_cache = gr_xml_request(set_url)\n                        if not in_cache:\n                            api_hits += 1\n                        if rootxml is None:\n                            logger.warn('Error getting XML for %s' % searchname)\n                        else:\n                            resultxml = rootxml.getiterator('work')\n                            for item in resultxml:\n                                booktitle = item.find('./best_book/title').text\n                                booktitle = replace_all(booktitle, dic)\n                                book_fuzz = fuzz.token_set_ratio(booktitle, bookname)\n                                if book_fuzz >= 98:\n                                    try:\n                                        author = item.find('./best_book/author/name').text\n                                    except (KeyError, AttributeError):\n                                        author = \"\"\n                                    # try:\n                                    #     workid = item.find('./work/id').text\n                                    # except (KeyError, AttributeError):\n                                    #     workid = \"\"\n                                    try:\n                                        authorid = item.find('./best_book/author/id').text\n                                    except (KeyError, AttributeError):\n                                        authorid = \"\"\n                                    logger.debug(\"Title Search found %s %s, authorid %s\" %\n                                                 (author, booktitle, authorid))\n                                    break\n                    if not authorid:\n                        logger.warn(\"GoodReads doesn't know about %s %s\" % (authorname, bookname))\n                except Exception as e:\n                    logger.error(\"Error finding goodreads results: %s %s\" % (type(e).__name__, str(e)))\n\n            if authorid:\n                lazylibrarian.importer.addAuthorToDB(refresh=False, authorid=authorid)\n\n    result = myDB.match(\"select count(*) as counter from authors\")\n    finish = int(result['counter'])\n    newauth = finish - start\n    logger.info(\"Added %s new author%s for %s\" % (newauth, plural(newauth), seriesname))\n    return newauth\n\n\ndef getSeriesMembers(seriesID=None, seriesname=None):\n    \"\"\" Ask librarything or goodreads for details on all books in a series\n        order, bookname, authorname, workid, authorid\n        (workid and authorid are goodreads only)\n        Return as a list of lists \"\"\"\n    results = []\n    api_hits = 0\n    if lazylibrarian.CONFIG['BOOK_API'] == 'GoodReads':\n        params = {\"format\": \"xml\", \"key\": lazylibrarian.CONFIG['GR_API']}\n        URL = 'https://www.goodreads.com/series/%s?%s' % (seriesID, urlencode(params))\n        try:\n            rootxml, in_cache = gr_xml_request(URL)\n            if not in_cache:\n                api_hits += 1\n            if rootxml is None:\n                logger.debug(\"Series %s:%s not recognised at goodreads\" % (seriesID, seriesname))\n                return [], api_hits\n        except Exception as e:\n            logger.error(\"%s finding series %s: %s\" % (type(e).__name__, seriesID, str(e)))\n            return [], api_hits\n\n        works = rootxml.find('series/series_works')\n        books = works.getiterator('series_work')\n        if books is None:\n            logger.warn('No books found for %s' % seriesID)\n            return [], api_hits\n        for book in books:\n            mydict = {}\n            for mykey, location in [('order', 'user_position'),\n                                    ('bookname', 'work/best_book/title'),\n                                    ('authorname', 'work/best_book/author/name'),\n                                    ('workid', 'work/id'),\n                                    ('authorid', 'work/best_book/author/id'),\n                                    ('pubyear', 'work/original_publication_year')\n                                    ]:\n                if book.find(location) is not None:\n                    mydict[mykey] = book.find(location).text\n                else:\n                    mydict[mykey] = \"\"\n            results.append([mydict['order'], mydict['bookname'], mydict['authorname'],\n                            mydict['workid'], mydict['authorid'], mydict['pubyear']])\n    else:\n        api_hits = 0\n        data = getBookWork(None, \"SeriesPage\", seriesID)\n        if data:\n            try:\n                table = data.split('class=\"worksinseries\"')[1].split('</table>')[0]\n                rows = table.split('<tr')\n                for row in rows:\n                    if 'href=' in row:\n                        booklink = row.split('href=\"')[1]\n                        bookname = booklink.split('\">')[1].split('<')[0]\n                        # booklink = booklink.split('\"')[0]\n                        try:\n                            authorlink = row.split('href=\"')[2]\n                            authorname = authorlink.split('\">')[1].split('<')[0]\n                            # authorlink = authorlink.split('\"')[0]\n                            order = row.split('class=\"order\">')[1].split('<')[0]\n                            results.append([order, bookname, authorname, '', ''])\n                        except IndexError:\n                            logger.debug('Incomplete data in series table for series %s' % seriesID)\n            except IndexError:\n                if 'class=\"worksinseries\"' in data:  # error parsing, or just no series data available?\n                    logger.debug('Error in series table for series %s' % seriesID)\n    return results, api_hits\n\n\ndef get_book_desc(isbn=None, author=None, title=None):\n    \"\"\" GoodReads does not always have a book description in its api results\n        due to restrictive TOS from some of its providers.\n        Try to get missing descriptions from googlebooks\n        Return description, empty string if not found, None if error\"\"\"\n    if not author or not title:\n        return ''\n\n    author = cleanName(author)\n    title = cleanName(title)\n    if lazylibrarian.CONFIG['BOOK_API'] == 'GoodReads':\n        baseurl = 'https://www.googleapis.com/books/v1/volumes?q='\n\n        urls = [baseurl + quote_plus('inauthor:%s intitle:%s' % (author, title))]\n        if isbn:\n            urls.insert(0, baseurl + quote_plus('isbn:' + isbn))\n\n        for url in urls:\n            if lazylibrarian.CONFIG['GB_API']:\n                url += '&key=' + lazylibrarian.CONFIG['GB_API']\n            if lazylibrarian.CONFIG['GB_COUNTRY'] and len(lazylibrarian.CONFIG['GB_COUNTRY'] == 2):\n                url += '&country=' + lazylibrarian.CONFIG['GB_COUNTRY']\n            results, cached = gb_json_request(url)\n            if results is None:  # there was an error\n                return None\n            if results and not cached:\n                time.sleep(1)\n            if results and 'items' in results:\n                for item in results['items']:\n                    # noinspection PyBroadException\n                    try:\n                        auth = item['volumeInfo']['authors'][0]\n                        book = item['volumeInfo']['title']\n                        desc = item['volumeInfo']['description']\n                        book_fuzz = fuzz.token_set_ratio(book, title)\n                        auth_fuzz = fuzz.token_set_ratio(auth, author)\n                        if book_fuzz > 98 and auth_fuzz > 80:\n                            return desc\n                    except Exception:\n                        pass\n    return ''\n\n\ndef getWorkSeries(bookID=None):\n    \"\"\" Return the series names and numbers in series for the given id as a list of tuples\n        For goodreads the id is a WorkID, for librarything it's a BookID \"\"\"\n    myDB = database.DBConnection()\n    serieslist = []\n    if not bookID:\n        logger.error(\"getWorkSeries - No bookID\")\n        return serieslist\n\n    if lazylibrarian.CONFIG['BOOK_API'] == 'GoodReads':\n        URL = \"https://www.goodreads.com/work/\"\n        seriesurl = URL + bookID + \"/series?format=xml&key=\" + lazylibrarian.CONFIG['GR_API']\n\n        rootxml, in_cache = gr_xml_request(seriesurl)\n        if rootxml is None:\n            logger.warn('Error getting XML for %s' % seriesurl)\n        else:\n            resultxml = rootxml.getiterator('series_work')\n            for item in resultxml:\n                try:\n                    seriesname = item.find('./series/title').text\n                    seriesname = seriesname.strip('\\n').strip('\\n').strip()\n                    seriesid = item.find('./series/id').text\n                    seriesnum = item.find('./user_position').text\n                except (KeyError, AttributeError):\n                    continue\n                if seriesname and seriesid:\n                    seriesname = cleanName(unaccented(seriesname), '&/')\n                    if seriesname:\n                        seriesnum = cleanName(unaccented(seriesnum))\n                        serieslist.append((seriesid, seriesnum, seriesname))\n                        match = myDB.match('SELECT SeriesID from series WHERE SeriesName=?', (seriesname,))\n                        if not match:\n                            match = myDB.match('SELECT SeriesName from series WHERE SeriesID=?', (seriesid,))\n                            if not match:\n                                myDB.action('INSERT INTO series VALUES (?, ?, ?, ?, ?)',\n                                            (seriesid, seriesname, \"Active\", 0, 0))\n                            else:\n                                logger.warn(\"Name mismatch for series %s, [%s][%s]\" % (\n                                            seriesid, seriesname, match['SeriesName']))\n                        elif match['SeriesID'] != seriesid:\n                            myDB.action('UPDATE series SET SeriesID=? WHERE SeriesName=?', (seriesid, seriesname))\n    else:\n        work = getBookWork(bookID, \"Series\")\n        if work:\n            try:\n                slist = work.split('<h3><b>Series:')[1].split('</h3>')[0].split('<a href=\"/series/')\n                for item in slist[1:]:\n                    try:\n                        series = item.split('\">')[1].split('</a>')[0]\n                        if series and '(' in series:\n                            seriesnum = series.split('(')[1].split(')')[0].strip()\n                            series = series.split(' (')[0].strip()\n                        else:\n                            seriesnum = ''\n                            series = series.strip()\n                        seriesname = cleanName(unaccented(series), '&/')\n                        seriesnum = cleanName(unaccented(seriesnum))\n                        if seriesname:\n                            serieslist.append(('', seriesnum, seriesname))\n                    except IndexError:\n                        pass\n            except IndexError:\n                pass\n\n    return serieslist\n\n\ndef get_book_pubdate(bookid, refresh=False):\n    URL = 'https://www.goodreads.com/book/show/' + bookid + '.xml?key=' + lazylibrarian.CONFIG['GR_API']\n    bookdate = \"0000\"\n    try:\n        rootxml, in_cache = gr_xml_request(URL, useCache=not refresh)\n    except Exception as e:\n        logger.error(\"%s fetching book publication date: %s\" % (type(e).__name__, str(e)))\n        return bookdate, False\n\n    if rootxml is None:\n        logger.debug(\"Error requesting book publication date\")\n        return bookdate, in_cache\n\n    try:\n        bookdate = rootxml.find('book/work/original_publication_year').text\n        if bookdate is None:\n            bookdate = '0000'\n    except (KeyError, AttributeError):\n        logger.error(\"Error reading pubdate for GoodReads bookid %s pubdate [%s]\" % (bookid, bookdate))\n\n    logger.debug(\"GoodReads bookid %s pubdate [%s] %s\" % (bookid, bookdate, in_cache))\n    return bookdate, in_cache\n\n\ndef thingLang(isbn):\n    # try searching librarything for a language code using the isbn\n    # if no language found, librarything return value is \"invalid\" or \"unknown\"\n    # librarything returns plain text, not xml\n    BOOK_URL = 'http://www.librarything.com/api/thingLang.php?isbn=' + isbn\n    proxies = proxyList()\n    booklang = ''\n    try:\n        librarything_wait()\n        timeout = check_int(lazylibrarian.CONFIG['HTTP_TIMEOUT'], 30)\n        r = requests.get(BOOK_URL, timeout=timeout, proxies=proxies)\n        resp = r.text\n        logger.debug(\"LibraryThing reports language [%s] for %s\" % (resp, isbn))\n        if 'invalid' not in resp and 'unknown' not in resp and '<' not in resp:\n            booklang = resp\n    except Exception as e:\n        logger.error(\"%s finding language: %s\" % (type(e).__name__, str(e)))\n    finally:\n        return booklang\n\n\ndef isbn_from_words(words):\n    \"\"\" Use Google to get an ISBN for a book from words in title and authors name.\n        Store the results in the database \"\"\"\n    myDB = database.DBConnection()\n    res = myDB.match(\"SELECT ISBN from isbn WHERE Words=?\", (words,))\n    if res:\n        logger.debug('Found cached ISBN for %s' % words)\n        return res['ISBN']\n\n    baseurl = \"http://www.google.com/search?q=ISBN+\"\n    if not PY2:\n        search_url = baseurl + quote(words.replace(' ', '+'))\n    else:\n        search_url = baseurl + words.replace(' ', '+')\n\n    headers = {'User-Agent': 'w3m/0.5.3',\n               'Content-Type': 'text/plain; charset=\"UTF-8\"',\n               'Content-Transfer-Encoding': 'Quoted-Printable',\n               }\n    content, success = fetchURL(search_url, headers=headers)\n    # noinspection Annotator\n    RE_ISBN13 = re.compile(r'97[89]{1}(?:-?\\d){10,16}|97[89]{1}[- 0-9]{10,16}')\n    RE_ISBN10 = re.compile(r'ISBN\\x20(?=.{13}$)\\d{1,5}([- ])\\d{1,7}\\1\\d{1,6}\\1(\\d|X)$|[- 0-9X]{10,16}')\n\n    # take the first valid looking answer\n    res = RE_ISBN13.findall(content)\n    logger.debug('Found %s ISBN13 for %s' % (len(res), words))\n    for item in res:\n        if len(item) > 13:\n            item = item.replace('-', '').replace(' ', '')\n        if len(item) == 13:\n            myDB.action(\"INSERT into isbn (Words, ISBN) VALUES (?, ?)\", (words, item))\n            return item\n\n    res = RE_ISBN10.findall(content)\n    logger.debug('Found %s ISBN10 for %s' % (len(res), words))\n    for item in res:\n        if len(item) > 10:\n            item = item.replace('-', '').replace(' ', '')\n        if len(item) == 10:\n            myDB.action(\"INSERT into isbn (Words, ISBN) VALUES (?, ?)\", (words, item))\n            return item\n\n    logger.debug('No valid ISBN found for %s' % words)\n    return None\n","repo_name":"lazylibrarian/LazyLibrarian","sub_path":"lazylibrarian/bookwork.py","file_name":"bookwork.py","file_ext":"py","file_size_in_byte":45154,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"77"}
+{"seq_id":"38866192658","text":"from config import setting\nfrom RssCrawler import CrawlerServices\nfrom threading import Thread\nimport grpc\nimport json\nfrom queue import Queue\n\nfrom protobuf import train_pb2, train_pb2_grpc\n\n\ndef create_request(datas: list):\n    input_datas = []\n    for data in datas:\n        item = train_pb2.Block(\n            title=data[\"title\"],\n            description=data[\"description\"],\n            link=data[\"link\"],\n            blog_title=data[\"blog title\"],\n            blog_link=data[\"blog link\"],\n            authors=data[\"authors\"],\n            time_published=data[\"time_published\"]\n        )\n        input_datas.append(item)\n    return train_pb2.InputData(block=input_datas)\n\n\ndef push_data(datas: list):\n    try:\n        chanel = grpc.insecure_channel(setting.core_url)\n        stub = train_pb2_grpc.TrainStub(chanel)\n        res = stub.Training(create_request(datas))\n        if res.status == 200:\n            print(\"\\tPush data succeed\")\n    except:\n        pass\n\n\ndef crawlerService():\n    i = 0\n    for url in setting.urls:\n        datas = CrawlerServices.crawler(url)\n        push_data(datas)\n        i += 1\n        if i == len(setting.urls):\n            print(\"Crawl data is done\")\n","repo_name":"Baristi000/covid-rss-crawler","sub_path":"apis/ApiServices.py","file_name":"ApiServices.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38707019911","text":"\"\"\"\n13\n0\n1\n2\n0\n0\n3\n2\n1\n0\n0\n0\n0\n0\n\"\"\"\nimport heapq\nfrom sys import stdin\n\nls = []\nheapq.heapify(ls)\nn = int(stdin.readline())\nfor _ in range(n):\n    t = int(stdin.readline())\n    if t == 0:\n        if len(ls) == 0:\n            print(0)\n        else: print(-heapq.heappop(ls))\n    else: heapq.heappush(ls, -t)","repo_name":"Ju-yeop/Algorithm_BaekJoon","sub_path":"Heap_11279.py","file_name":"Heap_11279.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16328388050","text":"import socket\nimport time\nimport hello\n\n\ns = socket.socket()\ns.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nhost = socket.gethostname()\nport = 9000\ns.bind((host, port))  #Attatch socket\ns.listen()\n\n\nc, addr = s.accept()\nprint(\"Connection accepted from \" + repr(addr[1]))\nc.sendall(b\"Thank you for connecting\")\nhello.bip()\nc.close()\ntime.sleep(1)\n\n","repo_name":"samseven7/sometests","sub_path":"SendMsgHost.py","file_name":"SendMsgHost.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40507345518","text":"a = \"1\"\nb = \"3\"\nprint(type(int(a+b)))\nprint(int(a)+int(b))\nq=4\nt=9\nprint(q+t)\n\n# Implicit Typecasting\nh =1\nu=8.9\nprint(h+u)","repo_name":"HarshvardhanAC/Python_course","sub_path":"typecasting.py","file_name":"typecasting.py","file_ext":"py","file_size_in_byte":123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31825217234","text":"import os\nfrom typing import TYPE_CHECKING, Any, Dict, List, Tuple, Optional, Union\nfrom typing_extensions import Final\n\nif TYPE_CHECKING:\n    # Circular import otherwise\n    from ..decompile import ByteCodeDecompiler\n\n\nclass Expression:\n    # Any thing that can be evaluated for a result, such as a variable\n    # reference, function call, or mathematical operation.\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        raise NotImplementedError(\n            f\"{self.__class__.__name__} does not implement render()!\"\n        )\n\n\nclass GenericObject(Expression):\n    # One of several built-in object types that can be found in AFP bytecode.\n    # The possible values are all instantiated below and we perform 'is' checks\n    # to determine what actual object is being accessed.\n    def __init__(self, name: str) -> None:\n        self.name = name\n\n    def __repr__(self) -> str:\n        return self.name\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        return self.name\n\n\nNULL = GenericObject(\"NULL\")\nUNDEFINED = GenericObject(\"UNDEFINED\")\nTHIS = GenericObject(\"THIS\")\nROOT = GenericObject(\"ROOT\")\nPARENT = GenericObject(\"PARENT\")\nCLIP = GenericObject(\"CLIP\")\nGLOBAL = GenericObject(\"GLOBAL\")\n\n\nclass Register(Expression):\n    def __init__(self, no: int) -> None:\n        self.no = no\n\n    def __repr__(self) -> str:\n        return f\"Register({self.no})\"\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        return f\"registers[{self.no}]\"\n\n\nclass StringConstant(Expression):\n    __PROPERTIES: Final[List[Tuple[int, str]]] = [\n        # Seems to be properties on every object. These also match the original\n        # SWF properties up to 0x115. GET_PROPERTY and SET_PROPERTY use these\n        # values to determine what to look up on an object.\n        (0x100, \"_x\"),\n        (0x101, \"_y\"),\n        (0x102, \"_xscale\"),\n        (0x103, \"_yscale\"),\n        (0x104, \"_currentframe\"),\n        (0x105, \"_totalframes\"),\n        (0x106, \"_alpha\"),\n        (0x107, \"_visible\"),\n        (0x108, \"_width\"),\n        (0x109, \"_height\"),\n        (0x10A, \"_rotation\"),\n        (0x10B, \"_target\"),\n        (0x10C, \"_framesloaded\"),\n        (0x10D, \"_name\"),\n        (0x10E, \"_droptarget\"),\n        (0x10F, \"_url\"),\n        (0x110, \"_highquality\"),\n        (0x111, \"_focusrect\"),\n        (0x112, \"_soundbuftime\"),\n        (0x113, \"_quality\"),\n        (0x114, \"_xmouse\"),\n        (0x115, \"_ymouse\"),\n        (0x116, \"_z\"),\n        # Global properties on every object.\n        (0x120, \"this\"),\n        (0x121, \"_root\"),\n        (0x122, \"_parent\"),\n        (0x123, \"_global\"),\n        (0x124, \"arguments\"),\n        # Object properties?\n        (0x140, \"blendMode\"),\n        (0x141, \"enabled\"),\n        (0x142, \"hitArea\"),\n        (0x143, \"_lockroot\"),\n        (0x144, \"$version\"),\n        (0x145, \"numChildren\"),\n        (0x146, \"transform\"),\n        (0x147, \"graphics\"),\n        (0x148, \"loaderInfo\"),\n        (0x149, \"mask\"),\n        (0x14A, \"upState\"),\n        (0x14B, \"overState\"),\n        (0x14C, \"downState\"),\n        (0x14D, \"hitTestState\"),\n        (0x14E, \"doubleClickEnabled\"),\n        (0x14F, \"cacheAsBitmap\"),\n        (0x150, \"scrollRect\"),\n        (0x151, \"opaqueBackground\"),\n        (0x152, \"tabChildren\"),\n        (0x153, \"tabEnabled\"),\n        (0x154, \"tabIndex\"),\n        (0x155, \"mouseEnabled\"),\n        (0x156, \"mouseChildren\"),\n        (0x157, \"buttonMode\"),\n        (0x158, \"useHandCursor\"),\n        # Text input properties.\n        (0x160, \"textWidth\"),\n        (0x161, \"textHeight\"),\n        (0x162, \"text\"),\n        (0x163, \"autoSize\"),\n        (0x164, \"textColor\"),\n        (0x165, \"selectable\"),\n        (0x166, \"multiline\"),\n        (0x167, \"wordWrap\"),\n        (0x168, \"border\"),\n        (0x169, \"borderColor\"),\n        (0x16A, \"background\"),\n        (0x16B, \"backgroundColor\"),\n        (0x16C, \"embedFonts\"),\n        (0x16D, \"defaultTextFormat\"),\n        (0x16E, \"htmlText\"),\n        (0x16F, \"mouseWheelEnabled\"),\n        (0x170, \"maxChars\"),\n        (0x171, \"sharpness\"),\n        (0x172, \"thickness\"),\n        (0x173, \"antiAliasType\"),\n        (0x174, \"gridFitType\"),\n        (0x175, \"maxScrollH\"),\n        (0x176, \"maxScrollV\"),\n        (0x177, \"restrict\"),\n        (0x178, \"numLines\"),\n        # Color properties?\n        (0x180, \"ra\"),\n        (0x181, \"rb\"),\n        (0x182, \"ga\"),\n        (0x183, \"gb\"),\n        (0x184, \"ba\"),\n        (0x185, \"bb\"),\n        (0x186, \"aa\"),\n        (0x187, \"ab\"),\n        # Text properties?\n        (0x1A0, \"font\"),\n        (0x1A1, \"size\"),\n        (0x1A2, \"color\"),\n        (0x1A3, \"bold\"),\n        (0x1A4, \"italic\"),\n        (0x1A5, \"underline\"),\n        (0x1A6, \"url\"),\n        (0x1A7, \"target\"),\n        (0x1A8, \"align\"),\n        (0x1A9, \"leftMargin\"),\n        (0x1AA, \"rightMargin\"),\n        (0x1AB, \"indent\"),\n        (0x1AC, \"leading\"),\n        (0x1AD, \"letterSpacing\"),\n        # Who the fuck knows...\n        (0x1C0, \"a\"),\n        (0x1C1, \"b\"),\n        (0x1C2, \"c\"),\n        (0x1C3, \"d\"),\n        (0x1C4, \"e\"),\n        (0x1C5, \"f\"),\n        (0x1C6, \"g\"),\n        (0x1C7, \"h\"),\n        (0x1C8, \"i\"),\n        (0x1C9, \"j\"),\n        (0x1CA, \"k\"),\n        (0x1CB, \"l\"),\n        (0x1CC, \"m\"),\n        (0x1CD, \"n\"),\n        (0x1CE, \"o\"),\n        (0x1CF, \"p\"),\n        (0x1D0, \"q\"),\n        (0x1D1, \"r\"),\n        (0x1D2, \"s\"),\n        (0x1D3, \"t\"),\n        (0x1D4, \"u\"),\n        (0x1D5, \"v\"),\n        (0x1D6, \"w\"),\n        (0x1D7, \"x\"),\n        (0x1D8, \"y\"),\n        (0x1D9, \"z\"),\n        (0x1DA, \"tx\"),\n        (0x1DB, \"ty\"),\n        (0x1DC, \"length\"),\n        (0x1DD, \"ignoreWhite\"),\n        (0x1DE, \"loaded\"),\n        (0x1DF, \"childNodes\"),\n        (0x1E0, \"firstChild\"),\n        (0x1E1, \"nodeValue\"),\n        (0x1E2, \"nextSibling\"),\n        (0x1E3, \"nodeName\"),\n        (0x1E4, \"nodeType\"),\n        (0x1E5, \"attributes\"),\n        (0x1E6, \"__count\"),\n        (0x1E7, \"__type\"),\n        (0x1E8, \"width\"),\n        (0x1E9, \"height\"),\n        (0x1EA, \"useCodepage\"),\n        (0x1EB, \"duration\"),\n        (0x1EC, \"position\"),\n        (0x1ED, \"matrixType\"),\n        (0x1EE, \"matrix\"),\n        (0x1EF, \"prototype\"),\n        (0x1F0, \"__proto__\"),\n        (0x1F1, \"xMin\"),\n        (0x1F2, \"xMax\"),\n        (0x1F3, \"yMin\"),\n        (0x1F4, \"yMax\"),\n        (0x1F5, \"lastChild\"),\n        (0x1F6, \"parentNode\"),\n        (0x1F7, \"previousSibling\"),\n        (0x1F8, \"callee\"),\n        (0x1F9, \"caller\"),\n        (0x1FA, \"colorTransform\"),\n        (0x1FB, \"concatenatedColorTransform\"),\n        (0x1FC, \"concatenatedMatrix\"),\n        (0x1FD, \"pixelBounds\"),\n        (0x1FE, \"matrix3D\"),\n        (0x1FF, \"perspectiveProjection\"),\n        # Commands and object references?\n        (0x200, \"FSCommand:fullscreen\"),\n        (0x201, \"FSCommand:showmenu\"),\n        (0x202, \"FSCommand:allowscale\"),\n        (0x203, \"FSCommand:quit\"),\n        (0x204, \"NaN\"),\n        (0x205, \"Infinity\"),\n        (0x206, \"number\"),\n        (0x207, \"boolean\"),\n        (0x208, \"string\"),\n        (0x209, \"object\"),\n        (0x20A, \"movieclip\"),\n        (0x20B, \"null\"),\n        (0x20C, \"undefined\"),\n        (0x20D, \"function\"),\n        (0x20E, \"normal\"),\n        (0x20F, \"layer\"),\n        (0x210, \"darken\"),\n        (0x211, \"multiply\"),\n        (0x212, \"lighten\"),\n        (0x213, \"screen\"),\n        (0x214, \"overlay\"),\n        (0x215, \"hardlight\"),\n        (0x216, \"subtract\"),\n        (0x217, \"difference\"),\n        (0x218, \"invert\"),\n        (0x219, \"alpha\"),\n        (0x21A, \"erase\"),\n        (0x21B, \"/\"),\n        (0x21C, \"..\"),\n        (0x21D, \"linear\"),\n        (0x21E, \"radial\"),\n        (0x21F, \"none\"),\n        (0x220, \"square\"),\n        (0x221, \"miter\"),\n        (0x222, \"bevel\"),\n        (0x223, \"left\"),\n        (0x224, \"right\"),\n        (0x225, \"center\"),\n        (0x226, \"box\"),\n        (0x227, \"reflect\"),\n        (0x228, \"repeat\"),\n        (0x229, \"RGB\"),\n        (0x22A, \"linearRGB\"),\n        (0x22B, \"justify\"),\n        (0x22C, \"shader\"),\n        (0x22D, \"vertical\"),\n        (0x22E, \"horizontal\"),\n        (0x22F, \"pad\"),\n        (0x230, \"evenOdd\"),\n        (0x231, \"nonZero\"),\n        (0x232, \"negative\"),\n        (0x233, \"positive\"),\n        (0x234, \"xml\"),\n        (0x235, \"B\"),\n        (0x236, \"BL\"),\n        (0x237, \"BR\"),\n        (0x238, \"L\"),\n        (0x239, \"R\"),\n        (0x23A, \"T\"),\n        (0x23B, \"TL\"),\n        (0x23C, \"TR\"),\n        (0x23D, \"exactFit\"),\n        (0x23E, \"noBorder\"),\n        (0x23F, \"noScale\"),\n        (0x240, \"showAll\"),\n        (0x241, \"easeInSine\"),\n        (0x242, \"easeOutSine\"),\n        (0x243, \"easeInOutSine\"),\n        (0x244, \"easeOutInSine\"),\n        (0x245, \"easeInQuad\"),\n        (0x246, \"easeOutQuad\"),\n        (0x247, \"easeInOutQuad\"),\n        (0x248, \"easeOutInQuad\"),\n        (0x249, \"easeInFlash\"),\n        (0x24A, \"easeOutFlash\"),\n        (0x24B, \"element\"),\n        (0x24C, \"dynamic\"),\n        (0x24D, \"binary\"),\n        (0x24E, \"variables\"),\n        (0x24F, \"LB\"),\n        (0x250, \"RB\"),\n        (0x251, \"LT\"),\n        (0x252, \"RT\"),\n        (0x253, \"\"),\n        (0x254, \"arrow\"),\n        (0x255, \"auto\"),\n        (0x256, \"button\"),\n        (0x257, \"hand\"),\n        (0x258, \"ibeam\"),\n        (0x259, \"advanced\"),\n        (0x25A, \"pixel\"),\n        (0x25B, \"subpixel\"),\n        (0x25C, \"full\"),\n        (0x25D, \"inner\"),\n        (0x25E, \"outer\"),\n        (0x25F, \"easeInBack\"),\n        (0x260, \"easeOutBack\"),\n        (0x261, \"easeInOutBack\"),\n        (0x262, \"easeOutInBack\"),\n        (0x263, \"registerClassConstructor\"),\n        (0x264, \"setter\"),\n        (0x265, \"getter\"),\n        (0x266, \"???\"),\n        (0x267, \"aep_dummy\"),\n        (0x268, \"kind\"),\n        (0x269, \"_kind\"),\n        (0x26A, \"org\"),\n        (0x26B, \"flashdevelop\"),\n        (0x26C, \"utils\"),\n        (0x26D, \"FlashConnect\"),\n        (0x26E, \"path\"),\n        (0x26F, \"if\"),\n        (0x270, \"notif\"),\n        (0x271, \"not\"),\n        (0x272, \"A\"),\n        (0x273, \"dmy0273\"),\n        (0x274, \"C\"),\n        (0x275, \"D\"),\n        (0x276, \"dmy0276\"),\n        (0x277, \"F\"),\n        (0x278, \"G\"),\n        (0x279, \"H\"),\n        (0x27A, \"I\"),\n        (0x27B, \"J\"),\n        (0x27C, \"K\"),\n        (0x27D, \"dmy027d\"),\n        (0x27E, \"M\"),\n        (0x27F, \"N\"),\n        (0x280, \"O\"),\n        (0x281, \"P\"),\n        (0x282, \"Q\"),\n        (0x283, \"dmy0283\"),\n        (0x284, \"S\"),\n        (0x285, \"dmy0285\"),\n        (0x286, \"U\"),\n        (0x287, \"V\"),\n        (0x288, \"W\"),\n        (0x289, \"X\"),\n        (0x28A, \"Y\"),\n        (0x28B, \"Z\"),\n        (0x28C, \"fullscreen\"),\n        (0x28D, \"showmenu\"),\n        (0x28E, \"allowscale\"),\n        (0x28F, \"quit\"),\n        (0x290, \"true\"),\n        (0x291, \"false\"),\n        (0x292, \"clamp\"),\n        (0x293, \"ignore\"),\n        (0x294, \"wrap\"),\n        (0x295, \"unknown\"),\n        (0x296, \"bigEndian\"),\n        (0x297, \"littleEndian\"),\n        (0x298, \"fragment\"),\n        (0x299, \"vertex\"),\n        (0x29A, \"bgra\"),\n        (0x29B, \"bgraPacked4444\"),\n        (0x29C, \"bgrPacked565\"),\n        (0x29D, \"compressed\"),\n        (0x29E, \"compressedAlpha\"),\n        (0x29F, \"bytes4\"),\n        (0x2A0, \"float1\"),\n        (0x2A1, \"float2\"),\n        (0x2A2, \"float3\"),\n        (0x2A3, \"float4\"),\n        (0x2A4, \"super\"),\n        (0x2A5, \"axisAngle\"),\n        (0x2A6, \"eulerAngles\"),\n        (0x2A7, \"quaternion\"),\n        (0x2A8, \"orientationStyle\"),\n        # Layer depths\n        (0x2E0, \"_level0\"),\n        (0x2E1, \"_level1\"),\n        (0x2E2, \"_level2\"),\n        (0x2E3, \"_level3\"),\n        (0x2E4, \"_level4\"),\n        (0x2E5, \"_level5\"),\n        (0x2E6, \"_level6\"),\n        (0x2E7, \"_level7\"),\n        (0x2E8, \"_level8\"),\n        (0x2E9, \"_level9\"),\n        (0x2EA, \"_level10\"),\n        (0x2EB, \"_level11\"),\n        (0x2EC, \"_level12\"),\n        (0x2ED, \"_level13\"),\n        (0x2EE, \"_level14\"),\n        (0x2EF, \"_level15\"),\n        # System objects\n        (0x300, \"System\"),\n        (0x301, \"Stage\"),\n        (0x302, \"Key\"),\n        (0x303, \"Math\"),\n        (0x304, \"flash\"),\n        (0x305, \"MovieClip\"),\n        (0x306, \"String\"),\n        (0x307, \"TextField\"),\n        (0x308, \"Color\"),\n        (0x309, \"Date\"),\n        (0x30A, \"SharedObject\"),\n        (0x30B, \"Mouse\"),\n        (0x30C, \"Object\"),\n        (0x30D, \"Sound\"),\n        (0x30E, \"Number\"),\n        (0x30F, \"Array\"),\n        (0x310, \"XML\"),\n        (0x311, \"TextFormat\"),\n        (0x312, \"display\"),\n        (0x313, \"geom\"),\n        (0x314, \"Matrix\"),\n        (0x315, \"Point\"),\n        (0x316, \"BitmapData\"),\n        (0x317, \"data\"),\n        (0x318, \"filters\"),\n        (0x319, \"ColorMatrixFilter\"),\n        (0x31A, \"Function\"),\n        (0x31B, \"XMLNode\"),\n        (0x31C, \"aplib\"),\n        (0x31D, \"Transform\"),\n        (0x31E, \"ColorTransform\"),\n        (0x31F, \"Rectangle\"),\n        (0x320, \"asdlib\"),\n        (0x321, \"XMLController\"),\n        (0x322, \"eManager\"),\n        (0x323, \"Error\"),\n        (0x324, \"MovieClipLoader\"),\n        (0x325, \"UndefChecker\"),\n        (0x326, \"int\"),\n        (0x327, \"uint\"),\n        (0x328, \"Vector\"),\n        (0x329, \"Event\"),\n        (0x32A, \"MouseEvent\"),\n        (0x32B, \"Matrix3D\"),\n        (0x32C, \"Keyboard\"),\n        (0x32D, \"DisplayObject\"),\n        (0x32E, \"Dictionary\"),\n        (0x32F, \"BlendMode\"),\n        (0x330, \"DisplayObjectContainer\"),\n        (0x331, \"Class\"),\n        (0x332, \"EventDispatcher\"),\n        (0x333, \"PerspectiveProjection\"),\n        (0x334, \"Vector3D\"),\n        (0x335, \"aplib3\"),\n        (0x336, \"SoundChannel\"),\n        (0x337, \"Loader\"),\n        (0x338, \"URLRequest\"),\n        (0x339, \"Sprite\"),\n        (0x33A, \"KeyboardEvent\"),\n        (0x33B, \"Timer\"),\n        (0x33C, \"TimerEvent\"),\n        (0x33D, \"asdlib3\"),\n        (0x33E, \"eManager3\"),\n        (0x33F, \"LoaderInfo\"),\n        (0x340, \"ProgressEvent\"),\n        (0x341, \"IOErrorEvent\"),\n        (0x342, \"Graphics\"),\n        (0x343, \"LineScaleMode\"),\n        (0x344, \"CapsStyle\"),\n        (0x345, \"JointStyle\"),\n        (0x346, \"GradientType\"),\n        (0x347, \"SpreadMethod\"),\n        (0x348, \"InterpolationMethod\"),\n        (0x349, \"GraphicsPathCommand\"),\n        (0x34A, \"GraphicsPathWinding\"),\n        (0x34B, \"TriangleCulling\"),\n        (0x34C, \"GraphicsBitmapFill\"),\n        (0x34D, \"GraphicsEndFill\"),\n        (0x34E, \"GraphicsGradientFill\"),\n        (0x34F, \"GraphicsPath\"),\n        (0x350, \"GraphicsSolidFill\"),\n        (0x351, \"GraphicsStroke\"),\n        (0x352, \"GraphicsTrianglePath\"),\n        (0x353, \"IGraphicsData\"),\n        (0x354, \"external\"),\n        (0x355, \"ExternalInterface\"),\n        (0x356, \"Scene\"),\n        (0x357, \"FrameLabel\"),\n        (0x358, \"Shape\"),\n        (0x359, \"SimpleButton\"),\n        (0x35A, \"Bitmap\"),\n        (0x35B, \"StageQuality\"),\n        (0x35C, \"InteractiveObject\"),\n        (0x35D, \"MotionBase\"),\n        (0x35E, \"KeyframeBase\"),\n        (0x35F, \"XMLList\"),\n        (0x360, \"StageAlign\"),\n        (0x361, \"StageScaleMode\"),\n        (0x362, \"AnimatorBase\"),\n        (0x363, \"Animator3D\"),\n        (0x364, \"URLLoader\"),\n        (0x365, \"Capabilities\"),\n        (0x366, \"Aweener\"),\n        (0x367, \"Aweener3\"),\n        (0x368, \"SoundTransform\"),\n        (0x369, \"Namespace\"),\n        (0x36A, \"RegExp\"),\n        (0x36B, \"afplib\"),\n        (0x36C, \"afplib3\"),\n        (0x36D, \"ByteArray\"),\n        (0x36E, \"TextFormatAlign\"),\n        (0x36F, \"TextFieldType\"),\n        (0x370, \"TextFieldAutoSize\"),\n        (0x371, \"SecurityErrorEvent\"),\n        (0x372, \"ApplicationDomain\"),\n        (0x373, \"TextEvent\"),\n        (0x374, \"ErrorEvent\"),\n        (0x375, \"LoaderContext\"),\n        (0x376, \"QName\"),\n        (0x377, \"IllegalOperationError\"),\n        (0x378, \"URLLoaderDataFormat\"),\n        (0x379, \"Security\"),\n        (0x37A, \"DropShadowFilter\"),\n        (0x37B, \"ReferenceError\"),\n        (0x37C, \"Proxy\"),\n        (0x37D, \"XMLSocket\"),\n        (0x37E, \"DataEvent\"),\n        (0x37F, \"Font\"),\n        (0x380, \"IEventDispatcher\"),\n        (0x381, \"LocalConnection\"),\n        (0x382, \"ActionScriptVersion\"),\n        (0x383, \"MouseCursor\"),\n        (0x384, \"TypeError\"),\n        (0x385, \"FocusEvent\"),\n        (0x386, \"AntiAliasType\"),\n        (0x387, \"GridFitType\"),\n        (0x388, \"ArgumentError\"),\n        (0x389, \"BitmapFilterType\"),\n        (0x38A, \"BevelFilter\"),\n        (0x38B, \"BitmapFilter\"),\n        (0x38C, \"BitmapFilterQuality\"),\n        (0x38D, \"XMLController3\"),\n        (0x38E, \"URLVariables\"),\n        (0x38F, \"URLRequestMethod\"),\n        (0x390, \"aeplib\"),\n        (0x391, \"BlurFilter\"),\n        (0x392, \"Stage3D\"),\n        (0x393, \"Context3D\"),\n        (0x394, \"Multitouch\"),\n        (0x395, \"Script\"),\n        (0x396, \"AccessibilityProperties\"),\n        (0x397, \"StaticText\"),\n        (0x398, \"MorphShape\"),\n        (0x399, \"BitmapDataChannel\"),\n        (0x39A, \"DisplacementMapFilter\"),\n        (0x39B, \"GlowFilter\"),\n        (0x39C, \"DisplacementMapFilterMode\"),\n        (0x39D, \"AnimatorFactoryBase\"),\n        (0x39E, \"Endian\"),\n        (0x39F, \"IOError\"),\n        (0x3A0, \"EOFError\"),\n        (0x3A1, \"Context3DTextureFormat\"),\n        (0x3A2, \"Context3DProgramType\"),\n        (0x3A3, \"TextureBase\"),\n        (0x3A4, \"VertexBuffer3D\"),\n        (0x3A5, \"IndexBuffer3D\"),\n        (0x3A6, \"Program3D\"),\n        (0x3A7, \"NativeMenuItem\"),\n        (0x3A8, \"ContextMenuItem\"),\n        (0x3A9, \"NativeMenu\"),\n        (0x3AA, \"ContextMenu\"),\n        (0x3AB, \"ContextMenuEvent\"),\n        (0x3AC, \"Context3DVertexBufferFormat\"),\n        (0x3AD, \"TouchEvent\"),\n        (0x3AE, \"b2Vec2\"),\n        (0x3AF, \"b2Math\"),\n        (0x3B0, \"b2Transform\"),\n        (0x3B1, \"b2Mat22\"),\n        (0x3B2, \"b2Sweep\"),\n        (0x3B3, \"b2AABB\"),\n        (0x3B4, \"b2Vec3\"),\n        (0x3B5, \"b2Mat33\"),\n        (0x3B6, \"b2DistanceProxy\"),\n        (0x3B7, \"b2Shape\"),\n        (0x3B8, \"b2CircleShape\"),\n        (0x3B9, \"b2PolygonShape\"),\n        (0x3BA, \"b2MassData\"),\n        (0x3BB, \"b2DistanceInput\"),\n        (0x3BC, \"b2DistanceOutput\"),\n        (0x3BD, \"b2SimplexCache\"),\n        (0x3BE, \"b2Simplex\"),\n        (0x3BF, \"b2SimplexVertex\"),\n        (0x3C0, \"b2Distance\"),\n        (0x3C1, \"Orientation3D\"),\n        (0x3C2, \"GradientGlowFilter\"),\n        (0x3C3, \"GradientBevelFilter\"),\n        # XML functions\n        (0x400, \"afp_prop_init\"),\n        (0x401, \"afp_prop\"),\n        (0x402, \"afp_prop_dummy\"),\n        (0x403, \"afp_prop_destroy\"),\n        (0x404, \"afp_sync\"),\n        (0x405, \"afp_node_search\"),\n        (0x406, \"afp_node_value\"),\n        (0x407, \"afp_node_array_value\"),\n        (0x408, \"afp_complete\"),\n        (0x409, \"afp_sound_fade_in\"),\n        (0x40A, \"afp_sound_fade_out\"),\n        (0x40B, \"afp_make_gradient_data\"),\n        (0x40C, \"afp_make_alpha_texture\"),\n        (0x40D, \"afp_node_set_value\"),\n        (0x40E, \"afp_node_date_value\"),\n        (0x40F, \"afp_node_num_value\"),\n        (0x410, \"afp_node_array_num_value\"),\n        (0x411, \"afp_node_child\"),\n        (0x412, \"afp_node_parent\"),\n        (0x413, \"afp_node_next\"),\n        (0x414, \"afp_node_prev\"),\n        (0x415, \"afp_node_last\"),\n        (0x416, \"afp_node_first\"),\n        (0x417, \"afp_node_next_same_name\"),\n        (0x418, \"afp_node_prev_same_name\"),\n        (0x419, \"afp_node_name\"),\n        (0x41A, \"afp_node_absolute_path\"),\n        (0x41B, \"afp_node_has_parent\"),\n        (0x41C, \"afp_node_has_child\"),\n        (0x41D, \"afp_node_has_sibling\"),\n        (0x41E, \"afp_node_has_attrib\"),\n        (0x41F, \"afp_node_has_same_name_sibling\"),\n        # System functions\n        (0x420, \"updateAfterEvent\"),\n        (0x421, \"parseInt\"),\n        (0x422, \"parseFloat\"),\n        (0x423, \"Boolean\"),\n        (0x424, \"setInterval\"),\n        (0x425, \"clearInterval\"),\n        (0x426, \"escape\"),\n        (0x427, \"ASSetPropFlags\"),\n        (0x428, \"unescape\"),\n        (0x429, \"isNaN\"),\n        (0x42A, \"isFinite\"),\n        (0x42B, \"trace\"),\n        (0x42C, \"addFrameScript\"),\n        (0x42D, \"getDefinitionByName\"),\n        (0x42E, \"getTimer\"),\n        (0x42F, \"setTimeout\"),\n        (0x430, \"clearTimeout\"),\n        (0x431, \"escapeMultiByte\"),\n        (0x432, \"unescapeMultiByte\"),\n        (0x433, \"getQualifiedClassName\"),\n        (0x434, \"describeType\"),\n        (0x435, \"decodeURI\"),\n        (0x436, \"encodeURI\"),\n        (0x437, \"decodeURIComponent\"),\n        (0x438, \"encodeURIComponent\"),\n        (0x439, \"registerClassAlias\"),\n        (0x43A, \"getClassByAlias\"),\n        (0x43B, \"getQualifiedSuperclassName\"),\n        (0x43C, \"isXMLName\"),\n        (0x43D, \"fscommand\"),\n        # Current movie manipulation functions.\n        (0x440, \"stop\"),\n        (0x441, \"play\"),\n        (0x442, \"gotoAndPlay\"),\n        (0x443, \"gotoAndStop\"),\n        (0x444, \"prevFrame\"),\n        (0x445, \"nextFrame\"),\n        (0x446, \"createEmptyMovieClip\"),\n        (0x447, \"duplicateMovieClip\"),\n        (0x448, \"attachMovie\"),\n        (0x449, \"attachBitmap\"),\n        (0x44A, \"removeMovieClip\"),\n        (0x44B, \"unloadMovie\"),\n        (0x44C, \"loadMovie\"),\n        (0x44D, \"loadVariables\"),\n        (0x44E, \"startDrag\"),\n        (0x44F, \"stopDrag\"),\n        (0x450, \"setMask\"),\n        (0x451, \"hitTest\"),\n        (0x452, \"lineStyle\"),\n        (0x453, \"lineGradientStyle\"),\n        (0x454, \"beginFill\"),\n        (0x455, \"beginBitmapFill\"),\n        (0x456, \"endFill\"),\n        (0x457, \"moveTo\"),\n        (0x458, \"lineTo\"),\n        (0x459, \"curveTo\"),\n        (0x45A, \"clear\"),\n        (0x45B, \"getBytesLoaded\"),\n        (0x45C, \"getBytesTotal\"),\n        (0x45D, \"getDepth\"),\n        (0x45E, \"getNextHighestDepth\"),\n        (0x45F, \"swapDepths\"),\n        (0x460, \"localToGlobal\"),\n        (0x461, \"beginGradientFill\"),\n        (0x462, \"getSWFVersion\"),\n        (0x463, \"getRect\"),\n        (0x464, \"getBounds\"),\n        (0x465, \"getInstanceAtDepth\"),\n        (0x466, \"getURL\"),\n        (0x467, \"globalToLocal\"),\n        (0x468, \"nextScene\"),\n        (0x469, \"prevScene\"),\n        (0x46A, \"getChildByName\"),\n        (0x46B, \"getChildIndex\"),\n        (0x46C, \"addChild\"),\n        (0x46D, \"removeChildAt\"),\n        (0x46E, \"getChildAt\"),\n        (0x46F, \"setChildIndex\"),\n        (0x470, \"lineBitmapStyle\"),\n        (0x471, \"hitTestObject\"),\n        (0x472, \"hitTestPoint\"),\n        (0x473, \"addChildAt\"),\n        (0x474, \"removeChild\"),\n        (0x475, \"swapChildren\"),\n        (0x476, \"swapChildrenAt\"),\n        (0x477, \"getObjectsUnderPoint\"),\n        (0x478, \"createTextField\"),\n        (0x479, \"local3DToGlobal\"),\n        (0x47A, \"globalToLocal3D\"),\n        # System object manipulation functions.\n        (0x480, \"toString\"),\n        (0x481, \"distance\"),\n        (0x482, \"translate\"),\n        (0x483, \"rotate\"),\n        (0x484, \"scale\"),\n        (0x485, \"clone\"),\n        (0x486, \"transformPoint\"),\n        (0x487, \"add\"),\n        (0x488, \"cos\"),\n        (0x489, \"sin\"),\n        (0x48A, \"sqrt\"),\n        (0x48B, \"atan2\"),\n        (0x48C, \"log\"),\n        (0x48D, \"abs\"),\n        (0x48E, \"floor\"),\n        (0x48F, \"ceil\"),\n        (0x490, \"round\"),\n        (0x491, \"pow\"),\n        (0x492, \"max\"),\n        (0x493, \"min\"),\n        (0x494, \"random\"),\n        (0x495, \"acos\"),\n        (0x496, \"asin\"),\n        (0x497, \"atan\"),\n        (0x498, \"tan\"),\n        (0x499, \"exp\"),\n        (0x49A, \"getRGB\"),\n        (0x49B, \"setRGB\"),\n        (0x49C, \"getTransform\"),\n        (0x49D, \"setTransform\"),\n        (0x49E, \"fromCharCode\"),\n        (0x49F, \"substr\"),\n        (0x4A0, \"substring\"),\n        (0x4A1, \"toUpperCase\"),\n        (0x4A2, \"toLowerCase\"),\n        (0x4A3, \"indexOf\"),\n        (0x4A4, \"lastIndexOf\"),\n        (0x4A5, \"charAt\"),\n        (0x4A6, \"charCodeAt\"),\n        (0x4A7, \"split\"),\n        (0x4A8, \"concat\"),\n        (0x4A9, \"getFullYear\"),\n        (0x4AA, \"getUTCFullYear\"),\n        (0x4AB, \"getMonth\"),\n        (0x4AC, \"getUTCMonth\"),\n        (0x4AD, \"getDate\"),\n        (0x4AE, \"getUTCDate\"),\n        (0x4AF, \"getDay\"),\n        (0x4B0, \"getHours\"),\n        (0x4B1, \"getUTCHours\"),\n        (0x4B2, \"getMinutes\"),\n        (0x4B3, \"getUTCMinutes\"),\n        (0x4B4, \"getSeconds\"),\n        (0x4B5, \"getUTCSeconds\"),\n        (0x4B6, \"getTime\"),\n        (0x4B7, \"getTimezoneOffset\"),\n        (0x4B8, \"UTC\"),\n        (0x4B9, \"createElement\"),\n        (0x4BA, \"appendChild\"),\n        (0x4BB, \"createTextNode\"),\n        (0x4BC, \"parseXML\"),\n        (0x4BD, \"load\"),\n        (0x4BE, \"hasChildNodes\"),\n        (0x4BF, \"cloneNode\"),\n        (0x4C0, \"removeNode\"),\n        (0x4C1, \"loadInAdvance\"),\n        (0x4C2, \"createGradientBox\"),\n        (0x4C3, \"loadBitmap\"),\n        (0x4C4, \"hide\"),\n        (0x4C5, \"show\"),\n        (0x4C6, \"addListener\"),\n        (0x4C7, \"removeListener\"),\n        (0x4C8, \"isDown\"),\n        (0x4C9, \"getCode\"),\n        (0x4CA, \"getAscii\"),\n        (0x4CB, \"attachSound\"),\n        (0x4CC, \"start\"),\n        (0x4CD, \"getVolume\"),\n        (0x4CE, \"setVolume\"),\n        (0x4CF, \"setPan\"),\n        (0x4D0, \"loadSound\"),\n        (0x4D1, \"setTextFormat\"),\n        (0x4D2, \"getTextFormat\"),\n        (0x4D3, \"push\"),\n        (0x4D4, \"pop\"),\n        (0x4D5, \"slice\"),\n        (0x4D6, \"splice\"),\n        (0x4D7, \"reverse\"),\n        (0x4D8, \"sort\"),\n        (0x4D9, \"flush\"),\n        (0x4DA, \"getLocal\"),\n        (0x4DB, \"shift\"),\n        (0x4DC, \"unshift\"),\n        (0x4DD, \"registerClass\"),\n        (0x4DE, \"getUTCDay\"),\n        (0x4DF, \"getMilliseconds\"),\n        (0x4E0, \"getUTCMilliseconds\"),\n        (0x4E1, \"addProperty\"),\n        (0x4E2, \"hasOwnProperty\"),\n        (0x4E3, \"isPropertyEnumerable\"),\n        (0x4E4, \"isPrototypeOf\"),\n        (0x4E5, \"unwatch\"),\n        (0x4E6, \"valueOf\"),\n        (0x4E7, \"watch\"),\n        (0x4E8, \"apply\"),\n        (0x4E9, \"call\"),\n        (0x4EA, \"contains\"),\n        (0x4EB, \"containsPoint\"),\n        (0x4EC, \"containsRectangle\"),\n        (0x4ED, \"equals\"),\n        (0x4EE, \"inflate\"),\n        (0x4EF, \"inflatePoint\"),\n        (0x4F0, \"intersection\"),\n        (0x4F1, \"intersects\"),\n        (0x4F2, \"isEmpty\"),\n        (0x4F3, \"offset\"),\n        (0x4F4, \"offsetPoint\"),\n        (0x4F5, \"setEmpty\"),\n        (0x4F6, \"union\"),\n        (0x4F7, \"interpolate\"),\n        (0x4F8, \"join\"),\n        (0x4F9, \"loadClip\"),\n        (0x4FA, \"getProgress\"),\n        (0x4FB, \"unloadClip\"),\n        (0x4FC, \"polar\"),\n        (0x4FD, \"sortOn\"),\n        (0x4FE, \"containsRect\"),\n        (0x4FF, \"getYear\"),\n        # Event constants\n        (0x500, \"onKeyDown\"),\n        (0x501, \"onKeyUp\"),\n        (0x502, \"onMouseDown\"),\n        (0x503, \"onMouseUp\"),\n        (0x504, \"onMouseMove\"),\n        (0x505, \"onLoad\"),\n        (0x506, \"onEnterFrame\"),\n        (0x507, \"onUnload\"),\n        (0x508, \"onRollOver\"),\n        (0x509, \"onRollOut\"),\n        (0x50A, \"onPress\"),\n        (0x50B, \"onRelease\"),\n        (0x50C, \"onReleaseOutside\"),\n        (0x50D, \"onData\"),\n        (0x50E, \"onSoundComplete\"),\n        (0x50F, \"onDragOver\"),\n        (0x510, \"onDragOut\"),\n        (0x511, \"onMouseWheel\"),\n        (0x512, \"onLoadError\"),\n        (0x513, \"onLoadComplete\"),\n        (0x514, \"onLoadInit\"),\n        (0x515, \"onLoadProgress\"),\n        (0x516, \"onLoadStart\"),\n        (0x517, \"onComplete\"),\n        (0x518, \"onCompleteParams\"),\n        (0x519, \"ononCompleteScope\"),\n        (0x51A, \"onStart\"),\n        (0x51B, \"onStartParams\"),\n        (0x51C, \"onStartScope\"),\n        (0x51D, \"onUpdate\"),\n        (0x51E, \"onUpdateParams\"),\n        (0x51F, \"onUpdateScope\"),\n        (0x520, \"onKeyPress\"),\n        (0x521, \"onInitialize\"),\n        (0x522, \"onConstruct\"),\n        (0x5C0, \"scaleX\"),\n        (0x5C1, \"scaleY\"),\n        (0x5C2, \"currentFrame\"),\n        (0x5C3, \"totalFrames\"),\n        (0x5C4, \"visible\"),\n        (0x5C5, \"rotation\"),\n        (0x5C6, \"framesLoaded\"),\n        (0x5C7, \"dropTarget\"),\n        (0x5C8, \"focusRect\"),\n        (0x5C9, \"mouseX\"),\n        (0x5CA, \"mouseY\"),\n        (0x5CB, \"root\"),\n        (0x5CC, \"parent\"),\n        (0x5CD, \"stage\"),\n        (0x5CE, \"currentLabel\"),\n        (0x5CF, \"currentLabels\"),\n        (0x5D0, \"currentFrameLabel\"),\n        (0x5D1, \"currentScene\"),\n        (0x5D2, \"scenes\"),\n        (0x5D3, \"rotationX\"),\n        (0x5D4, \"rotationY\"),\n        (0x5D5, \"rotationZ\"),\n        (0x5D6, \"quality\"),\n        (0x5D7, \"skewX\"),\n        (0x5D8, \"skewY\"),\n        (0x5D9, \"rotationConcat\"),\n        (0x5DA, \"useRotationConcat\"),\n        (0x5DB, \"scaleZ\"),\n        (0x5DC, \"isPlaying\"),\n        # Key constants\n        (0x600, \"BACKSPACE\"),\n        (0x601, \"CAPSLOCK\"),\n        (0x602, \"CONTROL\"),\n        (0x603, \"DELETEKEY\"),\n        (0x604, \"DOWN\"),\n        (0x605, \"END\"),\n        (0x606, \"ENTER\"),\n        (0x607, \"ESCAPE\"),\n        (0x608, \"HOME\"),\n        (0x609, \"INSERT\"),\n        (0x60A, \"LEFT\"),\n        (0x60B, \"PGDN\"),\n        (0x60C, \"PGUP\"),\n        (0x60D, \"RIGHT\"),\n        (0x60E, \"SHIFT\"),\n        (0x60F, \"SPACE\"),\n        (0x610, \"TAB\"),\n        (0x611, \"UP\"),\n        (0x612, \"ARROW\"),\n        (0x613, \"AUTO\"),\n        (0x614, \"BUTTON\"),\n        (0x615, \"HAND\"),\n        (0x616, \"IBEAM\"),\n        # Some sort of sorting constants.\n        (0x620, \"CASEINSENSITIVE\"),\n        (0x621, \"DESCENDING\"),\n        (0x622, \"UNIQUESORT\"),\n        (0x623, \"RETURNINDEXEDARRAY\"),\n        (0x624, \"NUMERIC\"),\n        (0x640, \"ADD\"),\n        (0x641, \"ALPHA\"),\n        (0x642, \"DARKEN\"),\n        (0x643, \"DIFFERENCE\"),\n        (0x644, \"ERASE\"),\n        (0x645, \"HARDLIGHT\"),\n        (0x646, \"INVERT\"),\n        (0x647, \"LAYER\"),\n        (0x648, \"LIGHTEN\"),\n        (0x649, \"MULTIPLY\"),\n        (0x64A, \"NORMAL\"),\n        (0x64B, \"OVERLAY\"),\n        (0x64C, \"SCREEN\"),\n        (0x64D, \"SHADER\"),\n        (0x64E, \"SUBTRACT\"),\n        (0x660, \"dmy0660\"),\n        (0x661, \"NONE\"),\n        (0x662, \"VERTICAL\"),\n        (0x663, \"HORIZONTAL\"),\n        (0x664, \"ROUND\"),\n        (0x665, \"SQUARE\"),\n        (0x666, \"BEVEL\"),\n        (0x667, \"MITER\"),\n        (0x668, \"LINEAR\"),\n        (0x669, \"RADIAL\"),\n        (0x66A, \"PAD\"),\n        (0x66B, \"REFLECT\"),\n        (0x66C, \"REPEAT\"),\n        (0x66D, \"LINEAR_RGB\"),\n        (0x66E, \"NO_OP\"),\n        (0x66F, \"MOVE_TO\"),\n        (0x670, \"LINE_TO\"),\n        (0x671, \"CURVE_TO\"),\n        (0x672, \"WIDE_MOVE_TO\"),\n        (0x673, \"WIDE_LINE_TO\"),\n        (0x674, \"EVEN_ODD\"),\n        (0x675, \"NON_ZERO\"),\n        (0x676, \"NEGATIVE\"),\n        (0x677, \"POSITIVE\"),\n        (0x678, \"FRAGMENT\"),\n        (0x679, \"VERTEX\"),\n        (0x67A, \"BGRA\"),\n        (0x67B, \"BGRA_PACKED\"),\n        (0x67C, \"BGR_PACKED\"),\n        (0x67D, \"COMPRESSED\"),\n        (0x67E, \"COMPRESSED_ALPHA\"),\n        (0x67F, \"BYTES_4\"),\n        (0x680, \"FLOAT_1\"),\n        (0x681, \"FLOAT_2\"),\n        (0x682, \"FLOAT_3\"),\n        (0x683, \"FLOAT_4\"),\n        (0x684, \"e_unknownShape\"),\n        (0x685, \"e_circleShape\"),\n        (0x686, \"e_polygonShape\"),\n        (0x687, \"e_edgeShape\"),\n        (0x688, \"e_shapeTypeCount\"),\n        (0x689, \"CUBIC_CURVE_TO\"),\n        (0x690, \"BOTTOM\"),\n        (0x691, \"BOTTOM_LEFT\"),\n        (0x692, \"BOTTOM_RIGHT\"),\n        (0x693, \"TOP\"),\n        (0x694, \"TOP_LEFT\"),\n        (0x695, \"TOP_RIGHT\"),\n        (0x696, \"EXACT_FIT\"),\n        (0x697, \"NO_BORDER\"),\n        (0x698, \"NO_SCALE\"),\n        (0x699, \"SHOW_ALL\"),\n        (0x6A0, \"CENTER\"),\n        (0x6A1, \"JUSTIFY\"),\n        (0x6A2, \"dmy06a2\"),\n        (0x6A3, \"dmy06a3\"),\n        (0x6A4, \"dmy06a4\"),\n        (0x6A5, \"dmy06a5\"),\n        (0x6A6, \"dmy06a6\"),\n        (0x6A7, \"dmy06a7\"),\n        (0x6A8, \"dmy06a8\"),\n        (0x6A9, \"DYNAMIC\"),\n        (0x6AA, \"INPUT\"),\n        (0x6AB, \"ADVANCED\"),\n        (0x6AC, \"PIXEL\"),\n        (0x6AD, \"SUBPIXEL\"),\n        (0x6B0, \"BINARY\"),\n        (0x6B1, \"TEXT\"),\n        (0x6B2, \"VARIABLES\"),\n        (0x6C0, \"FULL\"),\n        (0x6C1, \"INNER\"),\n        (0x6C2, \"OUTER\"),\n        (0x6C3, \"RED\"),\n        (0x6C4, \"GREEN\"),\n        (0x6C5, \"BLUE\"),\n        (0x6C6, \"CLAMP\"),\n        (0x6C7, \"COLOR\"),\n        (0x6C8, \"IGNORE\"),\n        (0x6C9, \"WRAP\"),\n        (0x6D0, \"DELETE\"),\n        (0x6D1, \"GET\"),\n        (0x6D2, \"HEAD\"),\n        (0x6D3, \"OPTIONS\"),\n        (0x6D4, \"POST\"),\n        (0x6D5, \"PUT\"),\n        (0x6D6, \"BIG_ENDIAN\"),\n        (0x6D7, \"LITTLE_ENDIAN\"),\n        (0x6D8, \"AXIS_ANGLE\"),\n        (0x6D9, \"EULER_ANGLES\"),\n        (0x6DA, \"QUATERNION\"),\n        (0x6F0, \"NUMBER_0\"),\n        (0x6F1, \"NUMBER_1\"),\n        (0x6F2, \"NUMBER_2\"),\n        (0x6F3, \"NUMBER_3\"),\n        (0x6F4, \"NUMBER_4\"),\n        (0x6F5, \"NUMBER_5\"),\n        (0x6F6, \"NUMBER_6\"),\n        (0x6F7, \"NUMBER_7\"),\n        (0x6F8, \"NUMBER_8\"),\n        (0x6F9, \"NUMBER_9\"),\n        # Shape constants?\n        (0x700, \"redMultiplier\"),\n        (0x701, \"greenMultiplier\"),\n        (0x702, \"blueMultiplier\"),\n        (0x703, \"alphaMultiplier\"),\n        (0x704, \"redOffset\"),\n        (0x705, \"greenOffset\"),\n        (0x706, \"blueOffset\"),\n        (0x707, \"alphaOffset\"),\n        (0x708, \"rgb\"),\n        (0x709, \"bottom\"),\n        (0x70A, \"bottomRight\"),\n        (0x70B, \"top\"),\n        (0x70C, \"topLeft\"),\n        (0x70D, \"LOW\"),\n        (0x70E, \"MEDIUM\"),\n        (0x70F, \"HIGH\"),\n        (0x710, \"BEST\"),\n        (0x711, \"name\"),\n        (0x712, \"message\"),\n        (0x713, \"bytesLoaded\"),\n        (0x714, \"bytesTotal\"),\n        (0x715, \"once\"),\n        (0x716, \"MAX_VALUE\"),\n        (0x717, \"MIN_VALUE\"),\n        (0x718, \"NEGATIVE_INFINITY\"),\n        (0x719, \"POSITIVE_INFINITY\"),\n        (0x71A, \"stageWidth\"),\n        (0x71B, \"stageHeight\"),\n        (0x71C, \"frame\"),\n        (0x71D, \"numFrames\"),\n        (0x71E, \"labels\"),\n        (0x71F, \"currentTarget\"),\n        (0x720, \"void\"),\n        (0x721, \"fixed\"),\n        (0x722, \"rawData\"),\n        (0x723, \"type\"),\n        (0x724, \"focalLength\"),\n        (0x725, \"fieldOfView\"),\n        (0x726, \"projectionCenter\"),\n        (0x727, \"E\"),\n        (0x728, \"LN10\"),\n        (0x729, \"LN2\"),\n        (0x72A, \"LOG10E\"),\n        (0x72B, \"LOG2E\"),\n        (0x72C, \"PI\"),\n        (0x72D, \"SQRT1_2\"),\n        (0x72E, \"SQRT2\"),\n        (0x72F, \"stageX\"),\n        (0x730, \"stageY\"),\n        (0x731, \"localX\"),\n        (0x732, \"localY\"),\n        (0x733, \"tintColor\"),\n        (0x734, \"tintMultiplier\"),\n        (0x735, \"brightness\"),\n        (0x736, \"delay\"),\n        (0x737, \"repeatCount\"),\n        (0x738, \"currentCount\"),\n        (0x739, \"running\"),\n        (0x73A, \"charCode\"),\n        (0x73B, \"keyCode\"),\n        (0x73C, \"altKey\"),\n        (0x73D, \"ctrlKey\"),\n        (0x73E, \"shiftKey\"),\n        (0x73F, \"useCodePage\"),\n        (0x740, \"contentLoaderInfo\"),\n        (0x741, \"loaderURL\"),\n        (0x742, \"loader\"),\n        (0x743, \"fullYear\"),\n        (0x744, \"fullYearUTC\"),\n        (0x745, \"month\"),\n        (0x746, \"monthUTC\"),\n        (0x747, \"date\"),\n        (0x748, \"dateUTC\"),\n        (0x749, \"day\"),\n        (0x74A, \"dayUTC\"),\n        (0x74B, \"hours\"),\n        (0x74C, \"hoursUTC\"),\n        (0x74D, \"minutes\"),\n        (0x74E, \"minutesUTC\"),\n        (0x74F, \"seconds\"),\n        (0x750, \"secondsUTC\"),\n        (0x751, \"milliseconds\"),\n        (0x752, \"millisecondsUTC\"),\n        (0x753, \"timezoneOffset\"),\n        (0x754, \"time\"),\n        (0x755, \"joints\"),\n        (0x756, \"fill\"),\n        (0x757, \"colors\"),\n        (0x758, \"commands\"),\n        (0x759, \"miterLimit\"),\n        (0x75A, \"alphas\"),\n        (0x75B, \"ratios\"),\n        (0x75C, \"bitmapData\"),\n        (0x75D, \"vertices\"),\n        (0x75E, \"uvtData\"),\n        (0x75F, \"indices\"),\n        (0x760, \"parameters\"),\n        (0x761, \"frameRate\"),\n        (0x762, \"low\"),\n        (0x763, \"medium\"),\n        (0x764, \"high\"),\n        (0x765, \"best\"),\n        (0x766, \"index\"),\n        (0x767, \"blank\"),\n        (0x768, \"is3D\"),\n        (0x769, \"scaleMode\"),\n        (0x76A, \"frameEvent\"),\n        (0x76B, \"motion\"),\n        (0x76C, \"transformationPoint\"),\n        (0x76D, \"transformationPointZ\"),\n        (0x76E, \"sceneName\"),\n        (0x76F, \"targetParent\"),\n        (0x770, \"targetName\"),\n        (0x771, \"initialPosition\"),\n        (0x772, \"children\"),\n        (0x773, \"child\"),\n        (0x774, \"playerType\"),\n        (0x775, \"os\"),\n        (0x776, \"capabilities\"),\n        (0x777, \"transition\"),\n        (0x778, \"transitionParameters\"),\n        (0x779, \"useFrames\"),\n        (0x77A, \"_color_redMultiplier\"),\n        (0x77B, \"_color_redOffset\"),\n        (0x77C, \"_color_greenMultiplier\"),\n        (0x77D, \"_color_greenOffset\"),\n        (0x77E, \"_color_blueMultiplier\"),\n        (0x77F, \"_color_blueOffset\"),\n        (0x780, \"_color_alphaMultiplier\"),\n        (0x781, \"_color_alphaOffset\"),\n        (0x782, \"_color\"),\n        (0x783, \"soundTransform\"),\n        (0x784, \"volume\"),\n        (0x785, \"uri\"),\n        (0x786, \"prefix\"),\n        (0x787, \"content\"),\n        (0x788, \"contentType\"),\n        (0x789, \"swfVersion\"),\n        (0x78A, \"input\"),\n        (0x78B, \"source\"),\n        (0x78C, \"lastIndex\"),\n        (0x78D, \"stageFocusRect\"),\n        (0x78E, \"currentDomain\"),\n        (0x78F, \"applicationDomain\"),\n        (0x790, \"parentDomain\"),\n        (0x791, \"dataFormat\"),\n        (0x792, \"digest\"),\n        (0x793, \"errorID\"),\n        (0x794, \"available\"),\n        (0x795, \"client\"),\n        (0x796, \"actionScriptVersion\"),\n        (0x797, \"ACTIONSCRIPT2\"),\n        (0x798, \"ACTIONSCRIPT3\"),\n        (0x799, \"delta\"),\n        (0x79A, \"cursor\"),\n        (0x79B, \"buttonDown\"),\n        (0x79C, \"motionArray\"),\n        (0x79D, \"spanStart\"),\n        (0x79E, \"spanEnd\"),\n        (0x79F, \"placeholderName\"),\n        (0x7A0, \"instanceFactoryClass\"),\n        (0x7A1, \"instance\"),\n        (0x7A2, \"method\"),\n        (0x7A3, \"requestHeaders\"),\n        (0x7A4, \"info\"),\n        (0x7A5, \"dotall\"),\n        (0x7A6, \"extended\"),\n        (0x7A7, \"global\"),\n        (0x7A8, \"ignoreCase\"),\n        (0x7A9, \"X_AXIS\"),\n        (0x7AA, \"Y_AXIS\"),\n        (0x7AB, \"Z_AXIS\"),\n        (0x7AC, \"lengthSquared\"),\n        (0x7AD, \"dps\"),\n        (0x7AE, \"from0\"),\n        (0x7AF, \"_text\"),\n        (0x7B0, \"_text_sound\"),\n        (0x7B1, \"blurX\"),\n        (0x7B2, \"blurY\"),\n        (0x7B3, \"step\"),\n        (0x7B4, \"spc\"),\n        (0x7B5, \"stage3Ds\"),\n        (0x7B6, \"context3D\"),\n        (0x7B7, \"version\"),\n        (0x7B8, \"accessibilityProperties\"),\n        (0x7B9, \"description\"),\n        (0x7BA, \"adjustColorBrightness\"),\n        (0x7BB, \"adjustColorContrast\"),\n        (0x7BC, \"adjustColorSaturation\"),\n        (0x7BD, \"adjustColorHue\"),\n        (0x7BE, \"strength\"),\n        (0x7BF, \"angle\"),\n        (0x7C0, \"knockout\"),\n        (0x7C1, \"hideObject\"),\n        (0x7C2, \"manufacturer\"),\n        (0x7C3, \"smoothing\"),\n        (0x7C4, \"rect\"),\n        (0x7C5, \"transparent\"),\n        (0x7C6, \"mapBitmap\"),\n        (0x7C7, \"mapPoint\"),\n        (0x7C8, \"componentX\"),\n        (0x7C9, \"componentY\"),\n        (0x7CA, \"mode\"),\n        (0x7CB, \"highlightColor\"),\n        (0x7CC, \"highlightAlpha\"),\n        (0x7CD, \"shadowColor\"),\n        (0x7CE, \"shadowAlpha\"),\n        (0x7CF, \"endian\"),\n        (0x7D0, \"_scale\"),\n        (0x7D1, \"transitionParams\"),\n        (0x7D2, \"_text_color\"),\n        (0x7D3, \"_text_color_r\"),\n        (0x7D4, \"_text_color_g\"),\n        (0x7D5, \"_text_color_b\"),\n        (0x7D6, \"cancelable\"),\n        (0x7D7, \"col1\"),\n        (0x7D8, \"col2\"),\n        (0x7D9, \"localCenter\"),\n        (0x7DA, \"t0\"),\n        (0x7DB, \"a0\"),\n        (0x7DC, \"c0\"),\n        (0x7DD, \"lowerBound\"),\n        (0x7DE, \"upperBound\"),\n        (0x7DF, \"col3\"),\n        (0x7E0, \"mass\"),\n        (0x7E1, \"m_vertices\"),\n        (0x7E2, \"m_vertexCount\"),\n        (0x7E3, \"m_radius\"),\n        (0x7E4, \"m_p\"),\n        (0x7E5, \"m_normals\"),\n        (0x7E6, \"m_type\"),\n        (0x7E7, \"m_centroid\"),\n        (0x7E8, \"proxyA\"),\n        (0x7E9, \"proxyB\"),\n        (0x7EA, \"transformA\"),\n        (0x7EB, \"transformB\"),\n        (0x7EC, \"useRadii\"),\n        (0x7ED, \"pointA\"),\n        (0x7EE, \"pointB\"),\n        (0x7EF, \"iterations\"),\n        (0x7F0, \"count\"),\n        (0x7F1, \"metric\"),\n        (0x7F2, \"indexA\"),\n        (0x7F3, \"indexB\"),\n        (0x7F4, \"determinant\"),\n        # Some more system functions?\n        (0x800, \"sound_play\"),\n        (0x801, \"sound_stop\"),\n        (0x802, \"sound_stop_all\"),\n        (0x803, \"set_top_mc\"),\n        (0x804, \"set_controlled_XML\"),\n        (0x805, \"set_config_XML\"),\n        (0x806, \"set_data_top\"),\n        (0x807, \"attach_event\"),\n        (0x808, \"detach_event\"),\n        (0x809, \"set\"),\n        (0x80A, \"get\"),\n        (0x80B, \"ready\"),\n        (0x80C, \"afp_available\"),\n        (0x80D, \"controller_available\"),\n        (0x80E, \"push_state\"),\n        (0x80F, \"pop_state\"),\n        (0x810, \"afp_verbose_action\"),\n        (0x811, \"sound_fadeout\"),\n        (0x812, \"sound_fadeout_all\"),\n        (0x813, \"deepPlay\"),\n        (0x814, \"deepStop\"),\n        (0x815, \"deepGotoAndPlay\"),\n        (0x816, \"deepGotoAndStop\"),\n        (0x817, \"detach_event_all\"),\n        (0x818, \"detach_event_id\"),\n        (0x819, \"detach_event_obj\"),\n        (0x81A, \"get_version\"),\n        (0x81B, \"get_version_str\"),\n        (0x81C, \"addAween\"),\n        (0x81D, \"addCaller\"),\n        (0x81E, \"registerSpecialProperty\"),\n        (0x81F, \"registerSpecialPropertySplitter\"),\n        (0x820, \"registerTransition\"),\n        (0x821, \"removeAllAweens\"),\n        (0x822, \"removeAweens\"),\n        (0x823, \"use_konami_lib\"),\n        (0x824, \"sound_volume\"),\n        (0x825, \"sound_volume_all\"),\n        (0x826, \"afp_verbose_script\"),\n        (0x827, \"afp_node_check_value\"),\n        (0x828, \"afp_node_check\"),\n        (0x829, \"get_version_full_str\"),\n        (0x82A, \"set_debug\"),\n        (0x82B, \"num2str_comma\"),\n        (0x82C, \"areacode2str\"),\n        (0x82D, \"num2str_period\"),\n        (0x82E, \"get_columns\"),\n        (0x82F, \"num2mc\"),\n        (0x830, \"warning\"),\n        (0x831, \"fatal\"),\n        (0x832, \"aep_set_frame_control\"),\n        (0x833, \"aep_set_rect_mask\"),\n        (0x834, \"load_movie\"),\n        (0x835, \"get_movie_clip\"),\n        (0x836, \"aep_set_set_frame\"),\n        (0x837, \"deep_goto_play_label\"),\n        (0x838, \"deep_goto_stop_label\"),\n        (0x839, \"goto_play_label\"),\n        (0x83A, \"goto_stop_label\"),\n        (0x83B, \"goto_play\"),\n        (0x83C, \"goto_stop\"),\n        (0x83D, \"set_text\"),\n        (0x83E, \"get_text_data\"),\n        (0x83F, \"attach_movie\"),\n        (0x840, \"attach_bitmap\"),\n        (0x841, \"create_movie_clip\"),\n        (0x842, \"set_text_scroll\"),\n        (0x843, \"set_stage\"),\n        (0x880, \"flash.system\"),\n        (0x881, \"flash.display\"),\n        (0x882, \"flash.text\"),\n        (0x883, \"fl.motion\"),\n        (0x884, \"flash.net\"),\n        (0x885, \"flash.ui\"),\n        (0x886, \"flash.geom\"),\n        (0x887, \"flash.filters\"),\n        (0x888, \"flash.events\"),\n        (0x889, \"flash.utils\"),\n        (0x88A, \"flash.media\"),\n        (0x88B, \"flash.external\"),\n        (0x88C, \"flash.errors\"),\n        (0x88D, \"flash.xml\"),\n        (0x88E, \"flash.display3D\"),\n        (0x88F, \"flash.accessibility\"),\n        (0x890, \"flash.display3D.textures\"),\n        (0x891, \"Box2D.Common.Math\"),\n        (0x892, \"Box2D.Collision\"),\n        (0x893, \"Box2D.Collision.Shapes\"),\n        # Lots of generic function names.\n        (0x900, \"setDate\"),\n        (0x901, \"setUTCDate\"),\n        (0x902, \"setFullYear\"),\n        (0x903, \"setUTCFullYear\"),\n        (0x904, \"setHours\"),\n        (0x905, \"setUTCHours\"),\n        (0x906, \"setMilliseconds\"),\n        (0x907, \"setUTCMilliseconds\"),\n        (0x908, \"setMinutes\"),\n        (0x909, \"setUTCMinutes\"),\n        (0x90A, \"setMonth\"),\n        (0x90B, \"setUTCMonth\"),\n        (0x90C, \"setSeconds\"),\n        (0x90D, \"setUTCSeconds\"),\n        (0x90E, \"setTime\"),\n        (0x90F, \"setYear\"),\n        (0x910, \"addEventListener\"),\n        (0x911, \"removeEventListener\"),\n        (0x912, \"match\"),\n        (0x913, \"replace\"),\n        (0x914, \"search\"),\n        (0x915, \"append\"),\n        (0x916, \"appendRotation\"),\n        (0x917, \"appendScale\"),\n        (0x918, \"appendTranslation\"),\n        (0x919, \"decompose\"),\n        (0x91A, \"deltaTransformVector\"),\n        (0x91B, \"identity\"),\n        (0x91C, \"interpolateTo\"),\n        (0x91D, \"pointAt\"),\n        (0x91E, \"prepend\"),\n        (0x91F, \"prependRotation\"),\n        (0x920, \"prependScale\"),\n        (0x921, \"prependTranslation\"),\n        (0x922, \"recompose\"),\n        (0x923, \"transformVector\"),\n        (0x924, \"transformVectors\"),\n        (0x925, \"transpose\"),\n        (0x926, \"dispatchEvent\"),\n        (0x927, \"toMatrix3D\"),\n        (0x928, \"appendText\"),\n        (0x929, \"getLineText\"),\n        (0x92A, \"replaceText\"),\n        (0x92B, \"propertyIsEnumerable\"),\n        (0x92C, \"setPropertyIsEnumerable\"),\n        (0x92D, \"drawCircle\"),\n        (0x92E, \"drawEllipse\"),\n        (0x92F, \"drawGraphicsData\"),\n        (0x930, \"drawPath\"),\n        (0x931, \"drawRect\"),\n        (0x932, \"drawRoundRect\"),\n        (0x933, \"drawTriangles\"),\n        (0x934, \"copyFrom\"),\n        (0x935, \"addCallback\"),\n        (0x936, \"overrideTargetTransform\"),\n        (0x937, \"addPropertyArray\"),\n        (0x938, \"getCurrentKeyframe\"),\n        (0x939, \"getMatrix3D\"),\n        (0x93A, \"getColorTransform\"),\n        (0x93B, \"getFilters\"),\n        (0x93C, \"getValue\"),\n        (0x93D, \"hasEventListener\"),\n        (0x93E, \"registerParentFrameHandler\"),\n        (0x93F, \"processCurrentFrame\"),\n        (0x940, \"normalize\"),\n        (0x941, \"elements\"),\n        (0x942, \"toXMLString\"),\n        (0x943, \"attribute\"),\n        (0x944, \"localName\"),\n        (0x945, \"nodeKind\"),\n        (0x946, \"exec\"),\n        (0x947, \"toLocaleString\"),\n        (0x948, \"invalidate\"),\n        (0x949, \"getDefinition\"),\n        (0x94A, \"hasDefinition\"),\n        (0x94B, \"descendants\"),\n        (0x94C, \"loadPolicyFile\"),\n        (0x94D, \"reset\"),\n        (0x94E, \"callProperty\"),\n        (0x94F, \"getProperty\"),\n        (0x950, \"setProperty\"),\n        (0x951, \"willTrigger\"),\n        (0x952, \"send\"),\n        (0x953, \"addTargetInfo\"),\n        (0x954, \"drawRoundRectComplex\"),\n        (0x955, \"forEach\"),\n        (0x956, \"filter\"),\n        (0x957, \"every\"),\n        (0x958, \"some\"),\n        (0x959, \"map\"),\n        (0x95A, \"test\"),\n        (0x95B, \"toDateString\"),\n        (0x95C, \"toLocaleDateString\"),\n        (0x95D, \"toLocaleTimeString\"),\n        (0x95E, \"toTimeString\"),\n        (0x95F, \"toUTCString\"),\n        (0x960, \"parse\"),\n        (0x961, \"project\"),\n        (0x962, \"nearEquals\"),\n        (0x963, \"scaleBy\"),\n        (0x964, \"negate\"),\n        (0x965, \"incrementBy\"),\n        (0x966, \"decrementBy\"),\n        (0x967, \"dotProduct\"),\n        (0x968, \"crossProduct\"),\n        (0x969, \"angleBetween\"),\n        (0x96A, \"decode\"),\n        (0x96B, \"copyColumnFrom\"),\n        (0x96C, \"copyColumnTo\"),\n        (0x96D, \"copyRawDataFrom\"),\n        (0x96E, \"copyRawDataTo\"),\n        (0x96F, \"copyRowFrom\"),\n        (0x970, \"copyRowTo\"),\n        (0x971, \"copyToMatrix3D\"),\n        (0x972, \"requestContext3D\"),\n        (0x973, \"initFilters\"),\n        (0x974, \"addFilterPropertyArray\"),\n        (0x975, \"setTo\"),\n        (0x976, \"createBox\"),\n        (0x977, \"deltaTransformPoint\"),\n        (0x978, \"writeByte\"),\n        (0x979, \"writeInt\"),\n        (0x97A, \"readByte\"),\n        (0x97B, \"writeBoolean\"),\n        (0x97C, \"writeDouble\"),\n        (0x97D, \"readBoolean\"),\n        (0x97E, \"readDouble\"),\n        (0x97F, \"writeUnsignedInt\"),\n        (0x980, \"getVector\"),\n        (0x981, \"setVector\"),\n        (0x982, \"unload\"),\n        (0x983, \"unloadAndStop\"),\n        (0x984, \"toExponential\"),\n        (0x985, \"toFixed\"),\n        (0x986, \"toPrecision\"),\n        (0x987, \"getNewTextFormat\"),\n        (0x988, \"SetV\"),\n        (0x989, \"Set\"),\n        (0x98A, \"SetZero\"),\n        (0x98B, \"Make\"),\n        (0x98C, \"Copy\"),\n        (0x98D, \"Length\"),\n        (0x98E, \"LengthSquared\"),\n        (0x98F, \"Normalize\"),\n        (0x990, \"Multiply\"),\n        (0x991, \"GetNegative\"),\n        (0x992, \"NegativeSelf\"),\n        (0x993, \"Clamp\"),\n        (0x994, \"MulX\"),\n        (0x995, \"Dot\"),\n        (0x996, \"SubtractVV\"),\n        (0x997, \"CrossVF\"),\n        (0x998, \"CrossFV\"),\n        (0x999, \"MulTMV\"),\n        (0x99A, \"CrossVV\"),\n        (0x99B, \"Max\"),\n        (0x99C, \"MulMV\"),\n        (0x99D, \"Abs\"),\n        (0x99E, \"MulXT\"),\n        (0x99F, \"SetM\"),\n        (0x9A0, \"AddM\"),\n        (0x9A1, \"Solve\"),\n        (0x9A2, \"Add\"),\n        (0x9A3, \"Min\"),\n        (0x9A4, \"FromAngle\"),\n        (0x9A5, \"GetTransform\"),\n        (0x9A6, \"Advance\"),\n        (0x9A7, \"GetInverse\"),\n        (0x9A8, \"Combine\"),\n        (0x9A9, \"Contains\"),\n        (0x9AA, \"TestOverlap\"),\n        (0x9AB, \"GetCenter\"),\n        (0x9AC, \"Solve22\"),\n        (0x9AD, \"Solve33\"),\n        (0x9AE, \"SetLocalPosition\"),\n        (0x9AF, \"ComputeAABB\"),\n        (0x9B0, \"ComputeMass\"),\n        (0x9B1, \"GetType\"),\n        (0x9B2, \"SetAsArray\"),\n        (0x9B3, \"GetVertex\"),\n        (0x9B4, \"GetSupport\"),\n        (0x9B5, \"GetSupportVertex\"),\n        (0x9B6, \"GetVertexCount\"),\n        (0x9B7, \"GetVertices\"),\n        (0x9B8, \"ReadCache\"),\n        (0x9B9, \"GetClosestPoint\"),\n        (0x9BA, \"GetSearchDirection\"),\n        (0x9BB, \"Solve2\"),\n        (0x9BC, \"Solve3\"),\n        (0x9BD, \"GetWitnessPoints\"),\n        (0x9BE, \"WriteCache\"),\n        (0x9BF, \"Distance\"),\n        (0x9C0, \"cubicCurveTo\"),\n        (0x9C1, \"wideMoveTo\"),\n        (0x9C2, \"wideLineTo\"),\n        (0x9C3, \"insertAt\"),\n        (0x9C4, \"removeAt\"),\n        # Seems like more event constants.\n        (0xA00, \"CLICK\"),\n        (0xA01, \"ENTER_FRAME\"),\n        (0xA02, \"ADDED_TO_STAGE\"),\n        (0xA03, \"MOUSE_DOWN\"),\n        (0xA04, \"MOUSE_MOVE\"),\n        (0xA05, \"MOUSE_OUT\"),\n        (0xA06, \"MOUSE_OVER\"),\n        (0xA07, \"MOUSE_UP\"),\n        (0xA08, \"MOUSE_WHEEL\"),\n        (0xA09, \"ROLL_OUT\"),\n        (0xA0A, \"ROLL_OVER\"),\n        (0xA0B, \"KEY_DOWN\"),\n        (0xA0C, \"KEY_UP\"),\n        (0xA0D, \"TIMER\"),\n        (0xA0E, \"COMPLETE\"),\n        (0xA0F, \"SOUND_COMPLETE\"),\n        (0xA10, \"OPEN\"),\n        (0xA11, \"PROGRESS\"),\n        (0xA12, \"INIT\"),\n        (0xA13, \"IO_ERROR\"),\n        (0xA14, \"TIMER_COMPLETE\"),\n        (0xA15, \"REMOVED_FROM_STAGE\"),\n        (0xA16, \"REMOVED\"),\n        (0xA17, \"FRAME_CONSTRUCTED\"),\n        (0xA18, \"DOUBLE_CLICK\"),\n        (0xA19, \"RESIZE\"),\n        (0xA1A, \"ADDED\"),\n        (0xA1B, \"TAB_CHILDREN_CHANGE\"),\n        (0xA1C, \"TAB_ENABLED_CHANGE\"),\n        (0xA1D, \"TAB_INDEX_CHANGE\"),\n        (0xA1E, \"EXIT_FRAME\"),\n        (0xA1F, \"RENDER\"),\n        (0xA20, \"ACTIVATE\"),\n        (0xA21, \"DEACTIVATE\"),\n        (0xA22, \"SECURITY_ERROR\"),\n        (0xA23, \"ERROR\"),\n        (0xA24, \"CLOSE\"),\n        (0xA25, \"DATA\"),\n        (0xA26, \"CONNECT\"),\n        (0xA27, \"MOUSE_LEAVE\"),\n        (0xA28, \"FOCUS_IN\"),\n        (0xA29, \"FOCUS_OUT\"),\n        (0xA2A, \"KEY_FOCUS_CHANGE\"),\n        (0xA2B, \"MOUSE_FOCUS_CHANGE\"),\n        (0xA2C, \"LINK\"),\n        (0xA2D, \"TEXT_INPUT\"),\n        (0xA2E, \"CHANGE\"),\n        (0xA2F, \"SCROLL\"),\n        (0xA30, \"CONTEXT3D_CREATE\"),\n        (0xA31, \"MENU_ITEM_SELECT\"),\n        (0xA32, \"MENU_SELECT\"),\n        (0xA33, \"UNLOAD\"),\n        (0xA34, \"TOUCH_BEGIN\"),\n        (0xA35, \"TOUCH_END\"),\n        (0xA36, \"TOUCH_MOVE\"),\n        (0xA37, \"TOUCH_OUT\"),\n        (0xA38, \"TOUCH_OVER\"),\n        (0xA39, \"TOUCH_ROLL_OUT\"),\n        (0xA3A, \"TOUCH_ROLL_OVER\"),\n        (0xA3B, \"TOUCH_TAP\"),\n        (0xA3C, \"CONTEXT_MENU\"),\n        (0xA3D, \"MIDDLE_CLICK\"),\n        (0xA3E, \"MIDDLE_MOUSE_DOWN\"),\n        (0xA3F, \"MIDDLE_MOUSE_UP\"),\n        (0xA40, \"RELEASE_OUTSIDE\"),\n        (0xA41, \"RIGHT_CLICK\"),\n        (0xA42, \"RIGHT_MOUSE_DOWN\"),\n        (0xA43, \"RIGHT_MOUSE_UP\"),\n        # Seems like methods on objects tied to events.\n        (0xA80, \"click\"),\n        (0xA81, \"enterFrame\"),\n        (0xA82, \"addedToStage\"),\n        (0xA83, \"mouseDown\"),\n        (0xA84, \"mouseMove\"),\n        (0xA85, \"mouseOut\"),\n        (0xA86, \"mouseOver\"),\n        (0xA87, \"mouseUp\"),\n        (0xA88, \"mouseWheel\"),\n        (0xA89, \"rollOut\"),\n        (0xA8A, \"rollOver\"),\n        (0xA8B, \"keyDown\"),\n        (0xA8C, \"keyUp\"),\n        (0xA8D, \"timer\"),\n        (0xA8E, \"complete\"),\n        (0xA8F, \"soundComplete\"),\n        (0xA90, \"open\"),\n        (0xA91, \"progress\"),\n        (0xA92, \"init\"),\n        (0xA93, \"ioError\"),\n        (0xA94, \"timerComplete\"),\n        (0xA95, \"removedFromStage\"),\n        (0xA96, \"removed\"),\n        (0xA97, \"frameConstructed\"),\n        (0xA98, \"doubleClick\"),\n        (0xA99, \"resize\"),\n        (0xA9A, \"added\"),\n        (0xA9B, \"tabChildrenChange\"),\n        (0xA9C, \"tabEnabledChange\"),\n        (0xA9D, \"tabIndexChange\"),\n        (0xA9E, \"exitFrame\"),\n        (0xA9F, \"render\"),\n        (0xAA0, \"activate\"),\n        (0xAA1, \"deactivate\"),\n        (0xAA2, \"securityError\"),\n        (0xAA3, \"error\"),\n        (0xAA4, \"close\"),\n        (0xAA5, \"udf0aa5\"),\n        (0xAA6, \"connect\"),\n        (0xAA7, \"mouseLeave\"),\n        (0xAA8, \"focusIn\"),\n        (0xAA9, \"focusOut\"),\n        (0xAAA, \"keyFocusChange\"),\n        (0xAAB, \"mouseFocusChange\"),\n        (0xAAC, \"link\"),\n        (0xAAD, \"textInput\"),\n        (0xAAE, \"change\"),\n        (0xAAF, \"scroll\"),\n        (0xAB0, \"context3DCreate\"),\n        (0xAB1, \"menuItemSelect\"),\n        (0xAB2, \"menuSelect\"),\n        (0xAB3, \"udf0ab3\"),\n        (0xAB4, \"touchBegin\"),\n        (0xAB5, \"touchEnd\"),\n        (0xAB6, \"touchMove\"),\n        (0xAB7, \"touchOut\"),\n        (0xAB8, \"touchOver\"),\n        (0xAB9, \"touchRollOut\"),\n        (0xABA, \"touchRollOver\"),\n        (0xABB, \"touchTap\"),\n        (0xABC, \"contextMenu\"),\n        (0xABD, \"middleClick\"),\n        (0xABE, \"middleMouseDown\"),\n        (0xABF, \"middleMouseUp\"),\n        (0xAC0, \"releaseOutside\"),\n        (0xAC1, \"rightClick\"),\n        (0xAC2, \"rightMouseDown\"),\n        (0xAC3, \"rightMouseUp\"),\n        # Seems like debugging information.\n        (0xB00, \"flash.system.System\"),\n        (0xB01, \"flash.display.Stage\"),\n        (0xB02, \"udf0b02\"),\n        (0xB03, \"sme0b03\"),\n        (0xB04, \"udf0b04\"),\n        (0xB05, \"flash.display.MovieClip\"),\n        (0xB06, \"sme0b06\"),\n        (0xB07, \"flash.text.TextField\"),\n        (0xB08, \"fl.motion.Color\"),\n        (0xB09, \"sme0b09\"),\n        (0xB0A, \"flash.net.SharedObject\"),\n        (0xB0B, \"flash.ui.Mouse\"),\n        (0xB0C, \"sme0b0c\"),\n        (0xB0D, \"flash.media.Sound\"),\n        (0xB0E, \"sme0b0e\"),\n        (0xB0F, \"sme0b0f\"),\n        (0xB10, \"sme0b10\"),\n        (0xB11, \"flash.text.TextFormat\"),\n        (0xB12, \"udf0b12\"),\n        (0xB13, \"udf0b13\"),\n        (0xB14, \"flash.geom.Matrix\"),\n        (0xB15, \"flash.geom.Point\"),\n        (0xB16, \"flash.display.BitmapData\"),\n        (0xB17, \"udf0b17\"),\n        (0xB18, \"udf0b18\"),\n        (0xB19, \"flash.filters.ColorMatrixFilter\"),\n        (0xB1A, \"sme0b1a\"),\n        (0xB1B, \"flash.xml.XMLNode\"),\n        (0xB1C, \"sme0b1c\"),\n        (0xB1D, \"flash.geom.Transform\"),\n        (0xB1E, \"flash.geom.ColorTransform\"),\n        (0xB1F, \"flash.geom.Rectangle\"),\n        (0xB20, \"sme0b20\"),\n        (0xB21, \"sme0b21\"),\n        (0xB22, \"sme0b22\"),\n        (0xB23, \"sme0b23\"),\n        (0xB24, \"udf0b24\"),\n        (0xB25, \"sme0b25\"),\n        (0xB26, \"sme0b26\"),\n        (0xB27, \"sme0b27\"),\n        (0xB28, \"sme0b28\"),\n        (0xB29, \"flash.events.Event\"),\n        (0xB2A, \"flash.events.MouseEvent\"),\n        (0xB2B, \"flash.geom.Matrix3D\"),\n        (0xB2C, \"flash.ui.Keyboard\"),\n        (0xB2D, \"flash.display.DisplayObject\"),\n        (0xB2E, \"flash.utils.Dictionary\"),\n        (0xB2F, \"flash.display.BlendMode\"),\n        (0xB30, \"flash.display.DisplayObjectContainer\"),\n        (0xB31, \"sme0b31\"),\n        (0xB32, \"flash.events.EventDispatcher\"),\n        (0xB33, \"flash.geom.PerspectiveProjection\"),\n        (0xB34, \"flash.geom.Vector3D\"),\n        (0xB35, \"sme0b35\"),\n        (0xB36, \"flash.media.SoundChannel\"),\n        (0xB37, \"flash.display.Loader\"),\n        (0xB38, \"flash.net.URLRequest\"),\n        (0xB39, \"flash.display.Sprite\"),\n        (0xB3A, \"flash.events.KeyboardEvent\"),\n        (0xB3B, \"flash.utils.Timer\"),\n        (0xB3C, \"flash.events.TimerEvent\"),\n        (0xB3D, \"sme0b3d\"),\n        (0xB3E, \"sme0b3e\"),\n        (0xB3F, \"flash.display.LoaderInfo\"),\n        (0xB40, \"flash.events.ProgressEvent\"),\n        (0xB41, \"flash.events.IOErrorEvent\"),\n        (0xB42, \"flash.display.Graphics\"),\n        (0xB43, \"flash.display.LineScaleMode\"),\n        (0xB44, \"flash.display.CapsStyle\"),\n        (0xB45, \"flash.display.JointStyle\"),\n        (0xB46, \"flash.display.GradientType\"),\n        (0xB47, \"flash.display.SpreadMethod\"),\n        (0xB48, \"flash.display.InterpolationMethod\"),\n        (0xB49, \"flash.display.GraphicsPathCommand\"),\n        (0xB4A, \"flash.display.GraphicsPathWinding\"),\n        (0xB4B, \"flash.display.TriangleCulling\"),\n        (0xB4C, \"flash.display.GraphicsBitmapFill\"),\n        (0xB4D, \"flash.display.GraphicsEndFill\"),\n        (0xB4E, \"flash.display.GraphicsGradientFill\"),\n        (0xB4F, \"flash.display.GraphicsPath\"),\n        (0xB50, \"flash.display.GraphicsSolidFill\"),\n        (0xB51, \"flash.display.GraphicsStroke\"),\n        (0xB52, \"flash.display.GraphicsTrianglePath\"),\n        (0xB53, \"flash.display.IGraphicsData\"),\n        (0xB54, \"udf0b54\"),\n        (0xB55, \"flash.external.ExternalInterface\"),\n        (0xB56, \"flash.display.Scene\"),\n        (0xB57, \"flash.display.FrameLabel\"),\n        (0xB58, \"flash.display.Shape\"),\n        (0xB59, \"flash.display.SimpleButton\"),\n        (0xB5A, \"flash.display.Bitmap\"),\n        (0xB5B, \"flash.display.StageQuality\"),\n        (0xB5C, \"flash.display.InteractiveObject\"),\n        (0xB5D, \"fl.motion.MotionBase\"),\n        (0xB5E, \"fl.motion.KeyframeBase\"),\n        (0xB5F, \"sme0b5f\"),\n        (0xB60, \"flash.display.StageAlign\"),\n        (0xB61, \"flash.display.StageScaleMode\"),\n        (0xB62, \"fl.motion.AnimatorBase\"),\n        (0xB63, \"fl.motion.Animator3D\"),\n        (0xB64, \"flash.net.URLLoader\"),\n        (0xB65, \"flash.system.Capabilities\"),\n        (0xB66, \"sme0b66\"),\n        (0xB67, \"sme0b67\"),\n        (0xB68, \"flash.media.SoundTransform\"),\n        (0xB69, \"sme0b69\"),\n        (0xB6A, \"sme0b6a\"),\n        (0xB6B, \"sme0b6b\"),\n        (0xB6C, \"sme0b6c\"),\n        (0xB6D, \"flash.utils.ByteArray\"),\n        (0xB6E, \"flash.text.TextFormatAlign\"),\n        (0xB6F, \"flash.text.TextFieldType\"),\n        (0xB70, \"flash.text.TextFieldAutoSize\"),\n        (0xB71, \"flash.events.SecurityErrorEvent\"),\n        (0xB72, \"flash.system.ApplicationDomain\"),\n        (0xB73, \"flash.events.TextEvent\"),\n        (0xB74, \"flash.events.ErrorEvent\"),\n        (0xB75, \"flash.system.LoaderContext\"),\n        (0xB76, \"sme0b76\"),\n        (0xB77, \"flash.errors.IllegalOperationError\"),\n        (0xB78, \"flash.net.URLLoaderDataFormat\"),\n        (0xB79, \"flash.system.Security\"),\n        (0xB7A, \"flash.filters.DropShadowFilter\"),\n        (0xB7B, \"sme0b7b\"),\n        (0xB7C, \"flash.utils.Proxy\"),\n        (0xB7D, \"flash.net.XMLSocket\"),\n        (0xB7E, \"flash.events.DataEvent\"),\n        (0xB7F, \"flash.text.Font\"),\n        (0xB80, \"flash.events.IEventDispatcher\"),\n        (0xB81, \"flash.net.LocalConnection\"),\n        (0xB82, \"flash.display.ActionScriptVersion\"),\n        (0xB83, \"flash.ui.MouseCursor\"),\n        (0xB84, \"sme0b84\"),\n        (0xB85, \"flash.events.FocusEvent\"),\n        (0xB86, \"flash.text.AntiAliasType\"),\n        (0xB87, \"flash.text.GridFitType\"),\n        (0xB88, \"sme0b88\"),\n        (0xB89, \"flash.filters.BitmapFilterType\"),\n        (0xB8A, \"flash.filters.BevelFilter\"),\n        (0xB8B, \"flash.filters.BitmapFilter\"),\n        (0xB8C, \"flash.filters.BitmapFilterQuality\"),\n        (0xB8D, \"sme0b8d\"),\n        (0xB8E, \"flash.net.URLVariables\"),\n        (0xB8F, \"flash.net.URLRequestMethod\"),\n        (0xB90, \"sme0b90\"),\n        (0xB91, \"flash.filters.BlurFilter\"),\n        (0xB92, \"flash.display.Stage3D\"),\n        (0xB93, \"flash.display3D.Context3D\"),\n        (0xB94, \"flash.ui.Multitouch\"),\n        (0xB95, \"udf0b95\"),\n        (0xB96, \"flash.accessibility.AccessibilityProperties\"),\n        (0xB97, \"flash.text.StaticText\"),\n        (0xB98, \"flash.display.MorphShape\"),\n        (0xB99, \"flash.display.BitmapDataChannel\"),\n        (0xB9A, \"flash.filters.DisplacementMapFilter\"),\n        (0xB9B, \"flash.filters.GlowFilter\"),\n        (0xB9C, \"flash.filters.DisplacementMapFilterMode\"),\n        (0xB9D, \"fl.motion.AnimatorFactoryBase\"),\n        (0xB9E, \"flash.utils.Endian\"),\n        (0xB9F, \"flash.errors.IOError\"),\n        (0xBA0, \"flash.errors.EOFError\"),\n        (0xBA1, \"flash.display3D.Context3DTextureFormat\"),\n        (0xBA2, \"flash.display3D.Context3DProgramType\"),\n        (0xBA3, \"flash.display3D.textures.TextureBase\"),\n        (0xBA4, \"flash.display3D.VertexBuffer3D\"),\n        (0xBA5, \"flash.display3D.IndexBuffer3D\"),\n        (0xBA6, \"flash.display3D.Program3D\"),\n        (0xBA7, \"flash.display.NativeMenuItem\"),\n        (0xBA8, \"flash.ui.ContextMenuItem\"),\n        (0xBA9, \"flash.display.NativeMenu\"),\n        (0xBAA, \"flash.ui.ContextMenu\"),\n        (0xBAB, \"flash.events.ContextMenuEvent\"),\n        (0xBAC, \"flash.display3D.Context3DVertexBufferFormat\"),\n        (0xBAD, \"flash.events.TouchEvent\"),\n        (0xBAE, \"Box2D.Common.Math.b2Vec2\"),\n        (0xBAF, \"Box2D.Common.Math.b2Math\"),\n        (0xBB0, \"Box2D.Common.Math.b2Transform\"),\n        (0xBB1, \"Box2D.Common.Math.b2Mat22\"),\n        (0xBB2, \"Box2D.Common.Math.b2Sweep\"),\n        (0xBB3, \"Box2D.Collision.b2AABB\"),\n        (0xBB4, \"Box2D.Common.Math.b2Vec3\"),\n        (0xBB5, \"Box2D.Common.Math.b2Mat33\"),\n        (0xBB6, \"Box2D.Collision.b2DistanceProxy\"),\n        (0xBB7, \"Box2D.Collision.Shapes.b2Shape\"),\n        (0xBB8, \"Box2D.Collision.Shapes.b2CircleShape\"),\n        (0xBB9, \"Box2D.Collision.Shapes.b2PolygonShape\"),\n        (0xBBA, \"Box2D.Collision.Shapes.b2MassData\"),\n        (0xBBB, \"Box2D.Collision.b2DistanceInput\"),\n        (0xBBC, \"Box2D.Collision.b2DistanceOutput\"),\n        (0xBBD, \"Box2D.Collision.b2SimplexCache\"),\n        (0xBBE, \"Box2D.Collision.b2Simplex\"),\n        (0xBBF, \"Box2D.Collision.b2SimplexVertex\"),\n        (0xBC0, \"Box2D.Collision.b2Distance\"),\n        (0xBC1, \"flash.geom.Orientation3D\"),\n        (0xBC2, \"flash.filters.GradientGlowFilter\"),\n        (0xBC3, \"flash.filters.GradientBevelFilter\"),\n        # More generic constants.\n        (0xC00, \"NEARLY_ZERO\"),\n        (0xC01, \"EXACTLY_ZERO\"),\n        (0xC02, \"debug_mode\"),\n        (0xD00, \"m_count\"),\n        (0xD01, \"wA\"),\n        (0xD02, \"wB\"),\n        (0xD03, \"bubbles\"),\n        (0xD04, \"checkPolicyFile\"),\n        (0xD05, \"securityDomain\"),\n        (0xD06, \"spreadMethod\"),\n        (0xD07, \"interpolationMethod\"),\n        (0xD08, \"focalPointRatio\"),\n        (0xD09, \"culling\"),\n        (0xD0A, \"caps\"),\n        (0xD0B, \"winding\"),\n    ]\n\n    @classmethod\n    def property_to_name(cls, propid: int) -> str:\n        for i, p in cls.__PROPERTIES:\n            if i == propid:\n                return p\n        return f\"<UNKNOWN {hex(propid)}>\"\n\n    def __init__(self, const: int, alias: Optional[str] = None) -> None:\n        self.const = const\n        self.alias = alias\n\n    def __repr__(self) -> str:\n        if self.alias:\n            return f\"StringConstant({hex(self.const)}: {self.alias})\"\n        else:\n            return f\"StringConstant({hex(self.const)}: {StringConstant.property_to_name(self.const)})\"\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        if self.alias:\n            return self.alias\n        else:\n            return StringConstant.property_to_name(self.const)\n\n\nclass ArithmeticExpression(Expression):\n    def __init__(self, left: Any, op: str, right: Any) -> None:\n        self.left = left\n        self.op = op\n        self.right = right\n\n    def __repr__(self) -> str:\n        left = value_ref(self.left, \"\", parens=True)\n        right = value_ref(self.right, \"\", parens=True)\n        return f\"{left} {self.op} {right}\"\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        left = value_ref(self.left, parent_prefix, parens=True)\n        right = value_ref(self.right, parent_prefix, parens=True)\n\n        if nested and self.op == \"-\":\n            return f\"({left} {self.op} {right})\"\n        else:\n            return f\"{left} {self.op} {right}\"\n\n\nclass NotExpression(Expression):\n    def __init__(self, obj: Any) -> None:\n        self.obj = obj\n\n    def __repr__(self) -> str:\n        obj = value_ref(self.obj, \"\", parens=True)\n        return f\"not {obj}\"\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        obj = value_ref(self.obj, parent_prefix, parens=True)\n        return f\"not {obj}\"\n\n\nclass Array(Expression):\n    # Call a method on an object.\n    def __init__(self, params: List[Any]) -> None:\n        self.params = params\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        params = [value_ref(param, parent_prefix) for param in self.params]\n        return f\"[{', '.join(params)}]\"\n\n\nclass Object(Expression):\n    # Call a method on an object.\n    def __init__(self, params: Dict[Any, Any]) -> None:\n        self.params = params\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        params = [\n            f\"{value_ref(key, parent_prefix)}: {value_ref(val, parent_prefix)}\"\n            for (key, val) in self.params.items()\n        ]\n        lpar = \"{\"\n        rpar = \"}\"\n\n        return f\"{lpar}{', '.join(params)}{rpar}\"\n\n\nclass FunctionCall(Expression):\n    # Call a method on an object.\n    def __init__(\n        self, insertion_ref: int, name: Union[str, StringConstant], params: List[Any]\n    ) -> None:\n        self.insertion_ref = insertion_ref\n        self.name = name\n        self.params = params\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        name = name_ref(self.name, parent_prefix)\n        params = [value_ref(param, parent_prefix) for param in self.params]\n        return f\"{name}({', '.join(params)})\"\n\n\nclass GetTimeFunctionCall(FunctionCall):\n    # Call the built-in 'get time' method which returns the current playback position.\n    def __init__(self, insertion_ref: int) -> None:\n        super().__init__(insertion_ref, \"builtin_GetCurrentPlaybackPosition\", [])\n\n\nclass GetPathFunctionCall(FunctionCall):\n    # Call the built-in 'get time' method which returns the current playback position.\n    def __init__(self, insertion_ref: int, movieclip: Any) -> None:\n        super().__init__(insertion_ref, \"builtin_GetPathOfMovie\", [movieclip])\n\n\nclass MethodCall(Expression):\n    # Call a method on an object.\n    def __init__(\n        self,\n        insertion_ref: int,\n        objectref: Any,\n        name: Union[str, int, Expression],\n        params: List[Any],\n    ) -> None:\n        self.insertion_ref = insertion_ref\n        self.objectref = objectref\n        self.name = name\n        self.params = params\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        try:\n            obj = object_ref(self.objectref, parent_prefix)\n            name = name_ref(self.name, parent_prefix)\n            params = [value_ref(param, parent_prefix) for param in self.params]\n            return f\"{obj}.{name}({', '.join(params)})\"\n        except Exception:\n            obj = object_ref(self.objectref, parent_prefix)\n            name = value_ref(self.name, parent_prefix)\n            params = [value_ref(param, parent_prefix) for param in self.params]\n            return f\"{obj}[{name}]({', '.join(params)})\"\n\n\nclass NewFunction(Expression):\n    # Create a new function.\n    def __init__(self, flags: int, body: \"ByteCodeDecompiler\") -> None:\n        self.flags = flags\n        self.body = body\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        code = self.body.as_string(prefix=parent_prefix + \"    \")\n        opar = \"{\"\n        cpar = \"}\"\n        val = f\"new Function({hex(self.flags)}, {opar}{os.linesep}{code}{os.linesep}{parent_prefix}{cpar})\"\n        if nested:\n            return f\"({val})\"\n        else:\n            return val\n\n\nclass NewObject(Expression):\n    # Create a new object of type.\n    def __init__(self, objname: Union[str, StringConstant], params: List[Any]) -> None:\n        self.objname = objname\n        self.params = params\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        objname = name_ref(self.objname, parent_prefix)\n        params = [value_ref(param, parent_prefix) for param in self.params]\n        val = f\"new {objname}({', '.join(params)})\"\n        if nested:\n            return f\"({val})\"\n        else:\n            return val\n\n\nclass Variable(Expression):\n    def __init__(self, name: Union[str, StringConstant]) -> None:\n        self.name = name\n\n    def __repr__(self) -> str:\n        return f\"Variable({name_ref(self.name, '')})\"\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        return name_ref(self.name, parent_prefix)\n\n\nclass Member(Expression):\n    # A member can be an array entry in an array, or an object member as accessed\n    # in array lookup syntax or dot notation.\n    def __init__(self, objectref: Any, member: Union[str, int, Expression]) -> None:\n        self.objectref = objectref\n        self.member = member\n\n    def __repr__(self) -> str:\n        return self.render(\"\")\n\n    def render(self, parent_prefix: str, nested: bool = False) -> str:\n        try:\n            member = name_ref(self.member, parent_prefix)\n            ref = object_ref(self.objectref, parent_prefix)\n            return f\"{ref}.{member}\"\n        except Exception:\n            # This is not a simple string object reference.\n            member = value_ref(self.member, parent_prefix)\n            ref = object_ref(self.objectref, parent_prefix)\n            return f\"{ref}[{member}]\"\n\n\n# The following are helpers which facilitate rendering out various parts of expressions.\ndef object_ref(obj: Any, parent_prefix: str) -> str:\n    if isinstance(\n        obj, (GenericObject, Variable, Member, MethodCall, FunctionCall, Register)\n    ):\n        return obj.render(parent_prefix, nested=True)\n    else:\n        raise Exception(f\"Unsupported objectref {obj} ({type(obj)})\")\n\n\ndef value_ref(param: Any, parent_prefix: str, parens: bool = False) -> str:\n    if isinstance(param, StringConstant):\n        # Treat this as a string constant.\n        return repr(param.render(parent_prefix))\n    elif isinstance(param, Expression):\n        return param.render(parent_prefix, nested=parens)\n    elif isinstance(param, (str, int, float, bool)):\n        return repr(param)\n    else:\n        raise Exception(f\"Unsupported valueref {param} ({type(param)})\")\n\n\ndef name_ref(param: Any, parent_prefix: str) -> str:\n    # Reference a name, so strings should not be quoted.\n    if isinstance(param, str):\n        return param\n    elif isinstance(param, StringConstant):\n        return param.render(parent_prefix)\n    else:\n        raise Exception(f\"Unsupported nameref {param} ({type(param)})\")\n","repo_name":"DragonMinded/bemaniutils","sub_path":"bemani/format/afp/types/expression.py","file_name":"expression.py","file_ext":"py","file_size_in_byte":70589,"program_lang":"python","lang":"en","doc_type":"code","stars":185,"dataset":"github-code","pt":"77"}
+{"seq_id":"9810208812","text":"#!/usr/bin/env python3\nfrom tkinter import Tk, Frame, Menu, Label, Button, Entry, TOP, BOTH, BOTTOM\nimport matplotlib.pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport numpy as np\n\nclass Plotter:\n    \"\"\"\n    A General Plotter that plots an arbitrary function\n\n    Attributes:\n        func: The fucntion to plot\n        x: List containing the x values\n        y: List containing the y values\n        figure: Matplotlib figure object\n        axes: Axes for the plot\n        line: the plotted line used in animation\n\n    Methods:\n        init_animation: A neccecary function for the FuncAnimation to work\n        init_plot: Sets up the x and y lists\n        get_animation: runs the animation\n        animate: A neccecary function for FuncAnimation\n        plot: Plots the values in x and y lists\n    \"\"\"\n\n    def __init__(self, func):\n        \"\"\"\n        Parameters:\n             func: A function to plot\n        \"\"\"\n        self.func = func\n        self.x = []\n        self.y = []\n        self.figure = plt.figure(figsize = (8,6), dpi=100)\n        self.axes = self.figure.add_subplot()\n        self.line = None\n\n        # Config\n        plt.style.use('fivethirtyeight')\n        plt.grid()\n\n    def init_animation(self):\n        \"\"\"initializes plot\"\"\"\n        self.line, = self.axes.plot(self.x, self.y, 'r', linewidth=0.5)\n        self.line.set_data(self.x, self.y)\n        return self.line,\n\n    def init_plot(self, xlimit):\n        \"\"\"initializes value lists\"\"\"\n        self.x = list(range(xlimit))\n        self.y = [self.func(x) for x in self.x]\n\n    def animate(self,i):\n        \"\"\"Runs every frame of animation\"\"\"\n        self.x = list(range(i))\n        self.y = [self.func(x) for x in range(i)]\n        self.line.set_data(self.x, self.y)\n        return self.line,\n\n\n    def get_animation(self):\n        \"\"\"\n        Runs animation and returns the variabal.  \n        IMPORTANT:\n        The animation variable must be saved otherwise the \n        animation gets garbage collected\n        \"\"\"\n        animation = FuncAnimation(self.figure, self.animate, \n                                  init_func=self.init_animation, interval=20,\n                                  frames=1000, blit=True)\n        return animation\n\n    def plot(self, xlimit):\n        \"\"\"Sets up the data and plots it\"\"\"\n        self.init_plot(xlimit)\n        self.axes.plot(self.x, self.y, 'r', linewidth=0.5)\n        plt.tight_layout()\n\n\nclass GUI:\n    \"\"\"\n    A GUI class that can handle menus, different pages\n    and displaying graphs\n\n    Attributes:\n         root: Description\n         main_frame: Description\n         frames: Description\n         entry_boxes: Description\n\n    Methods:\n         add_menu_item:\n         add_frame:\n         add_button:\n         add_entry_box:\n\n    \"\"\"\n\n    def __init__(self):\n        self.root = Tk()\n        self.main_frame = Frame(self.root)\n        self.title = 'GUI Test'\n        self.entry_boxes = {self.main_frame: []}\n\n        # GUI Objects\n        self.menu_area = Menu(self.root)\n\n        # Config\n        self.root.title(self.title)\n        self.root.geometry('800x800')\n        self.root.config(menu=self.menu_area)\n        self.main_frame.pack(fill=BOTH, expand=1, padx=10, pady=10)\n\n    def add_menu_item(self, label, command, last_item=False):\n        \"\"\"Adds a menu item to the top menu\"\"\"\n        self.menu_area.add_command(label=label, command=command)\n        if last_item:\n            self.menu_area.add_command(label='exit', command=self.root.destroy)\n\n    def add_frame(self, bg):\n        \"\"\"Creates a frame and returns it\"\"\"\n        frame = Frame(self.root, bg=bg)\n        frame.pack(padx=10, pady=10)\n        self.entry_boxes[frame] = []\n        return frame\n\n    def add_button(self, label, command, frame, bind_key=None):\n        \"\"\"Creates a button\"\"\"\n        button = Button(frame, text=label, command=command)\n        button.pack(side=TOP, padx=10, pady=10, fill=BOTH)\n        self.root.bind(bind_key, command)\n\n    def add_entry_box(self, label, frame):\n        \"\"\"Creates an text box width a label\"\"\"\n        entry_box = Entry(frame, width = 4)\n        self.entry_boxes[frame].append(entry_box)\n        Label(frame, text=label).pack(side=TOP, padx=10, pady=10)\n        entry_box.pack(side=TOP, padx=20, pady=10)\n\n    def main(self):\n        \"\"\"Just a wrapper for root.mainloop\"\"\"\n        self.root.mainloop()\n\n\n\nclass GUI_Plotter(GUI):\n    \"\"\"\n    A GUI app that plots and animates a function\n\n    Attributes:\n         amp: Description\n         freq: Description\n         x: Description\n         y:\n         func:\n         plotter_main:\n         plotter_sec:\n         sec_frame:\n         started:\n         animation:\n         paused:\n\n    Methods:\n         main: A wrapper for root.mainloop\n         pause:\n         start:\n         update:\n         create_canvas:\n         hide_frames:\n         show_main:\n         show_sec:\n         plot:\n\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        self.amp = -5\n        self.freq = 3\n        self.xmax = 1000\n        self.x = []\n        self.y = []\n        self.func = lambda t : 3 * np.pi * np.exp(self.amp*np.sin(self.freq*np.pi*t))\n        self.plotter_main = Plotter(self.func)\n        self.plotter_sec = Plotter(self.func)\n        self.sec_frame = self.add_frame(bg='Green')\n        self.started = False\n        self.animation = None\n        self.paused = False\n\n        # UI\n        self.add_entry_box('Amplitude', self.main_frame)\n        self.add_entry_box('Amplitude', self.sec_frame)\n        self.add_entry_box('Frequency', self.main_frame)\n        self.add_entry_box('Frequency', self.sec_frame)\n        self.add_button('Plot', self.plot, self.main_frame)\n        self.add_button('Start', self.start, self.sec_frame)\n        self.add_button('Pause', self.pause, self.sec_frame)\n        self.add_menu_item(label='Exercise 2', command=self.show_main)\n        self.add_menu_item(label='Exercise 2.1', command=self.show_sec, last_item=True)\n        self.show_main()\n        self.create_canvas(self.sec_frame, self.plotter_sec)\n\n\n    def pause(self):\n        \"\"\"Pauses the animation if paused, starts it if not\"\"\"\n        if self.started:\n            if not self.paused:\n                self.animation.event_source.stop()\n            else:\n                self.animation.event_source.start()\n            self.paused = not self.paused\n\n    def start(self):\n        \"\"\"Starts the animation and runs the tkinter mainloop\"\"\"\n        self.started = True\n        self.update(self.plotter_sec, self.sec_frame)\n        self.animation = self.plotter_sec.get_animation()\n        self.root.mainloop()\n\n    def update(self, plotter_obj, frame):\n        \"\"\"A simle helper function that updates the values and sets limits based on those\"\"\"\n        try:\n            self.amp = float(self.entry_boxes[frame][0].get())\n            self.freq = float(self.entry_boxes[frame][1].get())\n        except ValueError:\n            print(f'Running plot with standard values Amplitude: {self.amp}, Frequency: {self.freq}')\n            pass\n        plotter_obj.axes.set_xlim([0, self.xmax])\n        end_vals = [self.func(x) for x in np.arange(self.xmax - self.xmax/10, self.xmax)]\n        max_val = max(np.abs(max(end_vals)), np.abs(min(end_vals)))\n        start_val = self.func(1)\n        print(max_val, start_val)\n        plotter_obj.axes.set_ylim(ymin=start_val-max_val/(10**11),\n                                  ymax=start_val+max_val/(10**11))\n\n    def create_canvas(self, frame, plotter_obj):\n        \"\"\"A helper function that sets up a canvas where the plot is drawn\"\"\"\n        canvas = FigureCanvasTkAgg(plotter_obj.figure, master=frame)\n        canvas.draw()\n        canvas.get_tk_widget().pack(side=BOTTOM, fill=BOTH, expand=1)\n\n    def hide_frames(self):\n        \"\"\"A helper function for reseting the frames when switching\"\"\"\n        self.main_frame.pack_forget()\n        self.sec_frame.pack_forget()\n\n    def show_main(self):\n        \"\"\"Shows the main frame\"\"\"\n        self.hide_frames()\n        self.main_frame.pack(fill=BOTH, expand=1)\n\n    def show_sec(self):\n        \"\"\"Shows the second frame\"\"\"\n        self.hide_frames()\n        self.sec_frame.pack(fill=BOTH, expand=1)\n\n    def plot(self):\n        \"\"\"Plots data inside the canvas tkinter widget\"\"\"\n        self.update(self.plotter_main, self.main_frame)\n        self.plotter_main.plot(1000)\n        self.create_canvas(self.main_frame, self.plotter_main)\n        plt.show()\n\n\n\ng = GUI_Plotter()\ng.main()\n","repo_name":"maobe2500/kthfs","sub_path":"exec2/GUI_Plotter.py","file_name":"GUI_Plotter.py","file_ext":"py","file_size_in_byte":8533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6484750004","text":"from django.test import TestCase\n\nfrom accounts.tests import GroupFactory\nfrom fac.models import Budget\nfrom fac.serializers import (\n    ContactCSVSerializer,\n    FolderSerializer,\n    OrganizationCSVSerializer,\n    TypeValorizationSerializer,\n    FolderModelSerializer,\n    ProjectSerializer,\n)\nfrom fac.tests.factories import (\n    ContactFactory,\n    FolderFactory,\n    FolderModelFactory,\n    OrganizationFactory,\n    TypeValorizationFactory,\n    CategoryModelFactory,\n    ProjectFactory,\n)\nfrom .test_fap import InitFapModelMixin\n\n\nclass OrganizationCSVSerializerTestCase(TestCase):\n    def test_csv(self):\n        organizations = [OrganizationFactory() for _ in range(10)]\n        serializer = OrganizationCSVSerializer(organizations)\n        self.assertTrue(\"csv\" in serializer.data)\n        header, organizations_lines = serializer.data[\"csv\"].strip().split(\"\\r\\n\", 1)\n        organizations_lines = organizations_lines.split(\"\\r\\n\")\n        self.assertEqual(len(organizations_lines), 10)\n\n\nclass ContactCSVSerializerTestCase(TestCase):\n    def test_csv(self):\n        contacts = [ContactFactory() for _ in range(10)]\n        serializer = ContactCSVSerializer(contacts)\n        self.assertTrue(\"csv\" in serializer.data)\n        header, contacts_lines = serializer.data[\"csv\"].strip().split(\"\\r\\n\", 1)\n        contacts_lines = contacts_lines.split(\"\\r\\n\")\n        self.assertEqual(len(contacts_lines), 10)\n\n\nclass TypeValorizationSerializerTestCase(TestCase):\n    def test_serialize(self):\n        groups = [GroupFactory() for _ in range(10)]\n        type_valorization = TypeValorizationFactory(groups=groups)\n        serializer = TypeValorizationSerializer(type_valorization)\n        self.assertEquals(\n            {group.pk for group in groups}, set(serializer.data[\"groups\"])\n        )\n\n\nclass FolderSerializerTestCase(InitFapModelMixin, TestCase):\n    def test_serialize(self):\n        contact = ContactFactory()\n        folder = FolderFactory(\n            model=self.folder_model, owning_group=self.user_group, linked_object=contact\n        )\n        serialized = FolderSerializer(folder).data\n        self.assertEquals(self.folder_model.pk, serialized[\"model\"][\"pk\"])\n        self.assertEquals(self.user_group.pk, serialized[\"owning_group\"])\n        self.assertEquals(self.type_valorization_1.pk, serialized[\"type_valorization\"])\n\n\nclass FolderModelSerializerTestCase(TestCase):\n    def test_serialize(self):\n        categories = [CategoryModelFactory() for _ in range(10)]\n        folder_model = FolderModelFactory(categories=categories)\n        serialized = FolderModelSerializer(folder_model).data\n        self.assertEquals(\n            {category.pk for category in categories},\n            {category[\"pk\"] for category in serialized[\"categories\"]},\n        )\n\n\nclass ProjectSerializerTestCase(TestCase):\n    def test_serialize_folder_models(self):\n        type_valorizations = [TypeValorizationFactory() for _ in range(10)]\n        groups = [GroupFactory() for _ in range(10)]\n        project = ProjectFactory(type_valorizations=type_valorizations, groups=groups)\n        folder_model = FolderModelFactory(icon=\"euro-sign\", project=project)\n        FolderFactory(model=folder_model)\n        project.user_budgets = Budget.objects.none()\n        serialized = ProjectSerializer(project).data\n        self.assertEquals(folder_model.icon, serialized[\"folder_models\"][0][\"icon\"])\n","repo_name":"alexandrenorman/mixeur","sub_path":"fac/tests/test_serializers.py","file_name":"test_serializers.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73684992249","text":"teams = {\n  \"Arizona Diamondbacks\": {\n    \"short_name\": \"ARI\",\n    \"name\": \"D-backs\",\n    \"color\": {\n      \"red\": 167,\n      \"green\": 25,\n      \"blue\": 48\n    }\n  },\n  \"Atlanta Braves\": {\n    \"short_name\": \"ATL\",\n    \"name\": \"Braves\",\n    \"color\": {\n      \"red\": 19,\n      \"green\": 39,\n      \"blue\": 79\n    }\n  },\n  \"Baltimore Orioles\": {\n    \"short_name\": \"BAL\",\n    \"name\": \"Orioles\",\n    \"color\": {\n      \"red\": 223,\n      \"green\": 70, \n      \"blue\": 1\n    }\n  },\n  \"Boston Red Sox\": {\n    \"short_name\": \"BOS\",\n    \"name\": \"Red Sox\",\n    \"color\": {\n      \"red\": 189,\n      \"green\": 48,\n      \"blue\": 57\n    }\n  },\n  \"Chicago Cubs\": {\n    \"short_name\": \"CHC\",\n    \"name\": \"Cubs\",\n    \"color\": {\n      \"red\": 14,\n      \"green\": 51,\n      \"blue\": 134\n    }\n  },\n  \"Chicago White Sox\": {\n    \"short_name\": \"CWS\",\n    \"name\": \"White Sox\",\n    \"color\": {\n      \"red\": 39,\n      \"green\": 37,\n      \"blue\": 31\n    }\n  },\n  \"Cincinnati Reds\": {\n    \"short_name\": \"CIN\",\n    \"name\": \"Reds\",\n    \"color\": {\n      \"red\": 198,\n      \"green\": 1,\n      \"blue\": 31\n    }\n  },\n  \"Cleveland Indians\": {\n    \"short_name\": \"CLE\",\n    \"name\": \"Indians\",\n    \"color\": {\n      \"red\": 12,\n      \"green\": 35,\n      \"blue\": 64\n    }\n  },\n  \"Colorado Rockies\": {\n    \"short_name\": \"COL\",\n    \"name\": \"Rockies\",\n    \"color\": {\n      \"red\": 51,\n      \"green\": 0,\n      \"blue\": 111\n    }\n  },        \n  \"Detroit Tigers\": {\n    \"short_name\": \"DET\",\n    \"name\": \"Tigers\",\n    \"color\": {\n      \"red\": 12,\n      \"green\": 35,\n      \"blue\": 64\n    }\n  },        \n  \"Houston Astros\": {\n    \"short_name\": \"HOU\",\n    \"name\": \"Astros\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 45,\n      \"blue\": 98\n    }\n  },        \n  \"Kansas City Royals\": {\n    \"short_name\": \"KC \",\n    \"name\": \"Royals\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 70,\n      \"blue\": 135\n    }\n  },\n  \"Los Angeles Angels\": { \n    \"short_name\":\"LAA\",\n    \"name\": \"Angels\",\n    \"color\": {\n      \"red\": 186,\n      \"green\": 0,\n      \"blue\": 33\n    }\n  },\n  \"Los Angeles Dodgers\": {\n    \"short_name\": \"LAD\",\n    \"name\": \"Dodgers\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 90,\n      \"blue\": 156\n    }\n  },\n  \"Miami Marlins\": {\n    \"short_name\": \"MIA\",\n    \"name\": \"Marlins\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 0,\n      \"blue\": 0\n    }\n  },\n  \"Milwaukee Brewers\": {\n    \"short_name\": \"MIL\",\n    \"name\": \"Brewers\",\n    \"color\": {\n      \"red\": 19,\n      \"green\": 41,\n      \"blue\": 75\n    }\n  },\n  \"Minnesota Twins\": {\n    \"short_name\": \"MIN\",\n    \"name\": \"Twins\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 43,\n      \"blue\": 92\n    }\n  },\n  \"New York Mets\": {\n    \"short_name\": \"NYM\",\n    \"name\": \"Mets\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 45,\n      \"blue\": 114\n    }\n  },\n  \"New York Yankees\": {\n    \"short_name\": \"NYY\",\n    \"name\": \"Yankees\",\n    \"color\": {\n      \"red\": 12,\n      \"green\": 35,\n      \"blue\": 64\n    }\n  },\n  \"Oakland Athletics\": {\n    \"short_name\": \"OAK\",\n    \"name\": \"Athletics\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 56,\n      \"blue\": 49\n    }\n  },\n  \"Philadelphia Phillies\": {\n    \"short_name\": \"PHI\",\n    \"name\": \"Phillies\",\n    \"color\": {\n      \"red\": 232,\n      \"green\": 24,\n      \"blue\": 40\n    }\n  },\n  \"Pittsburgh Pirates\": {\n    \"short_name\": \"PIT\",\n    \"name\": \"Pirates\",\n    \"color\": {\n      \"red\": 39,\n      \"green\": 37,\n      \"blue\": 37\n    }\n  },\n  \"San Diego Padres\": {\n    \"short_name\": \"SD \",\n    \"name\": \"Padres\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 45,\n      \"blue\": 98\n    }\n  },\n  \"San Francisco Giants\": {\n    \"short_name\": \"SF \",\n    \"name\": \"Giants\",\n    \"color\": {\n      \"red\": 39,\n      \"green\": 37,\n      \"blue\": 31\n    }\n  },\n  \"Seattle Mariners\": {\n    \"short_name\": \"SEA\",\n    \"name\": \"Mariners\",\n    \"color\": {\n      \"red\": 12,\n      \"green\": 44,\n      \"blue\": 86\n    }\n  },\n  \"St. Louis Cardinals\": {\n    \"short_name\": \"STL\",\n    \"name\": \"Cardinals\",\n    \"color\": {\n      \"red\": 196,\n      \"green\": 30,\n      \"blue\": 58\n    }\n  },\n  \"Tampa Bay Rays\": {\n    \"short_name\": \"TB \",\n    \"name\": \"Rays\",\n    \"color\": {\n      \"red\": 9,\n      \"green\": 44,\n      \"blue\": 92\n    }\n  },\n  \"Texas Rangers\": {\n    \"short_name\": \"TEX\",\n    \"name\": \"Rangers\",\n    \"color\": {\n      \"red\": 0,\n      \"green\": 50,\n      \"blue\": 120\n    }\n  },\n  \"Toronto Blue Jays\": {\n    \"short_name\": \"TOR\",\n    \"name\": \"Blue Jays\",\n    \"color\": {\n      \"red\": 19,\n      \"green\": 74,\n      \"blue\": 142\n    }\n  },\n  \"Washington Nationals\": {\n    \"short_name\": \"WSH\",\n    \"name\": \"Nationals\",\n    \"color\": {\n      \"red\": 171,\n      \"green\": 0,\n      \"blue\": 3\n    }\n  }\n}\n\n","repo_name":"ty-porter/MiniTron","sub_path":"teams.py","file_name":"teams.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4874594078","text":"import csv\n\nwith open('FL_insurance_sample.csv', 'r') as old:\n    read = csv.reader(old)\n    list1 = []\n    for data in read:\n        list1.append(data)\n\nold.close()\nwith open('new_file.csv','w') as new:\n    write = csv.writer(new)\n    for a in list1:\n        write.writerow(a[2:18:3])\nnew.close()","repo_name":"Prajakta3/Python_Exercise","sub_path":"q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72064951290","text":"from unittest.mock import MagicMock, patch, call\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport pywatts.summaries\nfrom pywatts.summaries.train_synthetic_test_real import TrainSyntheticTestReal, TSTRTask\n\n\nclass TestTSTRSummary(TestCase):\n    def setUp(self) -> None:\n        self.get_model_mock = MagicMock()\n        self.tstr = TrainSyntheticTestReal(repetitions=42, get_model=self.get_model_mock)\n\n    def tearDown(self) -> None:\n        self.tstr = None\n\n    def test_get_set_params(self):\n        new_model = MagicMock()\n        self.assertEqual(\n            self.tstr.get_params(),\n            {\"repetitions\": 42, \"get_model\": self.get_model_mock, \"train_test_split\": 0.66, \"task\": TSTRTask.Regression,\n             \"metrics\": [\"rmse\", \"mae\"],\n             \"name\":\"TSTR\",\"fit_kwargs\": {\"epochs\": 100, \"validation_split\": 0.2}, \"n_targets\": 1}\n        )\n        self.tstr.set_params(n_targets=42, get_model=new_model, train_test_split=0.8, fit_kwargs={}, repetitions=1,\n                             task=TSTRTask.Classification, metrics=[\"accuracy\"])\n        self.assertEqual(\n            self.tstr.get_params(),\n            {\"repetitions\": 1, \"get_model\": new_model, \"train_test_split\": 0.8,\n             \"fit_kwargs\": {},\"name\":\"TSTR\", \"n_targets\": 42, \"metrics\": [\"accuracy\"], \"task\": TSTRTask.Classification}\n        )\n\n    def test_transform(self):\n        regressor_mock = MagicMock()\n        model_mock = MagicMock()\n        model_mock.predict.return_value = np.array([2, 0])\n        regressor_mock.return_value = model_mock\n        self.tstr.set_params(get_model=regressor_mock)\n\n        time = pd.to_datetime(['2015-06-03 00:00:00', '2015-06-03 01:00:00',\n                               '2015-06-03 02:00:00', '2015-06-03 03:00:00',\n                               '2015-06-03 04:00:00'])\n\n        test_data = xr.Dataset({\"testCol\": ([\"time\", \"horizon\"], xr.DataArray([[-2], [-1], [0], [1], [2]]).data),\n                                \"predictCol1\": ([\"time\", \"horizon\"], xr.DataArray([[2], [-3], [3], [1], [-2]]).data),\n                                \"predictCol2\": ([\"time\", \"horizon\"], xr.DataArray([[-2], [-1], [0], [1], [2]]).data),\n                                \"time\": time})\n\n        fm_mock = MagicMock()\n\n        self.tstr.transform(file_manager=fm_mock, real=test_data['testCol'],\n                            pred1=test_data['predictCol1'], pred2=test_data['predictCol2'])\n\n        self.assertEqual(regressor_mock.call_count, 3 * 42)\n        model_mock.fit.assert_called()\n        model_mock.predict.assert_called()\n\n    @patch('pywatts.summaries.train_synthetic_test_real.cloudpickle')\n    @patch(\"builtins.open\")\n    def test_save_load(self, open_mock, cloudpickle_mock):\n        json = self.tstr.save(fm=MagicMock())\n\n        reloaded = TrainSyntheticTestReal.load(json)\n        params = self.tstr.get_params()\n\n        params[\"get_model\"] = cloudpickle_mock.load()\n        params[\"fit_kwargs\"] = cloudpickle_mock.load()\n\n        self.assertEqual(\n            params,\n            reloaded.get_params()\n        )\n\n        self.assertEqual(cloudpickle_mock.dump.call_count, 2)\n","repo_name":"KIT-IAI/pyWATTS","sub_path":"tests/unit/summaries/test_tstr.py","file_name":"test_tstr.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"77"}
+{"seq_id":"27063198962","text":"from ..merger import DeviceMerger\n\nstm_peripherals = \\\n{\n    'adc': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-f0',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['f0']}]\n            },{\n                'hardware': 'stm32-l0',\n                'features': ['oversampler', 'calfact', 'prescaler'],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['l0']}]\n            },{\n                'hardware': 'stm32-g0',\n                'features': ['oversampler', 'calfact', 'prescaler'],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['g0']}]\n            },{\n                # F373 & F378 has a non-special ADC\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['f3'], 'name': ['73', '78']}]\n            },{\n                'hardware': 'stm32-f3',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['f3', 'l4', 'l5', 'g4', 'wb']}]\n            },{\n                'hardware': 'stm32-h7',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['h7']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'sdadc': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-f3',\n                'features': [],\n                'protocols': ['analog-in'],\n                'devices': [{'family': ['f3']}]\n            }\n        ]\n    }],\n    'can': [{\n        'instances': '*',\n        'groups': [\n            {\n                # 14 shared filters\n                'hardware': 'stm32',\n                'features': ['filter-14'],\n                'protocols': ['can-v2.0a', 'can-v2.0b'],\n                'devices': [{'family': ['f0', 'g0', 'f1']}]\n            },{\n                # 28 shared filters\n                'hardware': 'stm32',\n                'features': ['filter-28'],\n                'protocols': ['can-v2.0a', 'can-v2.0b'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'fdcan': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-h7',\n                'features': [],\n                'protocols': [],\n                'devices': [{'family': ['h7']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'crc': [{\n        'instances': '*',\n        'groups': [\n            {\n                # Custom polynomial and reverse data\n                'hardware': 'stm32',\n                'features': ['polynomial', 'reverse'],\n                'protocols': ['crc32'],\n                'devices': [{'family': ['f0', 'f3', 'f7', 'h7', 'l5', 'u5']}]\n            },{\n                # Custom polynomial and reverse data\n                'hardware': 'stm32',\n                'features': ['reverse'],\n                'protocols': ['crc32'],\n                'devices': [{'family': ['g0', 'g4']}]\n            },{\n                # no poly size\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['crc32'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'dma': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-mux',\n                'features': [],\n                'protocols': ['mem2mem', 'mem2per', 'per2per'],\n                'devices': [{'family': ['g0', 'g4', 'l5', 'wb', 'wl']}, {'family': ['l4'], 'name': ['p5', 'p7', 'p9', 'q5', 'q7', 'q9', 'r5', 'r7', 'r9', 's5', 's7', 's9']}]\n            },\n            {\n                'hardware': 'stm32-mux-stream',\n                'features': [],\n                'protocols': ['mem2mem', 'mem2per', 'per2per'],\n                'devices': [{'family': ['h7']}]\n            },\n            {\n                'hardware': 'stm32-stream-channel',\n                'features': [],\n                'protocols': ['mem2mem', 'mem2per', 'per2per'],\n                'devices': [{'family': ['f2', 'f4', 'f7']}]\n            },\n            {\n                'hardware': 'stm32-channel-request',\n                'features': [],\n                'protocols': ['mem2mem', 'mem2per', 'per2per'],\n                'devices': [{'family': ['l0', 'l4']}, {'family': ['f0'], 'name': ['91', '98']}, {'family': ['f0'], 'name': ['30'], 'size': ['c']}]\n            },\n            {\n                'hardware': 'stm32-channel',\n                'features': [],\n                'protocols': ['mem2mem', 'mem2per', 'per2per'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'iwdg': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': ['window'],\n                'protocols': [],\n                'devices': [{'family': ['f0', 'f3', 'f7', 'g0', 'g4', 'l5', 'u5', 'wb']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'spi': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': ['data-size', 'nss-pulse', 'fifo'],\n                'protocols': [],\n                'devices': [{'family': ['f0', 'g0', 'f3', 'f7', 'l4', 'l5', 'g4', 'wb']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': [],\n                'protocols': [],\n                'devices': [{'family': ['h7', 'u5']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'dac': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': [{'family': ['f1']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['status'],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'dcmi': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'dsi': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'sdio': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'tim': [\n        {\n            'instances': ['1', '8', '20'],\n            'groups': [\n                {\n                    'hardware': 'stm32-advanced',\n                    'features': [],\n                    'protocols': [],\n                    'devices': '*'\n                }\n            ]\n        },{\n            'instances': ['2', '3', '4', '5', '19', '23', '24'],\n            'groups': [\n                {\n                    'hardware': 'stm32-general-purpose',\n                    'features': [],\n                    'protocols': [],\n                    'devices': '*'\n                }\n            ]\n        },{\n            'instances': ['9', '10', '11', '12', '13', '14', '15', '16', '17', '21', '22'],\n            'groups': [\n                {\n                    'hardware': 'stm32-general-purpose',\n                    'features': [],\n                    'protocols': [],\n                    'devices': '*'\n                }\n            ]\n        },{\n            'instances': ['6', '7', '18'],\n            'groups': [\n                {\n                    'hardware': 'stm32-basic',\n                    'features': [],\n                    'protocols': [],\n                    'devices': '*'\n                }\n            ]\n        }\n    ],\n    'sys': [{\n        'instances': '*',\n        'groups': [\n            {\n                # Registers are called AFIO, not SYS!\n                'hardware': 'stm32-f1',\n                'features': ['exti', 'remap'],\n                'protocols': [],\n                'devices': [{'family': ['f1']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['exti', 'fpu', 'ccm-wp', 'cfgr2'],\n                'protocols': [],\n                'devices': [{'family': ['f3', 'g4']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['exti', 'sram2-wp', 'cfgr2', 'imr'],\n                'protocols': [],\n                'devices': [{'family': ['l5', 'wb']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['exti', 'cfgr2', 'itline'],\n                'protocols': [],\n                'devices': [{'family': ['f0'], 'name': ['91', '98']}, {'family': ['g0']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['exti', 'cfgr2'],\n                'protocols': [],\n                'devices': [{'family': ['f0']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['exti'],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'dma2d': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['2d', 'blitter'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'rng': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': [],\n                'devices': '*'\n            }\n        ]\n    }],\n    'i2c': [\n        {\n            # F1/F2/F4/L1 standard I2C with SMBus support\n            'instances': '*',\n            'groups': [\n                {\n                    # Some F4 have a digital noise filter\n                    'hardware': 'stm32',\n                    'features': ['dnf'],\n                    'protocols': ['i2c-v3.0', 'smb-v2.0', 'pmb-v1.1'],\n                    'devices': [{'family': ['f4'], 'name': ['27', '29', '37', '39', '46', '69', '79']}]\n                },{\n                    'hardware': 'stm32',\n                    'features': [],\n                    'protocols': ['i2c-v3.0', 'smb-v2.0', 'pmb-v1.1'],\n                    'devices': [{'family': ['f1', 'f2', 'f4', 'l1']}]\n                }\n            ]\n        },{\n            # F0/F3/F7/L0/L4/L4+/H7 extended I2C instance 2 with optional FM+ and SMBus support\n            'instances': ['2'],\n            'groups': [\n                {\n                    # This hardware supports neither FM+ (1 Mhz) nor SMBus\n                    'hardware': 'stm32-extended',\n                    'features': ['dnf'],\n                    'protocols': ['i2c-v3.0'],\n                    'devices': [\n                        {\n                            'family': ['f0'],\n                            'name': ['30', '31', '38', '51', '58']\n                        },{\n                            'family': ['f0'],\n                            'name': ['70'],\n                            'size': ['b']\n                        }\n                    ]\n                },{\n                    # This hardware supports FM+ (1 Mhz) but not SMBus\n                    'hardware': 'stm32-extended',\n                    'features': ['dnf', 'fmp'],\n                    'protocols': ['i2c-v3.0'],\n                    'devices': [{'family': ['f0', 'g0', 'l0']}]\n                },{\n                    # This hardware supports FM+ (1 Mhz) and SMBus\n                    'hardware': 'stm32-extended',\n                    'features': ['dnf', 'fmp'],\n                    'protocols': ['i2c-v3.0', 'smb-v2.0', 'pmb-v1.1'],\n                    'devices': [{'family': ['f3', 'f7', 'l4', 'l5', 'h7', 'g4', 'u5', 'wb']}]\n                }\n            ]\n        },{\n            # F0/F3/F7/L0/L4/L4+/H7 extended I2C with FM+ and SMBus support\n            'instances': ['1', '3', '4'],\n            'groups': [\n                {\n                    # This hardware supports FM+ (1 Mhz) and SMBus\n                    'hardware': 'stm32-extended',\n                    'features': ['dnf', 'fmp'],\n                    'protocols': ['i2c-v3.0', 'smb-v2.0', 'pmb-v1.1'],\n                    'devices': [{'family': ['f0', 'g0', 'f3', 'f7', 'l0', 'l4', 'l5', 'h7', 'g4', 'u5', 'wb']}]\n                }\n            ]\n        }\n    ],\n    'uart': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-extended',\n                'features': ['wakeup'],\n                'protocols': ['uart'],\n                'devices': [{'family': ['f0', 'f3']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': ['tcbgt'],\n                'protocols': ['uart'],\n                'devices': [{'family': ['l4'], 'name': ['p5', 'p7', 'p9', 'q5', 'q7', 'q9', 'r5', 'r7', 'r9', 's5', 's7', 's9']}, {'family': ['g0', 'g4', 'wb', 'h7', 'l5', 'u5']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': [],\n                'protocols': ['uart'],\n                'devices': [{'family': ['f7', 'l4']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['over8'],\n                'protocols': ['uart'],\n                'devices': [{'family': ['f2', 'f4']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['uart'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'usart': [{\n        'instances': '*',\n        'groups': [\n            {\n                'hardware': 'stm32-extended',\n                'features': ['wakeup'],\n                'protocols': ['uart', 'spi'],\n                'devices': [{'family': ['f0', 'f3']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': ['tcbgt'],\n                'protocols': ['uart', 'spi'],\n                'devices': [{'family': ['l4'], 'name': ['p5', 'p7', 'p9', 'q5', 'q7', 'q9', 'r5', 'r7', 'r9', 's5', 's7', 's9']}, {'family': ['g0', 'g4', 'wb', 'h7', 'l5', 'u5']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': [],\n                'protocols': ['uart', 'spi'],\n                'devices': [{'family': ['f7', 'l4']}]\n            },{\n                'hardware': 'stm32-extended',\n                'features': ['over8'],\n                'protocols': ['uart', 'spi'],\n                'devices': [{'family': ['l0']}]\n            },{\n                'hardware': 'stm32',\n                'features': ['over8'],\n                'protocols': ['uart', 'spi'],\n                'devices': [{'family': ['f2', 'f4']}]\n            },{\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['uart', 'spi'],\n                'devices': '*'\n            }\n        ]\n    }],\n    'gpio': [{\n        'instances': '*',\n        'groups': [\n            {\n                # The F1 remaps groups of pins\n                'hardware': 'stm32-f1',\n                'features': [],\n                'protocols': ['digital-in', 'digital-out', 'open-drain', 'exti'],\n                'devices': [{'family': ['f1']}]\n            },{\n                # The rest remaps pins individually\n                'hardware': 'stm32',\n                'features': [],\n                'protocols': ['digital-in', 'digital-out', 'open-drain', 'exti'],\n                'devices': '*'\n            }\n        ]\n    }]\n}\n\ndef getPeripheralData(did, module):\n    name, inst, version = module\n    if name in stm_peripherals:\n        for instance_list in stm_peripherals[name]:\n            if instance_list['instances'] == '*' or inst[len(name):] in instance_list['instances']:\n                for group in instance_list['groups']:\n                    if group['devices'] == '*' or DeviceMerger._get_index_for_id(group['devices'], did) >= 0:\n                        return (group['hardware'], group['features'], group['protocols'])\n\n    return ('stm32-' + version, [], [])\n","repo_name":"modm-io/modm-devices","sub_path":"tools/generator/dfg/stm32/stm_peripherals.py","file_name":"stm_peripherals.py","file_ext":"py","file_size_in_byte":16720,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"77"}
+{"seq_id":"42447018436","text":"\"\"\"Support for reading data over i2c.\"\"\"\nfrom time import time\n\nfrom expliot.core.protocols.hardware.i2c import I2cEepromManager\nfrom expliot.core.interfaces.ftdi import DEFAULT_FTDI_URL\nfrom expliot.core.tests.test import TCategory, Test, TLog, \\\n    TTarget, LOGNO\nfrom expliot.plugins.i2c import DEFAULT_ADDR\n\nDESCRIPTION = \"\"\"\nThis plugin reads data from an I2C EEPROM chip. It needs an FTDI interface to\nread data from the target EEPROM chip. You can buy an FTDI device online. If you\nare interested we have an FTDI based product - 'EXPLIoT Nano' which you can\norder online from www.expliot.io This plugin uses pyi2cflash package which in\nturn uses pyftdi python driver for ftdi chips. For more details on supported\nI2C EEPROM chips, check the readme at https://github.com/eblot/pyi2cflash Thank\nyou Emmanuel Blot for pyi2cflash. You may want to run it as root in case you\nget a USB error related to langid.\"\"\"\n\n\n# pylint: disable=bare-except\nclass I2cEepromRead(Test):\n    \"\"\"\n    Plugin to read data over i2c.\n\n    Output Format:\n    There are two types of output format -\n    1. When the read data is stored in a file (--wfile argument).\n    2. When the read data has to be displayed instead of storing in a file.\n\n    [\n        {\n            \"data\": \"Foobar data\", # Data read from the chip, this field is present\n                                   # if --wfile is not specified\n        },\n        {\n            chip_size=32768, # Size of the chip in bytes\n            bytes_read=1000,\n            time_taken_secs=1.67,\n        }\n    ]\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize the test.\"\"\"\n        super().__init__(\n            name=\"readeeprom\",\n            summary=\"I2C EEPROM Reader\",\n            descr=DESCRIPTION,\n            author=\"Aseem Jakhar\",\n            email=\"aseemjakhar@gmail.com\",\n            ref=[\"https://github.com/eblot/pyi2cflash\"],\n            category=TCategory(TCategory.I2C, TCategory.HW, TCategory.ANALYSIS),\n            target=TTarget(TTarget.GENERIC, TTarget.GENERIC, TTarget.GENERIC),\n        )\n\n        self.argparser.add_argument(\n            \"-a\",\n            \"--addr\",\n            default=DEFAULT_ADDR,\n            type=int,\n            help=\"Specify the start address from where data is to be \"\n                 \"read. Default is {}\".format(DEFAULT_ADDR),\n        )\n        self.argparser.add_argument(\n            \"-l\",\n            \"--length\",\n            type=int,\n            help=\"Specify the total length of data, in bytes, to be read from \"\n                 \"the start address. If not specified, it reads till the end\",\n        )\n        self.argparser.add_argument(\n            \"-u\",\n            \"--url\",\n            default=DEFAULT_FTDI_URL,\n            help=\"URL of the connected FTDI device. Default is {}. \"\n            \"For more details on the URL scheme check \"\n            \"https://eblot.github.io/pyftdi/urlscheme.html\".format(DEFAULT_FTDI_URL),\n        )\n        self.argparser.add_argument(\n            \"-c\",\n            \"--chip\",\n            required=True,\n            help=\"Specify the chip. Supported chips are 24AA32A, 24AA64, \"\n            \"24AA128, 24AA256, 24AA512\",\n        )\n        self.argparser.add_argument(\n            \"-w\",\n            \"--wfile\",\n            help=\"Specify the file path where data, read from the i2c chip, \"\n            \"is to be written. If not specified output the data on the terminal\",\n        )\n\n        self.slaveaddr = 0x50\n\n    def execute(self):\n        \"\"\"Execute the test.\"\"\"\n        TLog.generic(\n            \"Reading data from i2c eeprom at address({}) using device({})\".format(\n                self.args.addr, self.args.url\n            )\n        )\n        device = None\n        try:\n            device = I2cEepromManager.get_flash_device(\n                self.args.url, self.args.chip, address=self.slaveaddr\n            )\n            length = self.args.length or (len(device) - self.args.addr)\n            TLog.trydo(\n                \"Reading {} bytes from start address {}\".format(length, self.args.addr)\n            )\n            if self.args.addr + length > len(device):\n                raise IndexError(\"Length is out of range of the chip size\")\n            start_time = time()\n            data = device.read(self.args.addr, length)\n            end_time = time()\n            if self.args.wfile:\n                TLog.trydo(\"Writing data to the file ({})\".format(self.args.wfile))\n                output_file = open(self.args.wfile, \"w+b\")\n                output_file.write(data)\n                output_file.close()\n            else:\n                self.output_handler(msg=\"data: {}\".format([hex(x) for x in data]),\n                                    logkwargs=LOGNO,\n                                    data=data)\n            self.output_handler(chip_size=len(device),\n                                bytes_read=len(data),\n                                time_taken_secs=round(end_time - start_time, 2))\n        except:  # noqa: E722\n            self.result.exception()\n        finally:\n            I2cEepromManager.close(device)\n","repo_name":"expliot-framework/expliot","sub_path":"expliot/plugins/i2c/i2ceepromread.py","file_name":"i2ceepromread.py","file_ext":"py","file_size_in_byte":5056,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"37355940508","text":"import requests\nimport urllib.request\nfrom bs4 import BeautifulSoup\nimport re\nimport os\nimport json\nimport pymongo\nimport csv\nimport smtplib\nfrom email.message import EmailMessage\nfrom datetime import datetime, timedelta\nimport time\nfrom Create_Email_Content import Create_Email_Content\nfrom Constnts import Constnts\nfrom scrape_website import scrape_website\n\n### TODO ætti að breyta þessu í margar skrár\n### TODO það þirfti að laga öll breytunöfn hafa samræmi þetta er sick\n\n\n## TODO þarf að gera enviroment breytur\n\n\nclass User(object):\n    def __init__(self, email, min_price, max_price, min_rooms, zip_codes, best_search_pricecutof):\n        self.email = email\n        self.min_price = min_price\n        self.max_price = max_price\n        self.min_rooms = min_rooms\n        self.zip_codes = zip_codes\n        self.best_search_pricecutof = best_search_pricecutof\n    def getUsername(self):\n        #TODO láta þetta skil emailinu - email eða eitthvað\n        username = re.sub(r'@.+', '', self.email)\n        username = re.sub(r'\\.', '', username)\n        return username\n\n\n##################################################################\n#  Data_manipulation_functions\n##################################################################\n### TODO breyta þessu i klasa\ndef sort_by_price( item ):\n    return item['price']\ndef sort_by_sqaremeter(item):\n    return item['squaremeter']\ndef sort_by_sqaremeter_price(item):\n    return item['squareMeter_price']\ndef find_lowest_prices( data, index):\n    data.sort(key=sort_by_price)\n    return data[:index]\ndef find_cheepes_squaremeter( data, index ):\n    data.sort(key=sort_by_sqaremeter_price)\n    return data[:index]\ndef find_avrage_price(data):\n    totalSum = 0\n    for item in data:\n        totalSum = totalSum + item['price']\n    if totalSum == 0:\n        return -1\n    if len(data) == 0:\n        return -1\n    return totalSum / len(data)\ndef find_avrage_squaremeter_price(data):\n    totalSum = 0\n    for item in data:\n        totalSum = totalSum + item['squareMeter_price']\n    return totalSum / len(data)\n\ndef find_biggest_propertys(data,index):\n    data.sort(key=sort_by_sqaremeter,reverse=True)\n    return data[:index]\n\ndef find_Best_Priced(data, scope_amount):\n    i = 0\n    scope_index = int(len(data) / 10)\n    if(scope_index < 5 ):\n        scope_index = 5\n    intresting_appartments = [{}]\n    intresting_appartments.pop()\n\n    data.sort(key=sort_by_sqaremeter,reverse=True)\n    while i < len(data) - scope_index-1:\n        avg = find_avrage_squaremeter_price(data[i:scope_index+i])\n        if( data[i+scope_index+1]['squareMeter_price'] - avg < -scope_amount):\n            data[i+scope_index+1]['Price Below Avrage'] = data[i+scope_index+1]['squareMeter_price'] - avg\n            intresting_appartments.append(data[i+scope_index+1])\n        i += 1\n    ## todo ath þetta fall skippar fyrstu 5 (scope_index) fasteignum þarf að búa til loopu sem skoðar þær\n    return intresting_appartments\n\n##################################################################\n#  Data_gathering exporting\n##################################################################\ndef update_array_in_row(row_id,data, connection_string, database, collection ):\n    mng_client = pymongo.MongoClient(connection_string)\n    mng_db = mng_client[database]\n    db_cm = mng_db[collection]\n    db_cm.update_one({'_id': row_id },\n                    { '$push': { 'new items today' : data } } )\n\n\ndef add_row_to_database(table_row, connection_string, database, collection ):\n    mng_client = pymongo.MongoClient(connection_string)\n    mng_db = mng_client[database]\n    db_cm = mng_db[collection]\n    db_cm.insert_one(table_row)\ndef compare_two_days(d1,d2):\n    d1_id = []\n    for dataset in d1:\n        if len(dataset) == 0:\n            continue\n        for d1_item in dataset[0]['best priced']:\n            d1_id.append(d1_item['id'])\n    d2_id = []\n    for dataset2 in d2:\n        if len(dataset2) == 0:\n            continue\n        for d2_item in dataset2[0]['best priced']:\n            d2_id.append(d2_item['id'])\n    if d1_id == d2_id:\n        return True\n    else:\n        return False\ndef Get_rows_from_today(connection_string, database, collection, today):\n\n    start = datetime(today.year, today.month, today.day, 0, 0)\n    end = datetime(today.year, today.month, today.day, 23, 59)\n\n    mng_client = pymongo.MongoClient(connection_string)\n    mng_db = mng_client[database]\n    db_cm = mng_db[collection]\n    ret = list(db_cm.find({ \"time\": {'$lt': end, '$gte': start}}))\n    return ret\n\ndef print_to_console(data):\n    print(generate_generic_text(data))\ndef generate_generic_text(data):\n    retString = ''\n    retString += data['City'] + '\\n'\n    retString += 'Meðalverð : ' + str(int(data['avrage price'])) + ' kr' + '\\n'\n    retString += '\\n'\n    retString += 'Fann ' + str(len(data['best priced'])) + ' íbúðir :' + '\\n'\n    retString += '------------------------------' + '\\n'\n    for item in data['best priced']:\n        retString += item['street_Address'] + ', ' + item['rooms'] + '\\n'\n        retString += 'Verð - ' + str(int(item['price'])) + ' kr, stærð - ' + str(int(item['squaremeter'])) + ' m2' + '\\n'\n        retString += 'Fermetraverð : ' + str(int(item['squareMeter_price'])) + ' kr/m2' + '\\n'\n        retString += 'hlekkur : '  + item['link']  + '\\n'\n        retString += '\\n'\n    retString += '------------------------------' + '\\n'\n    retString += 'Leitarskilyrði' + '\\n'\n    retString += 'verðbil : ' + str(int(data['search term price'][0])) + \" til \" + str(int(data['search term price'][1])) + \" kr\" + '\\n'\n    retString += 'herbergi : ' + str(int(data['search term min rooms'])) + '\\n'\n    return retString\ndef send_email(content, email, zip_code):\n    EMAIL_ADDRESS = Constnts.EMAIL_ADDRESS\n    EMAIL_PASSWORD = Constnts.EMAIL_PASSWORD\n    msg = EmailMessage()\n    msg['Subject'] = zip_code \n    msg['From'] = 'ekkisvara69@gmail.com'\n    msg['To'] = email\n    msg.set_content( content )\n\n    msg.add_alternative(content, subtype='html')\n\n    with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n        smtp.login(EMAIL_ADDRESS,EMAIL_PASSWORD)\n        smtp.send_message(msg)\n\ndef get_data_from_site( min_price, max_price, min_rooms, zip_code):\n    site_request = [\n        'http://fasteignir.visir.is/ajaxsearch/getresults?stype=sale&zip='+ str(zip_code) +'&category=1,2,4,7&price=' + str(min_price) +',' + str(max_price) + '&room='+str(min_rooms)+',10&itemcount=60&page=1',\n        'http://fasteignir.visir.is/ajaxsearch/getresults?stype=sale&zip='+ str(zip_code) +'&category=1,2,4,7&price=' + str(min_price) +',' + str(max_price) + '&room='+str(min_rooms)+',10&itemcount=60&page=2',\n        'http://fasteignir.visir.is/ajaxsearch/getresults?stype=sale&zip='+ str(zip_code) +'&category=1,2,4,7&price=' + str(min_price) +',' + str(max_price) + '&room='+str(min_rooms)+',10&itemcount=60&page=3',\n        'http://fasteignir.visir.is/ajaxsearch/getresults?stype=sale&zip='+ str(zip_code) +'&category=1,2,4,7&price=' + str(min_price) +',' + str(max_price) + '&room='+str(min_rooms)+',10&itemcount=60&page=4',\n        'http://fasteignir.visir.is/ajaxsearch/getresults?stype=sale&zip='+ str(zip_code) +'&category=1,2,4,7&price=' + str(min_price) +',' + str(max_price) + '&room='+str(min_rooms)+',10&itemcount=60&page=5'\n    ]\n    site_data = []\n    for req in site_request:\n        res = requests.get(req).text\n        data = scrape_website(res)\n        if len(data) == 0:\n            break\n        site_data = site_data + data\n        ## TODO Sleepa hér til að vera ekki að spamma visir.is\n        time.sleep(1)\n    return site_data\n\nstart_time = time.time()\nprint( str(datetime.today()) + ' Start')\n\n\nuser_list = []\n\nfor user in user_list:\n    content = Create_Email_Content()\n    content_was_added = False\n    all_today_rows = []\n    all_yesterday_rows = []\n    for zip_code in user.zip_codes:\n        username = user.getUsername()\n        today = datetime.today()\n        yesterday = today - timedelta(days = 1)\n        today_rows = Get_rows_from_today(Constnts.DB_CONNECTION_STRING, username, zip_code , today)\n\n        all_yesterday_rows.append(Get_rows_from_today(Constnts.DB_CONNECTION_STRING, username, zip_code , yesterday))\n\n        site_data = get_data_from_site( user.min_price, user.max_price, user.min_rooms, zip_code )\n        if len(site_data) == 0:\n            print( str(datetime.today()) + ' no items in ' + user.getUsername() + ' ' + str(zip_code))\n            continue\n        ## I have already added to db today so i will check if there is something new\n        if len(today_rows) >= 1:\n            print( str(datetime.today()) + ' already added to db '+ user.getUsername() + ' ' + str(zip_code) + ' today.')\n            db_items = today_rows[0]['best priced']\n            site_items = find_Best_Priced(site_data, user.best_search_pricecutof)\n            ## TODO ójj?? \n            db_items_id = []\n            for item in db_items:\n                db_items_id.append(item['id'])\n            not_same = []\n            for i in range(0,len( site_items ),1):\n                if site_items[i]['id'] not in db_items_id:\n                    not_same.append(site_items[i])\n            if(len(not_same) != 0):\n                if 'new items today' in today_rows[0].keys():\n                    for item in today_rows[0]['new items today']:\n                        site_items_ids = []\n                        for id in site_items:\n                            site_items_ids.append(int(id['id']))\n                        if int(item['id']) in site_items_ids:\n                            print( str(datetime.today()) + ' This item is not new ' + user.getUsername() + ' ' + str(zip_code) + ' ')\n                        else:\n                            print( str(datetime.today()) + ' this item is new i will add to new items and send email to ' + user.getUsername() + ' ' + str(zip_code) + ' ')\n                            header = { 'City': 'Update in ' + str(not_same[0]['post_number']), 'avrage price': -100 }\n                            content.add_header(header)\n                            content.add_contend(not_same)\n                            content_was_added = True\n                            update_array_in_row(today_rows[0]['_id'], item, Constnts.DB_CONNECTION_STRING, user.getUsername(), str(zip_code) )\n                else:\n                    print( str(datetime.today()) + ' this item is new i will add to new items and send email to ' + user.getUsername() + ' ' + str(zip_code) + ' ')\n\n                    header = { 'City': 'Update in ' + str(not_same[0]['post_number']), 'avrage price': -100 }\n                    content.add_header(header)\n                    content.add_contend(not_same)\n                    content_was_added = True\n                    for item in not_same:\n                        update_array_in_row(today_rows[0]['_id'], item, Constnts.DB_CONNECTION_STRING, user.getUsername(), str(zip_code) )\n        ## I will add to db\n        else:\n            print( str(datetime.today()) + ' adding to database '+ user.getUsername() + ' ' + str(zip_code) )\n            avrage_price = find_avrage_price(site_data)\n            best_priced = find_Best_Priced( site_data, user.best_search_pricecutof )\n            tableRow = {\n                'time' : datetime.today(),\n                'No of appartments': str(len(site_data)),\n                'City' : zip_code,\n                'search term price': [user.min_price,user.max_price],\n                'search term min rooms': 3,\n                'avrage price': avrage_price,\n                'best priced': best_priced\n            }\n            add_row_to_database(tableRow, Constnts.DB_CONNECTION_STRING, user.getUsername(), str(zip_code) )\n            header = { \n                'City': tableRow['City'],\n                'avrage price': tableRow['avrage price'] \n            }\n            content.add_header(header)\n            content.add_contend(tableRow['best priced'])\n            content_was_added = True\n            print( str(datetime.today()) + ' added new row to database ' + user.getUsername() + ' ' + str(zip_code))\n        all_today_rows.append(Get_rows_from_today(Constnts.DB_CONNECTION_STRING, username, zip_code , today))\n    if content_was_added is True and compare_two_days(all_today_rows,all_yesterday_rows) is False :\n        if not os.path.exists('send_email_temps'):\n            os.makedirs('send_email_temps')\n        f = open( 'send_email_temps/' + user.getUsername() + '.html', 'w' )\n        f.write(content.Html_Content())\n        f.close()\n        send_email(content.Html_Content(), user.email, 'Vel verðsettar íbúðir að skoða')\n        print( str(datetime.today()) + ' Email sent to ' + user.getUsername() )\n\nend_time = time.time()\nprint( str(datetime.today()) + ' Success exec time : %s Secounds' % (end_time - start_time) )\n","repo_name":"Stefanorn/Fasteigna-Leit","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21988524764","text":"import os\nimport uuid\nimport contextlib\nimport tempfile\n\nfrom baklava import pypi\n\n\n@contextlib.contextmanager\ndef pushenv():\n    original = os.environ.copy()\n    yield\n    os.environ = original\n\n\ndef test_load_env():\n\n    with pushenv():\n        # Set environment variables (Top Precedence)\n        index = str(uuid.uuid4())\n        extra = str(uuid.uuid4())\n        os.environ['PIP_INDEX_URL'] = index\n        os.environ['PIP_EXTRA_INDEX_URL'] = extra\n\n        # API Call\n        config = pypi.load()\n\n        # Assertions\n        assert config['index-url'] == index\n        assert config['extra-index-url'] == extra\n\n\ndef test_invalid_config_path():\n    with pushenv():\n        filename = str(uuid.uuid4())\n        os.environ[\"PIP_CONFIG_FILE\"] = filename\n        config = pypi.load()\n        assert config['config-file'] == filename\n\n\ndef test_invalid_config_content():\n\n    temp = tempfile.NamedTemporaryFile()\n\n    with open(temp.name, 'wb') as handle:\n        handle.write(b\"\\001\")\n\n    with pushenv():\n        os.environ[\"PIP_CONFIG_FILE\"] = temp.name\n        config = pypi.load()\n        assert config['config-file'] == temp.name\n\n\nif __name__ == '__main__':\n    test_load_env()\n    test_invalid_config_path()\n    test_invalid_config_content()\n","repo_name":"intuit/baklava","sub_path":"tests/test_pypi.py","file_name":"test_pypi.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"36709471643","text":"from market import db, app\nfrom market.models import Item, User\n\nwith app.app_context():\n    #u1 = User(username='jsc', password_hash='123456', email_address='jsc@jsc.com')\n    #item2 = Item(name=\"Laptop\", price=600, barcode='321912987542', description='description')\n    #i1 = Item(name=\"IPhone 10\", price=800, barcode='123456789012', description='megjegyzés')\n    #db.session.add(i1)\n    #db.session.commit()\n    #print(Item.query.all())\n    item1 = Item.query.filter_by(name='IPhone 10').first()\n    item1.owner = User.query.filter_by(username='jsc').first().id\n    db.session.add(item1)\n    db.session.commit()\n    print(item1.owner)","repo_name":"horvathlaszlo072/flask_market","sub_path":"databasewrite.py","file_name":"databasewrite.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73610055927","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 31 13:30:28 2021\n@author: Axel François\ngithub : https://github.com/AxFrancois/ProgrammationConcurrente\n\nTo do : placement manuel des points avant le lancement\n\"\"\"\n\n# %%----------------------Import----------------------------------------------#\n\nimport tkinter as tk\nimport time,random,sys\nimport multiprocessing as mp\n\n# %%----------------------Sémaphores------------------------------------------#\n\nsempActual1 = mp.Semaphore(1)\nsempActual2 = mp.Semaphore(1)\nsempActual3 = mp.Semaphore(1)\nsempActual4 = mp.Semaphore(1)\nsemPHG = mp.Semaphore(0)\nsemPHD = mp.Semaphore(0)\nsemPBG = mp.Semaphore(0)\nsemPBD = mp.Semaphore(0)\n\n# %%----------------------Fonctions-------------------------------------------#\n\ndef ProcessusCalcul(pNombreDeCase,pPosition,pListeModif,pAffichageActuel):\n    \"\"\"Processus de calcul principal : pour chaque processus, attribue les limites de calculs et de test, vérifie pour chaque case ses voisines, retourne les cases nécéssitant un changement dans pListeModif\n        Les commentaires correspondent à mes périgrinations pour les tests avec la grille de 10x10\n    Args:\n        pNombreDeCase (int): nombre de case par coté\n        pPosition (str): HG, HD, BG ou BD : permet de découper la case pour chaque process\n        pListeModif (mp Array): permet de faire passer la liste des cases à changer\n        pAffichageActuel (mp Array): permet de connaitre l'état actuel de toutes les cases\n    \"\"\"\n    moitie = int(pNombreDeCase / 2)\n    if pPosition == \"HG\":\n        limiteActualGauche,limiteActualHaut,limiteActualDroite,limiteActualBas = 0, 0, moitie, moitie #0,0,5,5 #0,0,4,4 mais faut prendre en compte que ça s'arrete un avant les range\n        limiteTestGauche,limiteTestHaut,limiteTestDroite, limiteTestBas = 0, 0, moitie, moitie #0,0,5,5\n    elif pPosition == \"HD\":\n        limiteActualGauche,limiteActualHaut,limiteActualDroite,limiteActualBas = moitie, 0, pNombreDeCase, moitie #5,0,10,5 #5,0,9,4\n        limiteTestGauche,limiteTestHaut,limiteTestDroite, limiteTestBas =  moitie - 1, 0, pNombreDeCase - 1, moitie #4,0,9,5\n    elif pPosition == \"BG\":\n        limiteActualGauche,limiteActualHaut,limiteActualDroite,limiteActualBas = 0, moitie, moitie, pNombreDeCase #0,5,5,10 #0,5,4,9\n        limiteTestGauche,limiteTestHaut,limiteTestDroite, limiteTestBas = 0, moitie - 1, moitie, pNombreDeCase - 1 #0,4,5,9\n    elif pPosition == \"BD\":\n        limiteActualGauche,limiteActualHaut,limiteActualDroite,limiteActualBas = moitie, moitie, pNombreDeCase, pNombreDeCase #5,5,10,10 #5,5,9,9\n        limiteTestGauche,limiteTestHaut,limiteTestDroite, limiteTestBas = moitie - 1, moitie - 1, pNombreDeCase - 1, pNombreDeCase - 1 #4,4,9,9\n    else:\n        print(\"Valeur de pPosition inatendue\")\n    for row in range(limiteActualHaut,limiteActualBas):\n        for col in range(limiteActualGauche,limiteActualDroite):#Pour toutes les cases de la zones attribuée\n            numeroRectangleEtudie = row * pNombreDeCase + col + 1\n            Etat = pAffichageActuel[numeroRectangleEtudie-1]\n            VoisinsVivants = 0\n            listeVoisins = [(row-1,col-1),(row,col-1),(row+1,col-1),(row-1,col),(row+1,col),(row-1,col+1),(row,col+1),(row+1,col+1)]    #Liste des voisins\n            for CoordVoisins in listeVoisins:   #Test de l'état de vie/mort de chaque voisins\n                if (limiteTestHaut <= CoordVoisins[0] <= limiteTestBas) and (limiteTestGauche <= CoordVoisins[1] <= limiteTestDroite):\n                    numeroRectangleVerif = CoordVoisins[0]*pNombreDeCase + CoordVoisins[1]\n                    if pAffichageActuel[numeroRectangleVerif] == 1 :\n                        VoisinsVivants += 1\n            if Etat == 1: #Si la cellule est vivante\n                if VoisinsVivants < 2 or VoisinsVivants > 3:\n                    pListeModif[numeroRectangleEtudie-1] = numeroRectangleEtudie\n            elif Etat == 0: #Si la cellule est morte\n                if VoisinsVivants == 3:\n                    pListeModif[numeroRectangleEtudie-1] = numeroRectangleEtudie\n            else:\n                print(\"couleur inatendue\")       \n\ndef ProcessusHG(pNombreDeCase,pPosition,pListeModif,pAffichageActuel):\n    \"\"\"Fonction du processus de calcul de la section en haut à gauche\n\n    Args:\n        pNombreDeCase (int): nombre de case par coté\n        pPosition (str): HG, HD, BG ou BD : permet de découper la case pour chaque process\n        pListeModif (mp Array): permet de faire passer la liste des cases à changer\n        pAffichageActuel (mp Array): permet de connaitre l'état actuel de toutes les cases\n    \"\"\"\n    global Run\n    while bool(Run.value) == True:\n        sempActual1.acquire()\n\n        ProcessusCalcul(pNombreDeCase,pPosition,pListeModif,pAffichageActuel)\n        #print(\"HG Fini\")\n        semPHG.release()\n    sys.exit(0)\n\ndef ProcessusHD(pNombreDeCase,pPosition,pListeModif,pAffichageActuel):\n    \"\"\"Fonction du processus de calcul de la section en haut à droite\n\n    Args:\n        pNombreDeCase (int): nombre de case par coté\n        pPosition (str): HG, HD, BG ou BD : permet de découper la case pour chaque process\n        pListeModif (mp Array): permet de faire passer la liste des cases à changer\n        pAffichageActuel (mp Array): permet de connaitre l'état actuel de toutes les cases\n    \"\"\"\n    global Run\n    while bool(Run.value) == True:\n        sempActual2.acquire()\n        \n        ProcessusCalcul(pNombreDeCase,pPosition,pListeModif,pAffichageActuel)\n        #print(\"HD fini\")\n        semPHD.release()\n\n    sys.exit(0)\n\ndef ProcessusBG(pNombreDeCase,pPosition,pListeModif,pAffichageActuel):\n    \"\"\"Fonction du processus de calcul de la section en bas à gauche\n\n    Args:\n        pNombreDeCase (int): nombre de case par coté\n        pPosition (str): HG, HD, BG ou BD : permet de découper la case pour chaque process\n        pListeModif (mp Array): permet de faire passer la liste des cases à changer\n        pAffichageActuel (mp Array): permet de connaitre l'état actuel de toutes les cases\n    \"\"\"\n    global Run\n    while bool(Run.value) == True:\n        sempActual3.acquire()\n        \n        ProcessusCalcul(pNombreDeCase,pPosition,pListeModif,pAffichageActuel)\n        #print(\"BG fini\")\n        semPBG.release()\n\n    sys.exit(0)\n\ndef ProcessusBD(pNombreDeCase,pPosition,pListeModif,pAffichageActuel):\n    \"\"\"Fonction du processus de calcul de la section en bas à droite\n\n    Args:\n        pNombreDeCase (int): nombre de case par coté\n        pPosition (str): HG, HD, BG ou BD : permet de découper la case pour chaque process\n        pListeModif (mp Array): permet de faire passer la liste des cases à changer\n        pAffichageActuel (mp Array): permet de connaitre l'état actuel de toutes les cases\n    \"\"\"\n    global Run\n    while bool(Run.value) == True:\n        sempActual4.acquire()\n\n        ProcessusCalcul(pNombreDeCase,pPosition,pListeModif,pAffichageActuel)\n        #print(\"BD fini\")\n        semPBD.release()\n    sys.exit(0)\n\ndef Actualisation(ArrayModif,ArrayEtat,Canevas, window):\n    \"\"\"Fonction du processus de synchronisation et d'actualisation. Modifie la couleur sur le Canvas en fonction de ArrayModif\n\n    Args:\n        ArrayModif (mp Array): permet de faire passer la liste des cases à changer\n        ArrayEtat (mp Array): permet de connaitre l'état actuel de toutes les cases\n        Canevas (tk Canvas): permet de modifier le Canvas en fonction de ArrayModif\n        window (Tk window): fenêtre tkinter contenant de Canevas\n    \"\"\"\n    global Run, Pause\n    while bool(Run.value) == True:\n        try:\n            #print(\"J'attend\")\n            semPHG.acquire()\n            semPHD.acquire()\n            semPBG.acquire()\n            semPBD.acquire()\n            #print(\"J'ai tout\")\n            for i in range(len(ArrayModif)):\n                if ArrayModif[i] != 0:\n                    if Canevas.itemcget(ArrayModif[i],\"fill\") == \"black\":\n                        Canevas.itemconfig(ArrayModif[i],fill='white')\n                        ArrayEtat[i] = 0\n                    elif Canevas.itemcget(ArrayModif[i],\"fill\") == \"white\":\n                        Canevas.itemconfig(ArrayModif[i],fill='black')\n                        ArrayEtat[i] = 1\n                ArrayModif[i] = 0\n\n            while bool(Pause.value) == True:\n                window.update()\n                time.sleep(0.5)\n\n            sempActual1.release()\n            sempActual2.release()\n            sempActual3.release()\n            sempActual4.release()\n            \n            window.update()\n            time.sleep(0.1)\n        \n        except:\n            Run.value = 0\n            sempActual1.release()\n            sempActual2.release()\n            sempActual3.release()\n            sempActual4.release()\n            sys.exit(0)\n\ndef fPause():\n    \"\"\"Fonction pour gérer la pause\n    \"\"\"\n    global Pause\n    if Pause.value == 0:\n        Pause.value = 1\n    else:\n        Pause.value = 0\n\n# %%----------------------Constantes-----------------------------------------#\n\nwidth = 500     #<------------- Changer cette valeur pour augmenter/diminuer le nombre de case\nheight = width + 30\ncellSize = 5    #<------------- Changer cette valeur pour augmenter/diminuer la taille et donc le nombre de case\nNombreDeCase = int(width/cellSize)\nRun = mp.Value('i',1)\nPause = mp.Value('i', 0)\nGrille = [[] for i in range(NombreDeCase)]\n\n# %%----------------------Interface graphique---------------------------------#\n\nwindow = tk.Tk()\nwindow.title(\"Game Of Life\")\nwidthScreen = window.winfo_screenwidth()\nheightScreen = window.winfo_screenheight()\nx = (widthScreen // 2) - (width // 2)\ny = (heightScreen // 2) - (height // 2)\nwindow.geometry('{}x{}+{}+{}'.format(width,height,x,y))\n\nMainFrame = tk.Frame(window)\nMainFrame.grid(row=1,column=0)\n\nSecondaryFrame = tk.Frame(window)\nSecondaryFrame.grid(row=0,column=0)\n\nCanevas = tk.Canvas(MainFrame,width=width,height=height, bg = '#FFFFFF', highlightthickness=0)\nCanevas.pack(side=tk.RIGHT)\n\nfor row in range(NombreDeCase):\n    for col in range(NombreDeCase):\n        BordHaut = row * cellSize\n        BordGauche = col * cellSize\n        BordBas = (row+1) * cellSize\n        BordDroite = (col+1) * cellSize\n        Rectangle = Canevas.create_rectangle(BordGauche,BordHaut,BordDroite,BordBas,outline='gray',fill='white')\n        Grille[row].append(Rectangle)\n\nreplayButton = tk.Button(SecondaryFrame, text = 'Play/Pause', height = 1, width = 10, command=fPause)\nreplayButton.grid(row = 0, column = 0)\n\n# %%----------------------Test Batch pour 10 carré de chaque coté---------------------------------#\n\n\"\"\"\nCanevas.itemconfig(55,fill='black')\nCanevas.itemconfig(56,fill='black')\nCanevas.itemconfig(57,fill='black')\nCanevas.itemconfig(65,fill='black')\n\nCanevas.itemconfig(1,fill='black')\nCanevas.itemconfig(2,fill='black')\nCanevas.itemconfig(11,fill='black')\nCanevas.itemconfig(3,fill='black')\nCanevas.itemconfig(80,fill='black')\nCanevas.itemconfig(81,fill='black')\n\nCanevas.itemconfig(55,fill='black')\nCanevas.itemconfig(56,fill='black')\nCanevas.itemconfig(57,fill='black')\nCanevas.itemconfig(46,fill='black')\n\"\"\"\n\n# %%----------------------Remplissage aléatoire de la grille---------------------------------#\n\nfor i in range(2000):\n    Canevas.itemconfig(random.randint(1,NombreDeCase**2),fill='black')\n\n# %%----------------------Initialisation des Arrays---------------------------------#\n\nArrayModif = mp.Array('i',range(NombreDeCase**2))\nfor i in range(len(ArrayModif)):\n    ArrayModif[i] = 0\n\nArrayEtat = mp.Array('i',range(NombreDeCase**2))\nfor i in range(len(ArrayEtat)):\n    if Canevas.itemcget(i+1,\"fill\") == \"black\":\n        ArrayEtat[i] = 1\n    else:\n        ArrayEtat[i] = 0\n\n# %%----------------------Affichage état initial---------------------------------#\n\nwindow.update()\ntime.sleep(1)\n\n# %%----------------------Lancement multiprocessing---------------------------------#\n\nP1 = mp.Process(target = ProcessusHG, args = (NombreDeCase,\"HG\",ArrayModif,ArrayEtat))\nP2 = mp.Process(target = ProcessusHD, args = (NombreDeCase,\"HD\",ArrayModif,ArrayEtat))\nP3 = mp.Process(target = ProcessusBG, args = (NombreDeCase,\"BG\",ArrayModif,ArrayEtat))\nP4 = mp.Process(target = ProcessusBD, args = (NombreDeCase,\"BD\",ArrayModif,ArrayEtat))\nP5 = mp.Process(target = Actualisation, args = (ArrayModif, ArrayEtat, Canevas, window))\n\nP1.start()\nP2.start()\nP3.start()\nP4.start()\nP5.start()\n\nP1.join()\nP2.join()\nP3.join()\nP4.join()\nP5.join()\n\nsys.exit(0)\n","repo_name":"AxFrancois/PC-ProgrammationConcurrente","sub_path":"Projet/GameOfLife.py","file_name":"GameOfLife.py","file_ext":"py","file_size_in_byte":12431,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"11668404282","text":"# from gym.envs.mujoco.walker2d import Walker2dEnv\nimport os\n\nimport gym\nimport numpy as np\nfrom gym import utils\nfrom gym.core import Wrapper\n\nfrom gym.envs.mujoco import mujoco_env\nfrom gym.envs.mujoco.mujoco_env import MujocoEnv\nfrom gym.envs.mujoco.half_cheetah_v3 import HalfCheetahEnv as HCEnvV3\nfrom gym.envs.mujoco.hopper_v3 import HopperEnv as HopperEnvV3\nfrom gym.envs.mujoco.walker2d_v3 import Walker2dEnv as Walker2dEnvV3\n\n\nclass Walker2dV3NoBackground(Walker2dEnvV3):\n    def __init__(self, require_shadow=False):\n        xml_path = 'walker2d(no_shadow).xml'\n        if require_shadow:\n            xml_path = \"walker2d.xml\"\n        xml_path = os.path.join(\n            os.path.dirname(__file__), \"modified_mujoco_assets\", xml_path\n        )\n        super(Walker2dV3NoBackground, self).__init__(xml_file=xml_path)\n\n\nclass HalfCheetahV2NoBackground(MujocoEnv, utils.EzPickle):\n    def __init__(self, require_shadow=False):\n        xml_path = 'half_cheetah(no_shadow).xml'\n        if require_shadow:\n            xml_path = \"half_cheetah.xml\"\n        xml_path = os.path.join(\n            os.path.dirname(__file__), \"modified_mujoco_assets\", xml_path\n        )\n        MujocoEnv.__init__(self, xml_path, 1)\n        utils.EzPickle.__init__(self)\n\n    def step(self, action):\n        xposbefore = self.sim.data.qpos[0]\n        self.do_simulation(action, self.frame_skip)\n        xposafter = self.sim.data.qpos[0]\n        ob = self._get_obs()\n        reward_ctrl = -0.1 * np.square(action).sum()\n        reward_run = (xposafter - xposbefore) / self.dt\n        reward = reward_ctrl + reward_run\n        done = False\n        return ob, reward, done, dict(\n            reward_run=reward_run, reward_ctrl=reward_ctrl\n        )\n\n    def _get_obs(self):\n        return np.concatenate(\n            [\n                self.sim.data.qpos.flat[1:],\n                self.sim.data.qvel.flat,\n            ]\n        )\n\n    def reset_model(self):\n        qpos = self.init_qpos + self.np_random.uniform(\n            low=-.1, high=.1, size=self.model.nq\n        )\n        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1\n        self.set_state(qpos, qvel)\n        return self._get_obs()\n\n    def viewer_setup(self):\n        self.viewer.cam.distance = self.model.stat.extent * 0.5\n\n\nclass HalfCheetahV3NoBackground(HCEnvV3):\n    # def __init__(self, require_shadow=False):\n    def __init__(\n            self,\n            require_shadow=False,\n            # xml_file='half_cheetah.xml',\n            forward_reward_weight=1.0,\n            ctrl_cost_weight=0.1,\n            reset_noise_scale=0.1,\n            exclude_current_positions_from_observation=True,\n            rgb_rendering_tracking=True\n    ):\n        xml_path = 'half_cheetah(no_shadow).xml'\n        if require_shadow:\n            xml_path = \"half_cheetah.xml\"\n        xml_path = os.path.join(\n            os.path.dirname(__file__), \"modified_mujoco_assets\", xml_path\n        )\n        utils.EzPickle.__init__(**locals())\n        self._forward_reward_weight = forward_reward_weight\n        self._ctrl_cost_weight = ctrl_cost_weight\n        self._reset_noise_scale = reset_noise_scale\n        self._exclude_current_positions_from_observation = (\n            exclude_current_positions_from_observation\n        )\n\n        mujoco_env.MujocoEnv.__init__(\n            self, xml_path, 5, rgb_rendering_tracking=rgb_rendering_tracking\n        )\n\n\nclass HopperV3NoBackground(HopperEnvV3, utils.EzPickle):\n    def __init__(\n            self,\n            require_shadow=False,\n            # xml_file='hopper.xml',\n            forward_reward_weight=1.0,\n            ctrl_cost_weight=1e-3,\n            healthy_reward=1.0,\n            terminate_when_unhealthy=True,\n            healthy_state_range=(-100.0, 100.0),\n            healthy_z_range=(0.7, float('inf')),\n            healthy_angle_range=(-0.2, 0.2),\n            reset_noise_scale=5e-3,\n            exclude_current_positions_from_observation=True,\n            rgb_rendering_tracking=True\n    ):\n        xml_path = 'hopper(no_shadow).xml'\n        if require_shadow:\n            xml_path = \"hopper.xml\"\n        xml_path = os.path.join(\n            os.path.dirname(__file__), \"modified_mujoco_assets\", xml_path\n        )\n\n        utils.EzPickle.__init__(**locals())\n\n        self._forward_reward_weight = forward_reward_weight\n\n        self._ctrl_cost_weight = ctrl_cost_weight\n\n        self._healthy_reward = healthy_reward\n        self._terminate_when_unhealthy = terminate_when_unhealthy\n\n        self._healthy_state_range = healthy_state_range\n        self._healthy_z_range = healthy_z_range\n        self._healthy_angle_range = healthy_angle_range\n\n        self._reset_noise_scale = reset_noise_scale\n\n        self._exclude_current_positions_from_observation = (\n            exclude_current_positions_from_observation\n        )\n\n        # Original the skip frame = 4\n        mujoco_env.MujocoEnv.__init__(\n            self, xml_path, 4, rgb_rendering_tracking=rgb_rendering_tracking\n        )\n\n\nclass MujocoWrapper(Wrapper):\n    def get_state_wrap(self):\n        return self.sim.get_state()\n\n    def set_state_wrap(self, state):\n        self.sim.set_state(state)\n\n\ndef _test_MujocoWrapper(env_name):\n    env = gym.make(env_name)\n    env = MujocoWrapper(env)\n\n    env.seed(0)\n    env.reset()\n\n    states = []\n    acts = []\n    for _ in range(10):\n        act = env.action_space.sample()\n        acts.append(act)\n        env.step(act)\n        state = env.get_state_wrap()\n        states.append(state)\n\n    # Test if replicable\n    new_env = gym.make(env_name)\n    new_env = MujocoWrapper(new_env)\n    new_env.seed(0)\n    new_env.reset()\n\n    for act, state in zip(acts, states):\n        new_env.step(act)\n        current_state = new_env.get_state_wrap()\n        assert state == current_state\n\n    # restore old env\n    env.seed(0)\n    env.reset()\n    env.set_state_wrap(states[5])\n    env.step(acts[6])\n    current_state = env.get_state_wrap()\n    assert current_state.time == states[6].time\n    np.testing.assert_array_almost_equal(current_state.qpos, states[6].qpos)\n    np.testing.assert_array_almost_equal(current_state.qvel, states[6].qvel)\n    np.testing.assert_array_equal(current_state.act, states[6].act)\n\n    print(\"Successfully Pass Test for env: {}!!!\".format(env_name))\n    return current_state, states[6]\n\n\ndef test_MujocoWrapper():\n    env_name_list = [\"Ant-v2\", \"HalfCheetah-v2\", \"Humanoid-v2\", \"Walker2d-v2\"]\n    for env_name in env_name_list:\n        _test_MujocoWrapper(env_name)\n\n\nif __name__ == '__main__':\n    test_MujocoWrapper()\n","repo_name":"pengzhenghao/rl-interpretation","sub_path":"toolbox/env/mujoco_wrapper.py","file_name":"mujoco_wrapper.py","file_ext":"py","file_size_in_byte":6557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2497812302","text":"import pygame as pg\r\n\r\nFPS = 60\r\n\r\nWIDTH, HEIGHT = 800,800\r\nROWS, COLS = 8, 8\r\nSQUARE_SIZE = WIDTH//COLS\r\n\r\n# RGB color codes\r\nRED = (255, 0 ,0)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nBLUE = (0, 0, 255)\r\nGRAY = (128, 128, 128)\r\n\r\nCROWN_PATH = r'assets/crown.png'\r\nCROWN = pg.transform.scale(pg.image.load(CROWN_PATH), (45, 35))\r\n\r\n\r\n","repo_name":"saltasaurus/Checkers","sub_path":"Checkers/checkers/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28119524342","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.action_chains import ActionChains\n\nfrom bs4 import BeautifulSoup\nimport time\nfrom selenium.webdriver.common.keys import Keys\n\nimport pandas as pd\n\n# Creating a webdriver instance\ndriver = webdriver.Chrome(\n    r\"C:\\Users\\mohit\\Downloads\\chromedriver_win32\\chromedriver.exe\")\n# This instance will be used to log into LinkedIn\n\n# Opening linkedIn's login page\ndriver.get(\"https://linkedin.com/uas/login\")\n\n# waiting for the page to load\ntime.sleep(5)\n\n\ndef main():\n    # entering username\n    username = driver.find_element(By.ID, \"username\")\n\n    # In case of an error, try changing the element\n    # tag used here.\n\n    # Enter Your Email Address\n    username.send_keys(\"negi.m@northeastern.edu\")\n\n    # entering password\n    pword = driver.find_element(By.ID, \"password\")\n    # In case of an error, try changing the element\n    # tag used here.\n\n    # Enter Your Password\n    pword.send_keys(\"Lonely123)\")\n\n    # Clicking on the log in button\n    # Format (syntax) of writing XPath -->\n    # //tagname[@attribute='value']\n    driver.find_element(By.XPATH, \"//button[@type='submit']\").click()\n\n    # In case of an error, try changing the\n    # XPath used here.\n    time.sleep(20)\n    job_icon = driver.find_element(\n        By.XPATH, \"/html/body/div[5]/header/div/nav/ul/li[3]/a\").click()\n\n    time.sleep(5)\n    job_icon = driver.find_element(\n        By.XPATH, \"/html/body/div[5]/div[3]/div/div[3]/div/div/main/section/ul/li[1]\").click()\n\n    html = driver.find_element(By.TAG_NAME, 'html')\n    html.send_keys(Keys.END)\n    time.sleep(10)\n\n    ActionChains(driver).move_to_element(\n        driver.find_element(By.XPATH, '//div[@class=\"scaffold-layout__list \"]')).perform()\n\n    driver.set_page_load_timeout(20)\n\n    # starting to gernerate the json file for the job posted\n    jobs_per_page_xpath = \"//div[@class='full-width artdeco-entity-lockup__title ember-view']\"\n    job_titles = set()\n\n    for i in range(8):\n        job_title_temp = driver.find_elements(By.XPATH, jobs_per_page_xpath)\n        for jd in job_title_temp:\n            job_titles.add(jd)\n\n    print(len(job_titles))\n    time.sleep(10)\n    job_title = []\n    for elem in job_titles:\n        job_title.append(elem.text)\n        time.sleep(2)\n    print(job_title)\n\n    compay_info = []\n    job_description = []\n    for elem in job_titles:\n        elem.click()\n        time.sleep(10)\n        job_info_xpath = \"//li[@class='jobs-unified-top-card__job-insight']\"\n        elem_attr = driver.find_elements(\n            By.XPATH, job_info_xpath)\n        counter = 0\n        attr_len = len(elem_attr)\n        comp_info_temp = []\n        for ele in elem_attr:\n            comp_info_temp.append(ele.text)\n        compay_info.append(comp_info_temp)\n        desc_xpath = \"//*[contains(@class,'jobs-box--fadein jobs-box--full-width jobs-box--with-cta-large jobs-description')]\"\n        desc = driver.find_element(By.XPATH, desc_xpath)\n        job_description.append(desc.text)\n\n    jobs = pd.DataFrame({\n        'job_titles': job_title,\n        'company_type': compay_info,\n        'job_description': job_description\n    })\n    print(jobs)\n    jobs.to_csv(\"Latest_Jobes.csv\", index=False)\\\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Negi97Mohit/Linkedin-hypocrisy","sub_path":"scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":3469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18654264384","text":"import pytest\n\nfrom homework.homework6.hw2 import *\n\nlazy_student = Student(\"Roman\", \"Petrov\")\ngood_student = Student(\"Lev\", \"Sokolov\")\n\nopp_teacher = Teacher(\"Daniil\", \"Shadrin\")\nadvanced_python_teacher = Teacher(\"Aleksandr\", \"Smetanin\")\n\noop_hw = opp_teacher.create_homework(\"Learn OOP\", 1)\ndocs_hw = opp_teacher.create_homework(\"Read docs\", 5)\ndead_hw = advanced_python_teacher.create_homework(\"Make some noise\", -2)\n\nresult_1 = good_student.do_homework(oop_hw, \"I have done this hw\")\nresult_2 = good_student.do_homework(docs_hw, \"I have done this hw too\")\nresult_3 = lazy_student.do_homework(docs_hw, \"done\")\n\n\ndef test_exception_do_homework():\n    with pytest.raises(AttributeError):\n        lazy_student.do_homework(result_1, \"done\")\n    with pytest.raises(DeadlineError):\n        lazy_student.do_homework(dead_hw, \"done\")\n\n\ndef test_homework_done_dict():\n    opp_teacher.check_homework(result_1)\n    temp_1 = opp_teacher.homework_done\n\n    advanced_python_teacher.check_homework(result_1)\n    temp_2 = Teacher.homework_done\n    assert temp_1 == temp_2\n\n\ndef test_reset_homework_done():\n    opp_teacher.check_homework(result_2)\n    opp_teacher.check_homework(result_3)\n    assert Teacher.homework_done\n    Teacher.reset_results()\n    assert not Teacher.homework_done\n","repo_name":"alexplahotnik/homework","sub_path":"tests/homework6/test_hw2.py","file_name":"test_hw2.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34869369074","text":"array = [[35, 17, 21, 36, 12], [22, 9, 8, 10, 7], [11, 65, 37, 22, 18]]\n\nm = len(array)\nn = len(array[0])\n\nflatten = sorted([(j, i) for i in range(m) for j in array[i]])\n\nlimit = (m + 1) // 2\n\ncounter = [0] * m\nstart = 0\ntotal = 0\nmink = float('inf')\nmaxprod = -float('inf')\n\nfor i in range(len(flatten)):\n    pair = flatten[i]\n    counter[pair[1]] += 1\n    total += (1 if counter[pair[1]] == limit else 0)\n    while counter[flatten[start][1]] > limit:\n        counter[flatten[start][1]] -= 1\n        start += 1\n    if total == m:\n        currentk = pair[0] - flatten[start][0]\n        if currentk < mink:\n            mink = currentk\n            currentprod = (i - start + 1) * mink\n            maxprod = max(maxprod, currentprod)\n\nprint(maxprod)\n","repo_name":"harperjiang/LeetCode","sub_path":"src/databricks/twodarray2/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14030864546","text":"from copy import copy\n\nfrom django.contrib.sessions.backends.db import SessionStore as DbSessionStore\n\nfrom apps.store.models import Cart\n\n\nclass SessionStore(DbSessionStore):\n    def cycle_key(self):\n        old_session_key = self.session_key\n        if old_session_key:\n            try:\n                old_cart = Cart.objects.get(session__session_key=old_session_key)\n                old_cart.session_id = None\n                old_cart.save()\n                super().cycle_key()\n                old_cart.session_id = self.session_key\n                old_cart.save()\n            except Cart.DoesNotExist:\n                super().cycle_key()\n        else:\n            super().cycle_key()\n","repo_name":"sayadlou/sepimo","sub_path":"apps/core/session_backend.py","file_name":"session_backend.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70792187449","text":"import matplotlib.pyplot as plt\n\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten,Conv2D,MaxPooling2D,Dropout\nfrom keras.utils import np_utils\nfrom keras.layers.normalization import BatchNormalization\nfrom ann_visualizer.visualize import ann_viz\n\ndef criaCNN ( larguraImagem, alturaImagem ) :\n    \"\"\"\n    Função que cria a CNN para classificação\n    de imagem\n\n    \"\"\"\n\n    cNN = Sequential ( )\n\n    # o número de kernels vem da forma n = 32*i\n    # onde i você decide a quantidade de bytes\n\n    # camada de convolução\n    cNN.add ( Conv2D  (\n        filters = 64,\n        kernel_size = ( 3, 3 ),\n        input_shape = ( larguraImagem, alturaImagem, 1 ),\n        activation = \"relu\"\n    ) )\n\n    # normalização dos dados após a 1 camada de convolução\n    cNN.add ( BatchNormalization (  ) )\n\n    # camada de pooling\n    cNN.add ( MaxPooling2D (\n        pool_size = ( 2, 2 )\n    ) )\n\n    # 2 camada da rede neural\n    cNN.add ( Conv2D (\n         filters = 64,\n         kernel_size = ( 3, 3 ),\n         input_shape = ( larguraImagem, alturaImagem, 1 ),\n         activation = \"relu\"\n    ) )\n\n    cNN.add ( BatchNormalization (  ) )\n\n    cNN.add ( MaxPooling2D (\n        pool_size = ( 2, 2 )\n    ) )\n\n    # camada de Flatten\n    cNN.add ( Flatten ( ) )\n\n    # montando a rede neural de camada densa de duas camadas escondidas\n    cNN.add ( Dense (\n        units = 128,\n        activation = \"relu\"\n     ) )\n\n    cNN.add ( Dropout ( 0.2 ) )\n\n    cNN.add ( Dense (\n        units = 128,\n        activation = \"relu\"\n    ) )\n\n    cNN.add ( Dropout ( 0.2 ) )\n\n    # camada saída\n    cNN.add ( Dense (\n         units = 10,\n         activation = \"softmax\"\n     ) )\n\n    cNN.compile (\n         loss = \"categorical_crossentropy\",\n         optimizer = \"adam\",\n         metrics = [\"accuracy\"]\n     )\n\n    #ann_viz ( cNN, title = \"Rede Convolucional\" )\n\n    return cNN\n\ndef preImageProcessing ( xDadoTreinamento, xDadoTeste, yDadoTreinamento, yDadoTeste ) :\n    \"\"\"\n    Função que faz o pré - processamento de todas\n    as imagens do dataset de treino e teste\n\n    \"\"\"\n\n    # remodelando o formato da imagem para o modelo\n    # de tensorflow para o dado {value for value in variable}de entrada treinameto\n    previsaoTreinamento = xDadoTreinamento.reshape (\n        xDadoTreinamento.shape[0],\n        len ( xDadoTreinamento[:][0] ),\n        len ( xDadoTreinamento[0][:] ),\n        1\n    )\n\n    previsaoTreinamento = previsaoTreinamento.astype ( \"float64\" )\n\n    # remodelando o formato da imagem para o modelo\n    # de tensorflow para o dado de entra teste\n    previsaoTeste = xDadoTeste.reshape (\n        xDadoTeste.shape[0],\n        len ( xDadoTeste[:][0] ),\n        len ( xDadoTeste[0][:] ),\n        1\n    )\n\n    previsaoTeste = previsaoTeste.astype ( \"float64\" )\n\n    # fazendo a normalização dos pixels cinzas\n    previsaoTreinamento /= 255\n    previsaoTeste /= 255\n\n    return previsaoTreinamento, previsaoTeste\n\ndef main (  ) :\n    \"\"\"\n    Função principal que faz o processo\n    de rede convolucional\n\n    \"\"\"\n    ( xDadoTreinamento, yDadoTreinamento ), ( xDadoTeste, yDadoTeste ) = mnist.load_data()\n\n    larguraImagem = len ( xDadoTeste[:][0] )\n    alturaImagem = len ( xDadoTeste[0][:] )\n\n    previsaoTreinamento, previsaoTeste = preImageProcessing (\n        xDadoTreinamento = xDadoTreinamento,\n        xDadoTeste = xDadoTreinamento,\n        yDadoTreinamento = yDadoTreinamento,\n        yDadoTeste = yDadoTeste )\n\n    dummyRespTreinamento = np_utils.to_categorical ( yDadoTreinamento, 10 ) # conversão dos dados categóricos de treinamento\n    dummyRespTeste = np_utils.to_categorical ( yDadoTreinamento, 10 ) # conversão dos dados categóricos de teste\n\n    cNN = criaCNN (\n        larguraImagem = larguraImagem,\n        alturaImagem = alturaImagem\n    )\n\n    cNN.fit ( previsaoTreinamento, dummyRespTreinamento,\n              batch_size = 128,\n              epochs = 20,\n              validation_data = ( previsaoTeste, dummyRespTeste )\n    )\n\n    resultado = cNN.evaluate (\n        previsaoTeste,\n        dummyRespTreinamento\n    )\n\nif __name__ == '__main__':\n    main()\n","repo_name":"borin98/RedesNeuraisMNIST","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1143518395","text":"from node import Node\nfrom rack import Rack\nfrom regneklynge import Regneklynge\n\ndef main():\n\n    abel = Regneklynge(12)\n\n    teller = 650\n    while teller != 0:\n            abel.settInnNode(Node(64, 1))\n            teller -= 1\n\n    teller = 16\n    while teller != 0:\n            abel.settInnNode(Node(1024, 2))\n            teller -= 1\n\n    print(\"Noder med minst 32 GB: \", abel.noderMedNokMinne(32), \".\", sep = \"\")\n    print(\"Noder med minst 64 GB: \", abel.noderMedNokMinne(64), \".\", sep = \"\")\n    print(\"Noder med minst 128 GB: \", abel.noderMedNokMinne(128), \".\", sep = \"\")\n\n    print(\"\")\n\n    print(\"Antal prosessorer: \", abel.antProsessorer(), \".\", sep = \"\")\n    print(\"Antal racks: \", abel.antRacks(), \".\", sep = \"\")\n\nmain()\n","repo_name":"MehCheniti/Python","sub_path":"IN1000/Obligatorisk innlevering 8/hovedprogram.py","file_name":"hovedprogram.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14054560706","text":"import setuptools\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n     name='mobi-python',\n     version='0.0.1',\n     scripts=[] ,\n     author=\"Elliot Kroo\",\n     author_email=\"elliot@kroo.net\",\n     description=\"Mobi Python Library\",\n     long_description=long_description,\n     long_description_content_type=\"text/markdown\",\n     url=\"https://github.com/kroo/mobi-python\",\n     packages=setuptools.find_packages(),\n     classifiers=[\n         \"Programming Language :: Python :: 3\",\n         \"License :: OSI Approved :: MIT License\",\n         \"Operating System :: OS Independent\",\n     ],\n )","repo_name":"krysinko/sort-my-books","sub_path":"mobi-python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71138300090","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport os\nimport traceback\nfrom PyQt4 import QtCore, QtGui\nfrom qgis.core import (QgsMessageLog,\n                       QgsErrorMessage,\n                       QgsVectorFileWriter,\n                       QgsVectorLayer,\n                       QgsMapLayerRegistry,\n                       QgsApplication)\nfrom qgis.gui import (QgsMessageBar)\nfrom qgis.utils import iface\n\nfrom algorithm import Algorithm\n\nclass OutputTabManager(QtCore.QObject):\n    ''' Class to hide managing of relative tab\n    '''\n    projectModified = QtCore.pyqtSignal()\n        \n    def __init__(self, parent=None):\n        '''constructor'''\n        super(OutputTabManager, self).__init__(parent)\n\n        # parent is the dock widget with all graphical elements\n        self.gui = parent\n        self.plugin = parent.parent\n        \n        # init some globals\n        self.applicationPath = os.path.dirname(os.path.realpath(__file__))\n        self.project = None\n        self.projectPath = None\n        self.outLayer = None\n        self.outLayerId = None\n        self.outLayerRemoved = False\n        \n        # retrieve the current tab index\n        self.initTabTabIndex()\n        \n        # disable tab at the beginning\n        self.gui.tabWidget.setTabEnabled(self.tabIndex, False)\n        \n        # add some gui events\n        self.gui.createNewLayer_RButton.toggled.connect(self.setInputLayer)\n        self.gui.selectOutfile_TButton.clicked.connect(self.selectNewOutFile)\n        self.gui.calculate_PButton.clicked.connect(self.calculate)\n        \n    def initTabTabIndex(self):\n        ''' Retrieve what tab index refer the current tab manager\n        '''\n        for tabIndex in range(self.gui.tabWidget.count()):\n            if self.gui.tabWidget.tabText(tabIndex) == \"Output\":\n                self.tabIndex = tabIndex\n    \n    def setProject(self, project=None):\n        ''' setting the new project on which the tab is based\n        '''\n        self.project = project\n        if self.project:\n            # write everything on disk\n            self.project.sync()\n            \n            # set some globals\n            confFileName = self.project.fileName()\n            self.projectPath = os.path.dirname(confFileName)\n            \n            # emit configurationLoaded with the status of loading\n            self.setTabGUIBasingOnProject()\n            \n            # enable current tab because project has been loaded\n            self.gui.tabWidget.setTabEnabled(self.tabIndex, True)\n        \n        else:\n            # disable current tab because no project has been loaded yet\n            self.gui.tabWidget.setTabEnabled(self.tabIndex, False)\n    \n    def selectNewOutFile(self):\n        ''' Select the the file\n        '''\n        oldOutputLayer = self.gui.outFile_LEdit.text()\n        \n        # get last conf to start from its path\n        startPath = self.projectPath\n        newOutputLayer = self.project.value('Processing.OutputFileDefinition/newOutputLayer', '')\n        if newOutputLayer:\n            startPath = os.path.dirname( newOutputLayer )\n        \n        # ask for the new out file\n        newOutputLayer = QtGui.QFileDialog.getSaveFileName(self.gui, \"Select an autput file\", startPath, \n                                                        self.plugin.tr(\"All (*)\"))\n        if not newOutputLayer:\n            return\n        \n        if oldOutputLayer == newOutputLayer:\n            return\n        \n        # add shp extension\n        components = os.path.splitext(newOutputLayer)\n        if len(components) == 1:\n            newOutputLayer = newOutputLayer + '.shp'\n        else:\n            if components[1] != '.shp':\n                newOutputLayer = components[0] + '.shp'\n        \n        # update gui\n        self.gui.outFile_LEdit.setText(newOutputLayer)\n        \n        self.saveTabOnProject()\n\n    def setInputLayer(self):\n        ''' Set GUI basing on toggled ComboBox to set if the layer is the input one \n        or have to select a new one \n        '''\n        self.gui.outFile_LEdit.setEnabled( self.gui.createNewLayer_RButton.isChecked() )\n        self.gui.selectOutfile_TButton.setEnabled( self.gui.createNewLayer_RButton.isChecked() )\n            \n    def setTabGUIBasingOnProject(self):\n        '''Set tab basing on project conf\n        '''\n        if not self.project:\n            return\n        \n        # get conf parameters\n        addToInputLayer = self.project.value('Processing.OutputFileDefinition/addToInputLayer', False, bool)\n        newOutputLayer = self.project.value('Processing.OutputFileDefinition/newOutputLayer', '')\n\n        Gasoline_Consumption = self.project.value('Processing.Parameters/Gasoline_Consumption', False, bool)\n        Diesel_Consumption = self.project.value('Processing.Parameters/Diesel_Consumption', False, bool)\n        LPG_Consumption = self.project.value('Processing.Parameters/LPG_Consumption', False, bool)\n        NewFuel_Consumption = self.project.value('Processing.Parameters/NewFuel_Consumption', False, bool)\n        Fuel_Consumption_Total = self.project.value('Processing.Parameters/Fuel_Consumption_Total', False, bool)\n        Energy_Consumption = self.project.value('Processing.Parameters/Energy_Consumption', False, bool)\n        \n        CO = self.project.value('Processing.Parameters/CO', False, bool)\n        NOx = self.project.value('Processing.Parameters/NOx', False, bool)\n        NMVOC = self.project.value('Processing.Parameters/NMVOC', False, bool)\n        CO2 = self.project.value('Processing.Parameters/CO2', False, bool)\n        CH4 = self.project.value('Processing.Parameters/CH4', False, bool)\n        N2O = self.project.value('Processing.Parameters/N2O', False, bool)\n        NH3 = self.project.value('Processing.Parameters/NH3', False, bool)\n        SO2 = self.project.value('Processing.Parameters/SO2', False, bool)\n        PM25 = self.project.value('Processing.Parameters/PM', False, bool)\n        C6H6 = self.project.value('Processing.Parameters/C6H6', False, bool)\n        \n        # avoid emitting signal in case of reset of indexes\n        try:\n            # to avoid add multiple listener, remove previous listener\n            self.gui.addToOriginaLayer_RButton.toggled.disconnect(self.saveTabOnProject)\n            self.gui.createNewLayer_RButton.toggled.disconnect(self.saveTabOnProject)\n            self.gui.outFile_LEdit.returnPressed.disconnect(self.saveTabOnProject)\n\n            self.gui.fuelEnergyConsumptionGasoline_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.fuelEnergyConsumptionDiesel_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.fuelEnergyConsumptionLPG_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.fuelEnergyConsumptionNewFuels_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.totalFuelConsumption_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.energyConsumption_CBox.clicked.disconnect(self.saveTabOnProject)\n\n            self.gui.pollutantsCO_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsNOx_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsNMVOCs_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsCO2_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsCH4_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsN2O_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsNH3_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsSO2_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsPM25_CBox.clicked.disconnect(self.saveTabOnProject)\n            self.gui.pollutantsC6H6_CBox.clicked.disconnect(self.saveTabOnProject)\n        except (Exception) as ex:\n            pass\n        \n        # now populate interface\n        self.gui.addToOriginaLayer_RButton.setChecked( addToInputLayer )\n        self.gui.createNewLayer_RButton.setChecked( not addToInputLayer )\n        self.gui.outFile_LEdit.setText( newOutputLayer )\n\n        self.gui.fuelEnergyConsumptionGasoline_CBox.setChecked( Gasoline_Consumption )\n        self.gui.fuelEnergyConsumptionDiesel_CBox.setChecked( Diesel_Consumption )\n        self.gui.fuelEnergyConsumptionLPG_CBox.setChecked( LPG_Consumption )\n        self.gui.fuelEnergyConsumptionNewFuels_CBox.setChecked( NewFuel_Consumption )\n        self.gui.totalFuelConsumption_CBox.setChecked( Fuel_Consumption_Total )\n        self.gui.energyConsumption_CBox.setChecked( Energy_Consumption )\n\n        self.gui.pollutantsCO_CBox.setChecked( CO )\n        self.gui.pollutantsNOx_CBox.setChecked( NOx )\n        self.gui.pollutantsNMVOCs_CBox.setChecked( NMVOC )\n        self.gui.pollutantsCO2_CBox.setChecked( CO2 )\n        self.gui.pollutantsCH4_CBox.setChecked( CH4 )\n        self.gui.pollutantsN2O_CBox.setChecked( N2O )\n        self.gui.pollutantsNH3_CBox.setChecked( NH3 )\n        self.gui.pollutantsSO2_CBox.setChecked( SO2 )\n        self.gui.pollutantsPM25_CBox.setChecked( PM25 )\n        self.gui.pollutantsC6H6_CBox.setChecked( C6H6 )\n        \n        # add all modification events to notify project modification\n        self.gui.addToOriginaLayer_RButton.toggled.connect(self.saveTabOnProject)\n        self.gui.createNewLayer_RButton.toggled.connect(self.saveTabOnProject)\n        self.gui.outFile_LEdit.returnPressed.connect(self.saveTabOnProject)\n\n        self.gui.fuelEnergyConsumptionGasoline_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.fuelEnergyConsumptionDiesel_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.fuelEnergyConsumptionLPG_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.fuelEnergyConsumptionNewFuels_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.totalFuelConsumption_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.energyConsumption_CBox.clicked.connect(self.saveTabOnProject)\n\n        self.gui.pollutantsCO_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsNOx_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsNMVOCs_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsCO2_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsCH4_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsN2O_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsNH3_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsSO2_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsPM25_CBox.clicked.connect(self.saveTabOnProject)\n        self.gui.pollutantsC6H6_CBox.clicked.connect(self.saveTabOnProject)\n        \n        # if and output file is specified and it's available => load it\n        if newOutputLayer and os.path.exists(newOutputLayer):\n            \n            # check if layer is already loaded in layer list checking it's source\n            found = False\n            for layerName, layer in QgsMapLayerRegistry.instance().mapLayers().items():\n                if newOutputLayer == layer.publicSource():\n                    # set the found layer as roadLayer\n                    self.outLayer = layer\n                    self.outLayerId = self.outLayer.id()\n                    found = True\n                    break\n            \n            if not found:\n                # get layer name to set as public name in the legend\n                layerName = os.path.splitext( os.path.basename(newOutputLayer) )[0]\n                    \n                # load layer\n                self.outLayer = QgsVectorLayer(newOutputLayer, layerName, 'ogr')\n                if not self.outLayer.isValid():\n                    message = self.tr(\"Error loading layer: %s\" % self.outLayer.error().message(QgsErrorMessage.Text))\n                    iface.messageBar().pushMessage(message, QgsMessageBar.CRITICAL)\n                    return\n                \n                self.outLayerId = self.outLayer.id()\n\n                # show layer in the canvas\n                QgsMapLayerRegistry.instance().addMapLayer(self.outLayer)\n        \n    def saveTabOnProject(self):\n        ''' Save tab configuration in the project basing on GUI values\n        '''\n        # get values from the GUI\n        addToInputLayer = self.gui.addToOriginaLayer_RButton.isChecked()\n        newOutputLayer = self.gui.outFile_LEdit.text()\n\n        Gasoline_Consumption = self.gui.fuelEnergyConsumptionGasoline_CBox.isChecked()\n        Diesel_Consumption = self.gui.fuelEnergyConsumptionDiesel_CBox.isChecked()\n        LPG_Consumption = self.gui.fuelEnergyConsumptionLPG_CBox.isChecked()\n        NewFuel_Consumption = self.gui.fuelEnergyConsumptionNewFuels_CBox.isChecked()\n        Fuel_Consumption_Total = self.gui.totalFuelConsumption_CBox.isChecked()\n        Energy_Consumption = self.gui.energyConsumption_CBox.isChecked()\n        \n        CO = self.gui.pollutantsCO_CBox.isChecked()\n        NOx = self.gui.pollutantsNOx_CBox.isChecked()\n        NMVOC = self.gui.pollutantsNMVOCs_CBox.isChecked()\n        CO2 = self.gui.pollutantsCO2_CBox.isChecked()\n        CH4 = self.gui.pollutantsCH4_CBox.isChecked()\n        N2O = self.gui.pollutantsN2O_CBox.isChecked()\n        NH3 = self.gui.pollutantsNH3_CBox.isChecked()\n        SO2 = self.gui.pollutantsSO2_CBox.isChecked()\n        PM25 = self.gui.pollutantsPM25_CBox.isChecked()\n        C6H6 = self.gui.pollutantsC6H6_CBox.isChecked()\n        \n        # set conf parameters\n        self.project.setValue('Processing.OutputFileDefinition/addToInputLayer', int(addToInputLayer))\n        self.project.setValue('Processing.OutputFileDefinition/newOutputLayer', newOutputLayer)\n        \n        self.project.setValue('Processing.Parameters/Gasoline_Consumption', int(Gasoline_Consumption))\n        self.project.setValue('Processing.Parameters/Diesel_Consumption', int(Diesel_Consumption))\n        self.project.setValue('Processing.Parameters/LPG_Consumption', int(LPG_Consumption))\n        self.project.setValue('Processing.Parameters/NewFuel_Consumption', int(NewFuel_Consumption))\n        self.project.setValue('Processing.Parameters/Fuel_Consumption_Total', int(Fuel_Consumption_Total))\n        self.project.setValue('Processing.Parameters/Energy_Consumption', int(Energy_Consumption))\n        \n        self.project.setValue('Processing.Parameters/CO', int(CO))\n        self.project.setValue('Processing.Parameters/NOx', int(NOx))\n        self.project.setValue('Processing.Parameters/NMVOC', int(NMVOC))\n        self.project.setValue('Processing.Parameters/CO2', int(CO2))\n        self.project.setValue('Processing.Parameters/CH4', int(CH4))\n        self.project.setValue('Processing.Parameters/N2O', int(N2O))\n        self.project.setValue('Processing.Parameters/NH3', int(NH3))\n        self.project.setValue('Processing.Parameters/SO2', int(SO2))\n        self.project.setValue('Processing.Parameters/PM', int(PM25))\n        self.project.setValue('Processing.Parameters/C6H6', int(C6H6))\n            \n        # notify project modification\n        self.projectModified.emit()\n    \n    def calculate(self):\n        ''' Prepare environment to run the alg and run it. After run, merge produced \n        data basing on plugin configuration.\n        Before calculation a parametere validation will be executed\n        '''\n        # perform validation\n        if not self.gui.validate():\n            return\n        else:\n            # notify successful validation\n            message = self.tr(\"QTraffic: Parameters validation passed successfully\")\n            iface.messageBar().pushMessage(message, QgsMessageBar.SUCCESS)\n        \n        # set number of classes in the project config (that is the temporary one... but equal to the official one)\n        fleetDistributionRoadTypes = self.gui.getRoadTypes()\n        self.project.setValue('Processing.Parameters/maximum_type', len(fleetDistributionRoadTypes))\n        self.project.sync()\n\n        # create the algorithm\n        self.alg = Algorithm()\n        roadLayer = self.gui.getRoadLayer()\n        \n        # prepare layer where to add result\n        addToInputLayer = self.gui.addToOriginaLayer_RButton.isChecked()\n        newOutputLayer = self.gui.outFile_LEdit.text()\n\n        if addToInputLayer:\n            self.outLayer = roadLayer\n            self.outLayerId = self.outLayer.id()\n        else:\n            # if layer is present... remove it\n            # out layer would not be the same of input road layer... in thi scase don't remove it\n            if self.outLayer and self.outLayer.isValid():\n                # to be sure, remove only if roadLayer and outLayer are different\n                if self.outLayer.publicSource() != roadLayer.publicSource():\n                    self.outLayerRemoved = False\n                    QgsMapLayerRegistry.instance().layerRemoved.connect(self.checkOutLayerRemoved)\n                    QgsMapLayerRegistry.instance().removeMapLayer(self.outLayer.id())\n                    \n                    # remove file when it has been removed from qgis\n                    while not self.outLayerRemoved:\n                        sleep(0.1)\n                    QgsMapLayerRegistry.instance().layerRemoved.disconnect(self.checkOutLayerRemoved)\n                        \n                    # reinit outLayer variables\n                    # If not, under windws remain a locking of the related file creating\n                    # an error during QgsVectorFileWriter.deleteShapeFile\n                    self.outLayer = None\n                    self.outLayerId = None\n\n                    if os.path.exists(newOutputLayer):\n                        if not QgsVectorFileWriter.deleteShapeFile(newOutputLayer):\n                            message = self.tr(\"Error removing shape: {}\".format(newOutputLayer))\n                            iface.messageBar().pushMessage(message, QgsMessageBar.CRITICAL)\n                            return\n            \n            # copy input layer to the new one\n            writeError = QgsVectorFileWriter.writeAsVectorFormat(roadLayer, newOutputLayer, 'utf-8',  roadLayer.crs())\n            if writeError != QgsVectorFileWriter.NoError:\n                message = self.tr('Error writing vector file {}'.format(newOutputLayer))\n                QgsMessageLog.logMessage(message, 'QTraffic', QgsMessageLog.CRITICAL)\n                iface.messageBar().pushCritical('QTraffic', message)\n                return\n            \n            # load the layer\n            newLayerName =os.path.splitext(os.path.basename(  newOutputLayer ))[0]\n            self.outLayer = QgsVectorLayer(newOutputLayer, newLayerName, 'ogr')\n            if not self.outLayer.isValid():\n                message = self.tr('Error loading vector file {}'.format(newOutputLayer))\n                QgsMessageLog.logMessage(message, 'QTraffic', QgsMessageLog.CRITICAL)\n                iface.messageBar().pushCritical('QTraffic', message)\n                return\n    \n            self.outLayerId = self.outLayer.id()\n            \n        # prepare environment\n        try:\n            self.alg.setProject(self.project)\n            self.alg.setLayer( roadLayer )\n            self.alg.initConfig()\n            self.alg.prepareRun()\n        except Exception as ex:\n            traceback.print_exc()\n            message = self.tr('Error preparing running contex for the algoritm: %s' % str(ex))\n            QgsMessageLog.logMessage(message, 'QTraffic', QgsMessageLog.CRITICAL)\n            iface.messageBar().pushCritical('QTraffic', message)\n            return\n        \n        # run the self.alg\n        self.thread = QtCore.QThread(self)\n        self.thread.started.connect(self.alg.run)\n        self.thread.finished.connect(self.threadCleanup)\n        self.thread.terminated.connect(self.threadCleanup)\n        \n        self.alg.moveToThread(self.thread)\n        self.alg.started.connect(self.manageStarted)\n        self.alg.progress.connect(self.manageProgress)\n        self.alg.message.connect(self.manageMessage)\n        self.alg.error.connect(self.manageError)\n        self.alg.finished.connect(self.manageFinished)\n\n        # set wait cursor and start\n        QgsApplication.instance().setOverrideCursor(QtCore.Qt.WaitCursor)\n        self.thread.start()\n    \n    def checkOutLayerRemoved(self, layerId):\n        ''' check when outLayer has been removed settign semaphore to True\n        '''\n        if self.outLayerId == layerId:\n            QgsMessageLog.logMessage('Completly removed outLayer', 'QTraffic', QgsMessageLog.INFO)\n            self.outLayerRemoved = True\n        \n    def threadCleanup(self):\n        ''' cleanup after thread run\n        '''\n        # restore cursor\n        QgsApplication.instance().restoreOverrideCursor()\n        \n        if self.alg:\n            self.alg.deleteLater()\n            self.alg = None\n        self.thread.wait()\n        self.thread.deleteLater()\n        \n        # remove progress bar\n        if self.progressMessageBarItem:\n            iface.messageBar().popWidget(self.progressMessageBarItem)\n            self.progressMessageBarItem = None\n        \n    def manageStarted(self):\n        ''' Setup GUI env to notify processing progress\n        '''\n        message = self.tr('Processing started')\n        QgsMessageLog.logMessage(message, 'QTraffic', QgsMessageLog.INFO)\n        \n        # create message bar to show progress\n        self.progressMessageBarItem = iface.messageBar().createMessage(self.tr('Executing alg'))\n        self.progress = QtGui.QProgressBar()\n        self.progress.setMaximum(0)\n        self.progress.setMinimum(0)\n        self.progress.setAlignment(QtCore.Qt.AlignLeft | QtCore.Qt.AlignVCenter)\n        self.progressMessageBarItem.layout().addWidget(self.progress)\n        iface.messageBar().pushWidget(self.progressMessageBarItem, QgsMessageBar.INFO)\n\n    def manageProgress(self, percentage):\n        ''' Update progress bar basing on alg nofify\n        '''\n        self.progress.setValue(percentage)\n    \n    def manageMessage(self, message, msgType): \n        ''' expose alg thread messages to gui and log\n        '''\n        QgsMessageLog.logMessage(message, 'QTraffic', msgType)\n        \n        duration = 0\n        if msgType == QgsMessageLog.INFO:\n            return\n        \n        if msgType == QgsMessageLog.CRITICAL:\n            msgType = QgsMessageBar.CRITICAL\n        \n        if msgType == QgsMessageLog.WARNING:\n            duration = 3\n            msgType = QgsMessageBar.WARNING\n\n        iface.messageBar().pushMessage(message, msgType, duration)\n            \n    def manageError(self, ex, exceptionMessage):\n        ''' Do actions in case of alg thread error. Now only notify exception \n        '''\n        QgsMessageLog.logMessage(exceptionMessage, 'QTraffic', QgsMessageLog.CRITICAL)\n        iface.messageBar().pushMessage(exceptionMessage, QgsMessageBar.CRITICAL)\n        \n    def manageFinished(self, success, reason):\n        ''' Do action after notify that alg is finished. These are the postprocessing steps\n        1) merge result to the output layer\n        2) add the layer to canvas in case it is new\n        3) notify edn of processing\n        4) terminate the thread\n        '''\n        # finish the thread\n        self.thread.quit()\n        \n        # check result\n        if not success:\n            QgsMessageLog.logMessage('Failed execution: {}'.format(reason), 'QTraffic', QgsMessageLog.CRITICAL)\n            iface.messageBar().pushCritical('QTraffic', self.tr(\"Error executing the algorithm: {}\".format(reason)))\n            return\n        \n        # prepare result\n        try:\n            self.alg.addResultToLayer(self.outLayer)\n        except Exception as ex:\n            traceback.print_exc()\n            QgsMessageLog.logMessage('Cannot add result to layer: {}'.format(str(ex)), 'QTraffic', QgsMessageLog.CRITICAL)\n            iface.messageBar().pushCritical('QTraffic', self.tr(\"Cannot add result to layer\"))\n            return\n        \n        # add or refresh rsult vector layer\n        addToInputLayer = self.gui.addToOriginaLayer_RButton.isChecked()\n        if not addToInputLayer:\n            QgsMapLayerRegistry.instance().addMapLayer(self.outLayer)\n        iface.mapCanvas().refresh()\n        \n        # notify the user the end of process\n        iface.messageBar().pushSuccess('QTraffic', self.tr('Alg terminated successfully'))\n        \n    def validate(self):\n        ''' pre calcluation validation related only to this tab\n        Mandatory:\n            At least an output parameters have to be set\n            if \"create new layer\" a new layer name have to be set\n        '''\n        addToInputLayer = self.gui.addToOriginaLayer_RButton.isChecked()\n        newOutputLayer = self.gui.outFile_LEdit.text()\n        if (not addToInputLayer and\n            not newOutputLayer):\n            message = self.tr('No output vector specified')\n            iface.messageBar().pushMessage(message, QgsMessageBar.CRITICAL)\n            return False\n        \n        Gasoline_Consumption = self.gui.fuelEnergyConsumptionGasoline_CBox.isChecked()\n        Diesel_Consumption = self.gui.fuelEnergyConsumptionDiesel_CBox.isChecked()\n        LPG_Consumption = self.gui.fuelEnergyConsumptionLPG_CBox.isChecked()\n        NewFuel_Consumption = self.gui.fuelEnergyConsumptionNewFuels_CBox.isChecked()\n        Fuel_Consumption_Total = self.gui.totalFuelConsumption_CBox.isChecked()\n        Energy_Consumption = self.gui.energyConsumption_CBox.isChecked()\n        \n        CO = self.gui.pollutantsCO_CBox.isChecked()\n        NOx = self.gui.pollutantsNOx_CBox.isChecked()\n        NMVOC = self.gui.pollutantsNMVOCs_CBox.isChecked()\n        CO2 = self.gui.pollutantsCO2_CBox.isChecked()\n        CH4 = self.gui.pollutantsCH4_CBox.isChecked()\n        N2O = self.gui.pollutantsN2O_CBox.isChecked()\n        NH3 = self.gui.pollutantsNH3_CBox.isChecked()\n        SO2 = self.gui.pollutantsSO2_CBox.isChecked()\n        PM25 = self.gui.pollutantsPM25_CBox.isChecked()\n        C6H6 = self.gui.pollutantsC6H6_CBox.isChecked()\n\n        if (not Gasoline_Consumption and\n            not Diesel_Consumption and\n            not LPG_Consumption and\n            not NewFuel_Consumption and\n            not Fuel_Consumption_Total and\n            not Energy_Consumption and\n            not CO and\n            not NOx and\n            not NMVOC and\n            not CO2 and\n            not CH4 and\n            not N2O and\n            not NH3 and\n            not SO2 and\n            not PM25 and\n            not C6H6 ):\n            message = self.tr(\"Validation error: At least an output parameter have to be selected\")\n            iface.messageBar().pushMessage(message, QgsMessageBar.CRITICAL)\n            return False\n        \n        return True\n        \n    def tr(self, string, context=''):\n        if not context:\n            context = 'QTraffic'\n        return QtCore.QCoreApplication.translate(context, string)\n","repo_name":"luipir/QTraffic","sub_path":"output_tab_manager.py","file_name":"output_tab_manager.py","file_ext":"py","file_size_in_byte":26932,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6527932688","text":"from datetime import date as Date\nfrom datetime import datetime as DateTime\n\nfrom gathernomics.utils import tryint, scalar_multiplier\nfrom gathernomics.filters.base import FilterBase\nfrom gathernomics.models.factor import TemporalFrequency\n\nADJUSTMENT_KEY = \"Seasonal adjustment\"\nADJUSTMENT_VALUE = \"Seasonally adjusted\"\n\nGEO_KEY = \"GEO\"\nGEO_VALUE = \"Canada\"\n\nBASIS_KEY = \"Basis\"\nBASIS_VALUE = \"Balance of payments\"\n\nPARTNERS_KEY = \"Principal trading partners\"\nPARTNERS_VALUE = \"Total of all merchandise\"\n\nTRADE_KEY = \"Trade\"\nTRADE_VALUES = [\"Import\", \"Export\"]\n\nVALUE_KEY = \"VALUE\"\nSCALAR_KEY = \"SCALAR_FACTOR\"\nDATE_KEY = \"REF_DATE\"\n\n\nclass ImportExportFilter(FilterBase):\n    \"\"\"Import Export Filter.\"\"\"\n    def __init__(\n            self,\n            csv_path: str,\n            category: str,\n            indicator: str,\n            frequency: TemporalFrequency):\n        super().__init__(csv_path=csv_path)\n        self.category = category\n        self.indicator = indicator\n        self.frequency = frequency\n\n    def isValid(self, row: dict) -> bool:\n        if (ADJUSTMENT_KEY not in row or\n                row[ADJUSTMENT_KEY] != ADJUSTMENT_VALUE):\n            return False\n        if (GEO_KEY not in row or\n                row[GEO_KEY] != GEO_VALUE):\n            return False\n        if BASIS_KEY not in row or row[BASIS_KEY] != BASIS_VALUE:\n            return False\n        if PARTNERS_KEY not in row or row[PARTNERS_KEY] != PARTNERS_VALUE:\n            return False\n        if TRADE_KEY not in row or row[TRADE_KEY] not in TRADE_VALUES:\n            return False\n        if (VALUE_KEY not in row or\n                SCALAR_KEY not in row or\n                DATE_KEY not in row):\n            return False\n        return True\n\n    def getIndicator(self, row) -> str:\n        return row[TRADE_KEY].lower()\n\n    def getCategory(self, row) -> str:\n        return row[TRADE_KEY].lower()\n\n    def getFrequency(self, _) -> TemporalFrequency:\n        return self.frequency\n\n    def getValue(self, row) -> int:\n        value = float(row[VALUE_KEY])\n        multiplier = scalar_multiplier(row[SCALAR_KEY])\n        return int(value * multiplier)\n\n    def getDate(self, row: dict) -> Date:\n        date_string = row[DATE_KEY]\n        dt = DateTime.strptime(date_string, \"%Y-%m\")\n        return dt.date()\n","repo_name":"SuperOxigen/CanDev-Finance-Canada","sub_path":"gathernomics/filters/ieport.py","file_name":"ieport.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38244057574","text":"import time\nfrom datetime import datetime\nfrom flask import Flask, request, abort\n\napp = Flask(__name__)\ndb = [\n    {\n        'time' : time.time(),\n        'name' : 'Jack',\n        'text' : 'Hello everyone',\n    },\n    {\n        'time': time.time(),\n        'name': 'Jack',\n        'text': 'Hello everyone',\n    },\n]\n\n@app.route(\"/\")\ndef hello():\n    return \"Hello, World! 1234567\"\n\n@app.route(\"/status\")\ndef status():\n    dt_now = datetime.now()\n    return {\n        'status' : True,\n        'name' : 'Messenger',\n        'time1' : time.asctime(),\n        'time2' : time.time(),\n        'time3' : dt_now,\n        'time4' : str(dt_now),\n        'time5' : dt_now.strftime('%Y/%m/%d time: %H:%M:%S'),\n        'time6' : dt_now.isoformat()\n    }\n\n@app.route(\"/send\", methods=['POST'])\ndef send_message():\n    data = request.json\n\n    if not isinstance(data, dict):\n        return abort(400)\n    if 'name' not in data or 'text' not in data:\n        return abort(400)\n    if len(data) != 2:\n        return abort(400)\n\n    name = data['name']\n    text = data['text']\n\n    if not isinstance(name, str) or not isinstance(text, str) or \\\n            name == '' or text == '':\n        return abort(400)\n\n    message = {\n        'time': time.time(),\n        'name': name,\n        'text': text,\n    }\n    db.append(message)\n    return {\"ok\" : True}\n\n@app.route(\"/messages\")\ndef get_messages():\n\n    try:\n        after = float(request.args['after'])\n    except:\n        return  abort(400)\n\n    result = []\n    for messsage in db:\n        if messsage['time'] > after:\n            result.append(messsage)\n            if len(result) >= 100:\n                break\n\n    return {'messages' : result}\n\napp.run()","repo_name":"MikePagorb/Messenger","sub_path":"Messenger/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13987587777","text":"from django.shortcuts import render\nfrom django.http import HttpRequest  # optional import\nfrom .models import Watch\n# created a view for store-index page\n\ndef index(request):\n    return render(request,\"index.html\")\n\n# creating a view for dukaan\n\n# create a view for stock and populate the stuff from models.py\n\ndef stock(request):\n    count = Watch.objects.all().count()\n    total_count = Watch.objects.all()\n\n    context = {\n        'count': count,\n        'totalcount' : total_count,\n        'title' : \"This is stock views page\",\n        'Fossil' : total_count[0],\n        'second' : total_count[1],\n        'third' : total_count[2],\n        'fourth' : total_count[3],\n    }\n    return render(request, 'stock.html',context)\n\n# View for dukaanam\n\ndef dukaanam(request):\n    return render(request, \"dukaan.html\")\n\n\n# def registration(request):\n#     return render(request)\n\n\n\n","repo_name":"srisreedhar/django_watchstore","sub_path":"dukaan/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29113116148","text":"import streamlit as st\nimport io\nimport fitz\nimport openai\nimport os\n\nopenai.api_key = os.getenv(\"API_KEY\")\nst.markdown(\n    f'''\n        <style>\n            .sidebar .sidebar-content {{\n                width: 575px;\n            }}\n        </style>\n    ''',\n    unsafe_allow_html=True\n)\n\n# Initialize session state\nif \"widget_outputs\" not in st.session_state:\n    st.session_state.widget_outputs = {\n        \"Widget 1\": \"\",\n        \"Widget 2\": \"\",\n        \"Widget 3\": \"\",\n        \"Widget 4\": \"\",\n    }\n\ndef generate_pdf(sections):\n    # Create a new PDF document\n    doc = fitz.open()\n\n    # Define the style for the section headings and text\n    style_heading = \"<style>\\nh1 {font-family: Arial, sans-serif; font-size: 20pt; font-weight: bold; color: #000000;}\\n</style>\"\n    style_text = \"<style>\\np {font-family: Arial, sans-serif; font-size: 12pt; color: #000000;}\\n</style>\"\n\n    # Define the page template for each section\n    template = style_heading + style_text + \"<h1>{}</h1><p>{}</p>\"\n\n    # Add a new page for each section\n    for section in sections:\n\n        if \":\" in section:\n\n            heading, text = section.split(\":\", 1)\n        else:\n            \n            heading, text = section, \"\"\n        # Create a new page\n        page = doc.new_page()\n\n        # Draw the section heading and text on the page\n        p1 = fitz.Point(50, 100)\n        p2 = fitz.Point(50, 150)\n        page.insert_text(p1, heading)\n        page.insert_text(p2, text)\n\n    # Save the document to a buffer\n    buffer = io.BytesIO()\n    doc.save(buffer)\n\n    # Close the document\n    doc.close()\n\n    # Set the buffer position to the beginning\n    buffer.seek(0)\n\n    return buffer\n\n\n\ndef generate_text(input):\n    response = openai.Completion.create(\n        model=\"text-davinci-003\",\n        prompt=input,\n        temperature=0.56,\n        max_tokens=2066,\n        top_p=1,\n        frequency_penalty=0.35,\n        presence_penalty=0\n    )\n    return response.choices[0].text\n\n\n# Define the expandable widgets\ndef app():\n    # Set the app title\n    st.title(\"QuickBOM.ai\")\n\n    widget1 = st.expander(\"Widget 1\")\n    widget2 = st.expander(\"Widget 2\")\n    widget3 = st.expander(\"Widget 3\")\n    widget4 = st.expander(\"Widget 4\")\n\n    # Define the query text area and submit button\n    query = st.sidebar.text_area(\"Ask Something\", key=\"query\")\n    submit = st.sidebar.button(\"Submit\")\n\n    # Define the widget selection drop-down\n    widget_select = st.sidebar.selectbox(\"Select a widget\", [\"Widget 1\", \"Widget 2\", \"Widget 3\", \"Widget 4\"])\n\n    # Generate text and display in the selected widget\n    if submit:\n        with st.spinner('Generating...'):\n            output = generate_text(query)\n\n            # Update the selected widget output in session state\n            st.session_state.widget_outputs[widget_select] = output\n\n            # Update all the widget outputs\n            widget1.write(st.session_state.widget_outputs[\"Widget 1\"])\n            widget2.write(st.session_state.widget_outputs[\"Widget 2\"])\n            widget3.write(st.session_state.widget_outputs[\"Widget 3\"])\n            widget4.write(st.session_state.widget_outputs[\"Widget 4\"])\n\n    # Collect the outputs of all the widgets into a list\n    widget_outputs = [\n        st.session_state.widget_outputs[\"Widget 1\"],\n        st.session_state.widget_outputs[\"Widget 2\"],\n        st.session_state.widget_outputs[\"Widget 3\"],\n        st.session_state.widget_outputs[\"Widget 4\"],\n    ]\n\n    # Add a button to generate the PDF file\n    if st.button(\"Generate PDF\"):\n        # Generate the PDF file\n        buffer = generate_pdf(widget_outputs)\n\n        # Download the PDF file\n        st.download_button(\n            label=\"Download PDF\",\n            data=buffer,\n            file_name=\"example.pdf\",\n            mime=\"application/pdf\"\n        )\n\nif __name__ == \"__main__\":\n    app()\n","repo_name":"SajithJude/QUICKBOM","sub_path":"Home.py","file_name":"Home.py","file_ext":"py","file_size_in_byte":3846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28034692388","text":"import numpy as no\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10,20)\ny = np.array([2, 1, 4, 5, 8, 12, 18, 25, 96, 48])\n\nr = np.corrcoef(x,y)\n#plot the data points\nplt.plot(x,y,'o')\n#define slope and intercept\nm,b = np.polyfit(x,y,1)\nplt.plot(x, m*x+b)\nplt.show()","repo_name":"iMari0/study_notes","sub_path":"python/Correlation/correlation_np.py","file_name":"correlation_np.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8237005359","text":"import pandas as pd, sys\nimport numpy as np\nfrom settings import *\n\nclass LogisticRegression:\n    def __init__(self):\n        self.train_x = []\n        self.train_y = []\n        # y = m * x + b\n        self.theta = []\n        # update times\n        self.epoch = 5000\n        # learning rate\n        self.ETA = 0.01\n        self.accuracy = 0\n        self.log = []\n\n    def get_datas(self, path=TRAINING_PATH, year=2017):\n        datas = pd.read_csv(path)\n        datas = datas[datas['Year'] == year]\n        week_return = []\n        adj_close = datas['Close'].iloc[0]\n        data = []\n        for i in range(len(datas)):\n            week_return.append(datas['Return'].iloc[i])\n            if datas['Weekday'].iloc[i] == 'Friday':\n                mean = round(np.mean(week_return) * 100, 2)\n                sd = round(np.std(week_return) * 100, 2)\n                color = datas['Label'].iloc[i][0].lower()\n                data.append([mean, sd, int(color == 'g')])\n                adj_close = datas['Close'].iloc[i]\n                week_return = []\n        data = np.array(data)\n        self.train_x = data[:, 0:2]\n        self.train_y = data[:, 2]\n        self.log.append((self.train_x, self.train_y))\n        \n    def add_data(self, new_x=None,new_y=None):\n        self.train_x = list(self.train_x)\n        self.train_y = list(self.train_y)\n        if new_x[0] != None:\n            self.train_x.append(new_x)\n            self.train_y.append(new_y)\n        self.train_x = np.array(self.train_x)\n        self.train_y = np.array(self.train_y)\n        self.log.append((self.train_x, self.train_y))\n\n    def delete_data(self, new_x=None, new_y=None):\n        self.train_x = list(self.train_x)\n        self.train_y = list(self.train_y)\n        min_dis = 1000\n        pos = -1\n        if new_x[0] != None:\n            for i in range(len(self.train_x)):\n                d = self.distance(self.train_x[i], new_x)\n                if min_dis > d:\n                    min_dis = d\n                    pos = i\n            if pos != -1:\n                self.train_x.pop(pos)\n                self.train_y.pop(pos)\n        self.train_x = np.array(self.train_x)\n        self.train_y = np.array(self.train_y)\n        self.log.append((self.train_x, self.train_y))\n\n    def fit(self):\n        # initialize parameter\n        self.theta = np.zeros(self.train_x.shape[1]+1)\n         # # standardization\n        # mu = self.train_x.mean(axis=0)\n        # sigma = self.train_x.std(axis=0)\n        # std_x = self.standardizer(self.train_x, mu, sigma)\n        self.mat_x = self.to_matrix(self.train_x)\n        \n    def standardizer(self, x, mu, sigma):\n        return (x - mu) / sigma\n\n    # get matrix\n    def to_matrix(self, std_x):\n        return np.array([[1, x1, x2] for x1, x2 in std_x])\n\n    # sigmoid function\n    def __sigmoid(self, z):\n        return 1 / (1 + np.exp(-z))\n        \n    def get_line(self, game):\n        z = np.dot(self.mat_x, self.theta)\n        h = self.__sigmoid(z)\n\n        gradient = np.dot(self.mat_x.T, (h - self.train_y)) / len(self.train_y)\n        self.theta -= self.ETA * gradient\n        # self.theta = self.theta - self.ETA * np.dot(self.f(self.mat_x) - self.train_y, self.mat_x)\n        y_new = []\n        y_altered = []\n        for x, y in self.train_x:\n            new_y = -(self.theta[0] + x * self.theta[1]) / self.theta[2]\n            if new_y >= y:\n                y_new.append(1)\n            else:\n                y_new.append(0)\n            y_altered.append(1 - y_new[-1])\n        self.accuracy = max(self.get_accuracy(y_new, self.train_y), self.get_accuracy(y_altered, self.train_y))\n        x1 = np.linspace(-2, 3, 100)\n        x2 = - (self.theta[0] + x1 * self.theta[1]) / self.theta[2]\n        return (x1, x2)\n\n    def get_accuracy(self, pred, test_y):\n        correct = 0\n        for i in range(len(test_y)):\n            correct += test_y[i] == pred[i]\n        return  round((correct / len(test_y)) * 100, 2)\n\n    def distance(self, x, y):\n        return ((x[0] - y[0]) ** 2 + (x[1] - y[1]) ** 2) ** 0.5 ","repo_name":"Nkhinatolla/projects","sub_path":"Logistic regression/linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":4029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23171094379","text":"\"\"\"\nVisualizer module.\n\"\"\"\n\nimport os\nimport logging\nimport logging.config\n\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport Tkinter as tk\nfrom matplotlib.backends.backend_tkagg import \\\n    FigureCanvasTkAgg, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\nimport tkFileDialog\nimport ttk\nimport numpy as np\nimport pandas as pd\n\nimport common\n\n\ndef data_viewer(folder=os.getcwd()):\n    \"\"\"Creates and shows DataViewer window.\n\n    Args:\n      folder (string): Folder at which to create data viewer.\n      parent (Tk toplevel, optional): Parent window for this data viewer.\n        Use when you want to specify your pre-existing window object to use\n        for this data viewer.\n    \"\"\"\n    dv = DataViewer(tk.Tk())\n    dv.show()\n    return dv\n\n\nclass DataViewer(object):\n\n    SHOW_ALL = 'show_all'\n    SHOW_SELECTED = 'show_selected'\n\n    def __init__(self, parent=None):\n        \"\"\"Data viewer constructor.\n\n        Args:\n          parent (Tk toplevel window, optional): Using this parameter you can\n            optionally specify window object to use for this data viewer.\n        \"\"\"\n        logging.debug('constructing data viewer')\n        self.window = parent\n        self._varShowMode = tk.StringVar(value=self.SHOW_ALL)\n        self._varObjects = tk.StringVar()\n        self._objects = {}\n\n    def show(self):\n        if self.window is None:\n            self.window = tk.Toplevel()\n        if hasattr(self, '_frame'):\n            return\n        self._init_ui()\n        self._connect_events()\n\n    def refresh(self):\n        self._mplFigure.canvas.draw()\n\n    def add_object(self, obj, name):\n        \"\"\"Adds new object to this data viewer.\"\"\"\n        self._lstObjects_additem(name)\n        self._objects[name] = obj\n\n    def remove_object(self, name):\n        \"\"\"Removes object by name.\"\"\"\n        del self._objects[name]\n        self._lstObjects_removeobj(name)\n\n    def get_object(self, name):\n        \"\"\"Gets object by name if such exists, otherwise returns None.\"\"\"\n        return self._objects.get(name)\n\n    def _init_ui(self):\n        self._init_menu()\n        self._frame = ttk.Frame(self.window)\n        self._panes = ttk.Panedwindow(self._frame, orient=tk.HORIZONTAL)\n        self._leftPaneFrame = ttk.Frame(self._panes, width=100)\n        self._rightPaneFrame = ttk.Frame(self._panes, width=300)\n        self._panes.add(self._leftPaneFrame)\n        self._panes.add(self._rightPaneFrame)\n\n        self._lblShowData = ttk.Label(self._leftPaneFrame,\n                                      text='Data objects:')\n        self._lstObjects = tk.Listbox(self._leftPaneFrame,\n                                      listvariable=self._varObjects,\n                                      selectmode=tk.EXTENDED)\n        self._scrFiles = ttk.Scrollbar(self._leftPaneFrame, orient=tk.VERTICAL,\n                                       command=self._lstObjects.yview)\n        self._lstObjects['yscrollcommand'] = self._scrFiles.set\n        self._lblDataView = ttk.Label(self._rightPaneFrame, text='Data View:')\n\n        self._frmPlot = ttk.Frame(self._rightPaneFrame)\n        self._mplFigure = Figure(figsize=(5, 4), dpi=100)\n        self._pltCanvas = FigureCanvasTkAgg(self._mplFigure,\n                                            master=self._frmPlot)\n        self._pltCanvas.show()\n        self._pltCanvas.get_tk_widget().pack(\n            side=tk.TOP, fill=tk.BOTH, expand=1)\n        self._mplToolbar = NavigationToolbar2TkAgg(self._pltCanvas,\n                                                   self._frmPlot)\n        self._mplToolbar.update()\n        self._pltCanvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=1)\n\n        self._leftPaneFrame.columnconfigure(0, weight=33)\n        self._leftPaneFrame.columnconfigure(1, weight=33)\n        self._leftPaneFrame.columnconfigure(2, weight=33)\n        self._leftPaneFrame.rowconfigure(1, weight=99)\n        self._lblShowData.grid(\n            column=0, row=0, padx=5, pady=10, sticky=(tk.N, tk.W, tk.S))\n        self._lstObjects.grid(column=0, columnspan=3, row=1, padx=5,\n                              pady=5, sticky=(tk.N, tk.W, tk.E, tk.S))\n        self._scrFiles.grid(column=50, row=1, pady=5, sticky=(tk.N, tk.S))\n\n        self._rightPaneFrame.rowconfigure(1, weight=99)\n        self._rightPaneFrame.columnconfigure(0, weight=99)\n        self._lblDataView.grid(column=0, row=0, padx=5, pady=10,\n                               sticky=(tk.W, tk.E))\n        self._frmPlot.grid(column=0, row=1, sticky=(tk.N, tk.W, tk.S, tk.E))\n\n        self._frame.columnconfigure(0, weight=100)\n        self._frame.rowconfigure(0, weight=99)\n        self._panes.grid(column=0, row=0, sticky=(tk.N, tk.W, tk.E, tk.S))\n        self._szGrip = ttk.Sizegrip(self._frame).grid(\n            column=0, row=999, sticky=(tk.W, tk.E))\n        self._frame.pack(side=tk.TOP, fill=tk.BOTH, expand=1)\n\n    def _connect_events(self):\n        self._varShowMode.trace('w', self._varShowMode_onchange)\n        self._lstObjects.bind('<<ListboxSelect>>', self._lstObjects_onselect)\n\n    def _init_menu(self):\n        self._menubar = tk.Menu(self.window)\n        self.window['menu'] = self._menubar\n        self._mnuFile = tk.Menu(self._menubar)\n        self._menubar.add_cascade(menu=self._mnuFile, label='File')\n        self._mnuFile.add_command(label='Exit', command=self._exit)\n\n        self._mnuSamplePlots = tk.Menu(self._menubar)\n        self._menubar.add_cascade(menu=self._mnuSamplePlots,\n                                  label='Sample Plots')\n        self._mnuSamplePlots.add_command(label='Sine',\n                                         command=self._draw_sample_sine)\n\n    def _varShowMode_onchange(self, name, index, mode):\n        logging.debug('mode selected: {}'.format(self._varShowMode.get()))\n\n    def _lstObjects_onselect(self, evt):\n        logging.debug('objects selection change: {}'.\n                      format(self._lstObjects.curselection()))\n        self._mplFigure.clear()\n        for idx in self._lstObjects.curselection():\n            objname = self._lstObjects.get(idx)\n            self._draw_object(self._objects[objname], objname)\n\n    def _lstObjects_additem(self, itemname):\n        self._lstObjects.insert(tk.END, itemname)\n        for i in range(0, self._lstObjects.size(), 2):\n            self._lstObjects.itemconfigure(i, background='#f0f0ff')\n\n    def _lstObjects_removeobj(self, itemname):\n        for idx, el in enumerate(self._lstObjects.get(0, tk.END)):\n            if el == itemname:\n                self._lstObjects.delete(idx)\n                break\n\n    def _draw_object(self, obj, name):\n        ax = self._mplFigure.add_subplot('111')\n        self._draw_pd_dataframe(obj, name, ax)\n        ax.legend(loc=4, prop={'size': 8})\n        ax.grid(b=True)\n        self.refresh()\n\n    def _draw_pd_dataframe(self, df, name, axes):\n        logging.debug('drawing dataset: \"{}\"'.format(name))\n        if len(df) == 0:\n            logging.warn('attemted to draw an empty dataset: \"{}\"'\n                         .format(name))\n            return\n        if len(df) < 100:\n            drawstyle = {'linestyle': 'None', 'marker': 'o',\n                         'markersize': 6}\n        else:\n            drawstyle = {}\n        for c in df.columns:\n            if isinstance(df[c][df.index[0]], complex):\n                axes.plot(df.index, np.real(df[c]),\n                          label=name + \" - Re(\" + str(c) + \")\", **drawstyle)\n                axes.plot(df.index, np.imag(df[c]),\n                          label=name + \" - Im(\" + str(c) + \")\", **drawstyle)\n            else:\n                axes.plot(df.index, df[c], label=name + \" - \" + str(c),\n                          **drawstyle)\n\n    def _draw_sample_sine(self):\n        args = np.arange(1000.)\n        df = pd.DataFrame(np.sin(np.radians(args)), index=args, columns=['y'])\n        self.add_object(df, 'sin(x)')\n\n    def _exit(self):\n        self.window.destroy()\n\n\nif __name__ == '__main__':\n    common.configure_logging()\n    root = tk.Tk()\n    data_viewer(root)\n    root.mainloop()\n","repo_name":"serjinio/python_tutors","sub_path":"nmr_analysis/nmran/datavis.py","file_name":"datavis.py","file_ext":"py","file_size_in_byte":8016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1882012051","text":"# -*- coding: utf-8 -*-\n# @Author  : hakusai\n# @Time    : 2022/10/16 19:41\nfrom collections import defaultdict, deque\nfrom typing import List\n\n\nclass Solution:\n    def minJumps(arr: List[int]) -> int:\n        n = len(arr)\n        d = defaultdict(list)\n        for i, x in enumerate(arr):\n            d[x].append(i)\n\n        q = deque([[0, 0]])\n        vis = {0, }\n        while q:\n            i, step = q.popleft()\n            if i == n - 1: return step\n            v = d[arr[i]]\n            v.append(i + 1)\n            if i: v.append(i - 1)\n            while v:\n                j = v.pop()\n                if j in vis: continue\n                q.append([j, step + 1])\n                vis.add(j)\n            # d.pop(arr[i])\n\n        # return -1\n\n\nif __name__ == '__main__':\n    arr = [100, -23, -23, 404, 100, 23, 23, 23, 3, 404]\n    Solution.minJumps(arr)\n","repo_name":"hakusai22/Go_Python_Study","sub_path":"Goland_Python3_Algorithm/Data_Structure/Graph/BFS/1345. 跳跃游戏 IV.py","file_name":"1345. 跳跃游戏 IV.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40720447248","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 getAllElements(self, root1: TreeNode, root2: TreeNode) -> List[int]:\n        res = []\n        stack1, stack2, p1, p2 = [], [], root1, root2\n        while (p1 or stack1) or (p2 or stack2):\n            while p1:\n                stack1.append(p1)\n                p1 = p1.left\n            while p2:\n                stack2.append(p2)\n                p2 = p2.left\n            if not stack1 and not stack2:\n                break\n            if not stack1 and stack2:\n                res.append(stack2[-1].val)\n                p2 = stack2.pop().right\n                continue\n            if stack1 and not stack2:\n                res.append(stack1[-1].val)\n                p1 = stack1.pop().right\n                continue\n                \n            if stack1[-1].val < stack2[-1].val:\n                res.append(stack1[-1].val)\n                p1 = stack1.pop().right\n            elif stack1[-1].val > stack2[-1].val:\n                res.append(stack2[-1].val)\n                p2 = stack2.pop().right\n            else:\n                res.append(stack1[-1].val)\n                res.append(stack2[-1].val)\n                p1 = stack1.pop().right\n                p2 = stack2.pop().right\n        return res\n","repo_name":"wondershow/CodingTraining","sub_path":"Python/LC1305_GetAllElementsInTwoBinarySearchTrees.py","file_name":"LC1305_GetAllElementsInTwoBinarySearchTrees.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4406248873","text":"from app.clubs import bp\nfrom flask import render_template, flash, redirect, url_for, request\nfrom urllib.parse import urlparse\nfrom flask_login import login_required\nfrom flask_login import current_user, login_user, logout_user\nfrom app.models import User, Club, ClubMembership\nfrom app import db\nfrom .forms import ClubForm\n\n@bp.route('/create_club', methods=['GET', 'POST'])\n@login_required\ndef create_club():\n    form = ClubForm()\n    if form.validate_on_submit():\n        club = Club(name=form.name.data, owner_id=current_user.id)\n        db.session.add(club)\n        db.session.commit()\n        flash('Club created successfully.', 'success')\n        return redirect(url_for('main.index'))\n    return render_template('clubs/club_create.html', form=form)\n\n@bp.route('/join_club/<int:club_id>', methods=['POST'])\n@login_required\ndef join_club(club_id):\n    club = Club.query.get(club_id)\n    if club:\n        if current_user not in club.members:\n            club.members.append(current_user)\n            db.session.commit()\n            flash('You have joined the club.', 'success')\n        else:\n            flash('You are already a member of this club.', 'warning')\n    return redirect(url_for('main.index'))\n\n@bp.route('/club_list', methods=['GET','POST'])\n@login_required\ndef club_list():\n    clubs = Club.query.all()\n    return render_template('clubs/club_list.html', clubs=clubs)\n\n@bp.route('/club_index/<int:club_id>', methods=['GET','POST'])\n@login_required\ndef club_index(club_id):\n    club = Club.query.get(club_id)\n    return render_template('clubs/club_index.html', club=club)","repo_name":"b-stead/strava_api","sub_path":"app/clubs/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71694819770","text":"def factorial(n):\r\n    if n == 0:\r\n        return 1\r\n    else:\r\n        return n * factorial(n-1)\r\n\r\n# Example usage\r\nnum = 5\r\nresult = factorial(num)\r\nprint(result) # Output: 120\r\n\r\n# Factorial Number Diferent-Way\r\ndef factorial(n):\r\n    fact = 1\r\n    for i in range(1, n+1):\r\n        fact *= i\r\n    return fact\r\n\r\n# Example usage\r\nnum = 5\r\nresult = factorial(num)\r\nprint(result) # Output: 120\r\n","repo_name":"anmamun0/Factorial_Number_pythoncode","sub_path":"Factorial_Number.py","file_name":"Factorial_Number.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39485121312","text":"# pandas 数据源下载地址：https://video.mugglecode.com/data_pd.zip，下载压缩包后解压即可（数据源与上节课相同）\n# -*- coding: utf-8 -*-\n\n\"\"\"\n    明确任务：\n        统计不同手机操作系统的每月流量使用情况\n\"\"\"\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# 用户及其使用的手机数据文件\nemployee_edu_datafile_path = './data_employee/employee_edu.csv'\n\n# 用户及其套餐使用情况的数据文件\nemployee_info_datafile_path = './data_employee/employee_info.csv'\n\n\n# 结果保存路径\noutput_path = './output'\nif not os.path.exists(output_path):\n    os.makedirs(output_path)\n\n\ndef collect_data():\n    \"\"\"\n        数据获取\n    \"\"\"\n    employee_edu_df = pd.read_csv(employee_edu_datafile_path)\n    employee_info_df = pd.read_csv(employee_info_datafile_path)\n    return employee_edu_df, employee_info_df\n\n\ndef process_data(employee_edu_df, employee_info_df):\n    \"\"\"\n        数据处理\n    \"\"\"\n\n    # 合并数据集\n    merged_df = pd.merge(employee_info_df, employee_edu_df, how='inner', on='EmployeeNumber')\n\n    return merged_df\n\n\ndef analyze_data(merged_df):\n    \"\"\"\n        数据分析\n    \"\"\"\n    #均值\n\n    return merged_df.groupby('EducationField')['MonthlyIncome'].mean()\n\n\ndef save_plot_results(system_usage_ser):\n    \"\"\"\n        结果展示\n    \"\"\"\n\n\n    system_usage_ser.plot(kind='bar',rot=45)\n    #sns.boxplot(x= 'system',y='monthly_mb', data=system_usage_ser)\n    plt.ylabel('Monthly Usage (MB)')\n    plt.tight_layout()\n\n    plt.show()\n\n\ndef main():\n    \"\"\"\n        主函数\n    \"\"\"\n    # 数据收集\n    user_device_df, user_usage_df = collect_data()\n\n    # 数据处理\n    merged_df = process_data(user_device_df, user_usage_df)\n\n    # 数据分析\n    system_usage_ser = analyze_data(merged_df)\n\n    # 结果展示\n    save_plot_results(system_usage_ser)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"baiyujia/python","sub_path":"data_analyze_11.py","file_name":"data_analyze_11.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5159962726","text":"from http.server import BaseHTTPRequestHandler, HTTPServer\nimport time\nimport json\nimport cgi\nimport random\n\n\n\"\"\"\nrestaurant API with http.server and json built-in python packages\n by Ecren Nur Esen\n\"\"\"\n# Veriyi önden tanımladım çünkü her request başına dosya açmasını istemedim\nalldata = json.loads(open(\"data.json\", \"r\").read())\n\ncategorized = json.loads(open(\"predataveg.json\", \"r\").read())\n\n\n\n\ndef dictionarize(inputstring):\n    output = {}\n    allvalues = inputstring.split(\"&\")\n    for a in allvalues:\n        splitter=a.split(\"=\")\n        name = splitter[0]\n        value = splitter[1]\n        output[name]=value\n    return output\n    \n\ndef getingredients(mealjson):\n    output = []\n    try:\n        mealjson[\"ingredients\"][0]\n\n       \n    except:\n        return []\n\n    for ing in mealjson[\"ingredients\"]:\n\n        for i in alldata[\"ingredients\"]:\n            if ing[\"name\"] == i[\"name\"]:\n\n                output.append(i)\n                break\n\n    return output\n\n\n\ndef get_random_meal_by_budget(budget:int):\n    meals = alldata[\"meals\"]\n    affordable_meals = []\n    for meal in meals:\n        total_price = 0\n        ingredients = []\n        for ingredient in meal[\"ingredients\"]:\n            ingredient_name = ingredient[\"name\"]\n            ingredient_quantity = ingredient[\"quantity\"]\n            quantity_type = ingredient[\"quantity_type\"]\n            isAffordable = True\n            try:\n                ingredient_options = [i for i in alldata[\"ingredients\"] if i[\"name\"] == ingredient_name][0][\"options\"]\n                affordable_options = [i for i in ingredient_options if i[\"price\"]* ingredient_quantity * 0.001 <= budget]\n                \n                if len(affordable_options)>0:\n                    option = random.choice(affordable_options)\n                    ingredients.append(option)\n                    optionPrice = option[\"price\"] * ingredient_quantity * 0.001\n                    total_price += optionPrice\n                else:\n                    isAffordable = False\n            except:\n                print(\"Ingredient does not found: \",ingredient_name)\n\n        if isAffordable and total_price <= budget:\n            meal[\"affordable_options\"] = ingredients\n            affordable_meals.append(meal)\n    if len(affordable_meals)>0:\n        return random.choice(affordable_meals)\n    else:\n        return None\n\nclass App(BaseHTTPRequestHandler):\n    \n    \n    def do_POST(self):\n\n        self.send_response(301)\n        self.send_header('Content-Type', 'application/json')\n        self.end_headers()\n\n        #NOTE KALITE KONTROL\n        if \"/quality\" in self.path:\n            arguments = dictionarize(self.rfile.read(int(self.headers['Content-Length'])).decode())\n            try:\n                meal_id = arguments[\"meal_id\"]\n            except:\n                meal_id= None\n            try:\n                currentmeal = alldata[\"meals\"][int(meal_id.strip())-1]\n            except:\n                currentmeal = None\n\n            if meal_id==None or currentmeal == None:\n\n\n                self.wfile.write(bytes(json.dumps({\"error\":\"Try to give a valid ID for a meal\"},indent=4,ensure_ascii=False),\"utf-8\"))\n            elif len(list(arguments.keys())) == 1 and meal_id!=None:\n                self.wfile.write(bytes(json.dumps({\"quality\":30},indent=4,ensure_ascii=False),\"utf-8\"))\n\n            totalofit = 0\n            lengthofingredients = 0\n            theingredients_given=[]\n            if len(list(arguments.keys())) >1 and meal_id!=None:\n                for l in list(arguments.keys()):\n                    \n                    if l == \"meal_id\":\n                        continue\n                    else:\n\n                        theingredients_given.append(l)\n\n                \n                for i in currentmeal[\"ingredients\"]:\n                    equal = 0\n                    for the in theingredients_given:\n                        \n                        if the.strip().lower() in i[\"name\"].strip().lower():\n\n                            equal = 1\n                            value = arguments[the]\n\n                            if value == \"low\":\n                                totalofit +=10\n                            if value == \"high\":\n                                totalofit+=30\n                            if value == \"medium\":\n                                totalofit += 20\n                             \n                            break\n                    if equal == 0:\n                        totalofit += 30\n                        lengthofingredients += 1\n\n                output = totalofit/len(currentmeal[\"ingredients\"])\n                self.wfile.write(bytes(json.dumps({\"quality\":output},indent=4),\"utf-8\"))\n        \n        \n        elif \"/random\" in self.path:\n                arguments = dictionarize(self.rfile.read(int(self.headers['Content-Length'])).decode())\n                budget = int(arguments[\"budget\"])\n                meal = get_random_meal_by_budget(budget)\n                \n                if meal:\n                    \n                    self.wfile.write(bytes(json.dumps(meal,indent=4,ensure_ascii=False),\"utf-8\"))\n                else:\n                    self.wfile.write(bytes(json.dumps({\"error\":\"There is no meal that fits your budget\"},indent=4,ensure_ascii=False),\"utf-8\"))\n                   \n\n        \n\n        \n        \n        #NOTE FIYAT KONTROL\n        elif \"/price\" in self.path:\n\n            arguments = dictionarize(self.rfile.read(int(self.headers['Content-Length'])).decode())\n            try:\n                meal_id = int(arguments[\"meal_id\"])\n            except:\n                meal_id = None\n            try:\n                currentmeal = alldata[\"meals\"][meal_id-1]\n            except:\n                currentmeal = None\n\n            if currentmeal ==None or meal_id == None:\n                self.wfile.write(bytes(json.dumps({\"error\":\"Couldn't find meal\"},indent=4,ensure_ascii=False),\"utf-8\"))\n            else:\n\n                if len(list(arguments.keys())) == 1:\n                    counter = 0\n                    for ing in currentmeal[\"ingredients\"]:\n                        for i in alldata[\"ingredients\"]:\n                            if ing[\"name\"] == i[\"name\"]:\n                                parameter = i[\"options\"][0][\"price\"]\n                                quantity = ing[\"quantity\"]\n                                output = (quantity/1000)*parameter\n                                counter += output\n                                break \n                    self.wfile.write(bytes(json.dumps({\"price\":counter},indent=4,ensure_ascii=False),\"utf-8\"))\n                if len(list(arguments.keys()))>1:\n                    counter = 0\n                    temproraryarguments = []\n                    for a in arguments:\n                        if a == \"meal_id\":\n                            continue\n                        else: \n                            if arguments[a] == \"low\" or arguments[a] == \"high\" or arguments[a] == \"medium\":\n                                temproraryarguments.append({f\"{a}\":f\"{arguments[a]}\"})\n\n\n                    for ing in currentmeal[\"ingredients\"]:\n                        for i in alldata[\"ingredients\"]:\n                            if ing[\"name\"] == i[\"name\"]:\n                                \n                                default = 0\n                                addition =0 \n                                for arg in temproraryarguments:\n\n                                    argstring = str(list(arg.keys())[0])\n                                    if argstring.lower().strip() in ing[\"name\"].lower().strip():\n                                        if arg[argstring] == \"low\":\n                                            addition+=0.1\n                                            default=2\n                                        if arg[argstring] == \"medium\":\n                                            default=1\n                                            addition += 0.05\n                                        break\n                                \n\n\n                                     \n                                parameter = i[\"options\"][default][\"price\"]\n                                quantity = ing[\"quantity\"]\n                                output = (quantity/1000)*parameter\n                                counter += output\n                                counter += addition\n                                break \n                    self.wfile.write(bytes(json.dumps({\"price\":round(counter,2)},indent=4,ensure_ascii=False),\"utf-8\"))\n        else:\n            self.wfile.write(\n                bytes(\n                    json.dumps(\n                        {\"Author\": \"Ecren Esen\", \"Description\": \"Page \"},\n                        indent=4,\n                    ),\n                    \"utf-8\",\n                )\n            )\n    \n\n                \n\n    def do_GET(self):\n\n        self.send_response(200)\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        arguments = {}\n        try:\n            pathsplitter = self.path.split(\"?\")[1]\n            try:\n                allarguments = pathsplitter.split(\"&\")\n                for a in allarguments:\n                    pathsplitter = a\n                    arguments[pathsplitter.split(\"=\")[0]] = pathsplitter.split(\"=\")[1]\n\n            except:\n                arguments[pathsplitter.split(\"=\")[0]] = pathsplitter.split(\"=\")[1]\n        except:\n            pass\n        #print(arguments)\n\n        if self.path == \"/\":\n            self.wfile.write(\n                bytes(\n                    json.dumps(\n                        {\n                            \"Author\": \"Ecren Esen\",\n                            \"Description\": \"Hello this is the API I wrote for Transparent Restaurant\",\n                        },\n                        indent=4,\n                    ),\n                    \"utf-8\",\n                )\n            )\n        \n        # MEAL listeleyici argümanlar çalışıyor\n        elif \"/listMeals\" in self.path:\n            try:\n                is_vegetarian = arguments[\"is_vegetarian\"]\n            except:\n                is_vegetarian = False\n            try:\n                is_vegan = arguments[\"is_vegan\"]\n            except:\n\n                is_vegan = False\n\n            if is_vegan == \"false\":\n                is_vegan = False\n            if is_vegetarian == \"false\":\n                is_vegetarian = False\n            if is_vegetarian == \"true\":\n                is_vegetarian = True\n            if is_vegan == \"true\":\n                is_vegan = True\n\n            if is_vegan == True and is_vegetarian == False:\n\n                self.wfile.write(\n                    bytes(\n                        json.dumps(categorized[\"vegan\"], indent=4, ensure_ascii=False),\n                        \"utf-8\",\n                    )\n                )\n            elif is_vegetarian == True and is_vegan == False:\n                self.wfile.write(\n                    bytes(\n                        json.dumps(\n                            categorized[\"vegetarian\"], indent=4, ensure_ascii=False\n                        ),\n                        \"utf-8\",\n                    )\n                )\n            elif is_vegetarian == True and is_vegan == True:\n                self.wfile.write(\n                    bytes(\n                        json.dumps(categorized[\"both\"], indent=4, ensure_ascii=False),\n                        \"utf-8\",\n                    )\n                )\n            else:\n                self.wfile.write(\n                    bytes(\n                        json.dumps(alldata[\"meals\"], indent=4, ensure_ascii=False),\n                        \"utf-8\",\n                    )\n                )\n\n        # id ile spesifik yiyecek alma\n        elif \"/getMeal\" in self.path:\n            try:\n                meal_id = int(arguments[\"id\"].strip())\n                current_data = None\n                try:\n                    current_data = alldata[\"meals\"][meal_id-1]\n                except:\n                    pass\n\n\n                if current_data == None:\n                    self.wfile.write(\n                        bytes(\n                            json.dumps(\n                                {\"error\": \"The meal is not in the list\"},\n                                indent=4,\n                                ensure_ascii=False,\n                            ),\n                            \"utf-8\",\n                        )\n                    )\n                else:\n                    self.wfile.write(\n                        bytes(json.dumps(current_data, indent=4, ensure_ascii=False), \"utf-8\")\n                    )\n\n            except Exception as e:\n\n                self.wfile.write(\n                    bytes(\n                        json.dumps(\n                            {\"error\": \"You didn't specified ID of the meal\"},\n                            indent=4,\n                            ensure_ascii=False,\n                        ),\n                        \"utf-8\",\n                    )\n                )\n\n        #search with GET\n        elif \"/search\" in self.path:\n            try:\n                query = arguments[\"query\"]\n                results = []\n                for m in alldata[\"meals\"]:\n                    if query.lower().strip() in m[\"name\"].lower():\n                        results.append(m)\n                output = {\"output\": results}\n                self.wfile.write(\n                    bytes(json.dumps(output, indent=4, ensure_ascii=False), \"utf-8\")\n                )\n            except:\n\n                self.wfile.write(\n                    bytes(\n                        json.dumps(\n                            {\"error\": \"We couldn't find what you are looking for.\"},\n                            indent=4,\n                            ensure_ascii=False,\n                        ),\n                        \"utf-8\",\n                    )\n                )\n\n\n\n\n\n        else:\n            self.wfile.write(\n                bytes(\n                    json.dumps(\n                        {\"Author\": \"Ecren Nur Esen\", \"Description\": \"None\"},\n                        indent=4,\n                    ),\n                    \"utf-8\",\n                )\n            )\n\n\nif __name__ == \"__main__\":\n    server = HTTPServer((\"0.0.0.0\", 8080), App)\n\n    server.serve_forever()\n    server.server_close()\n","repo_name":"ecrenesen/transparent-restaurant-api","sub_path":"ecren-esen-API/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":14375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"397185915","text":"#This file will be used for developmental purposes. Ultimately, the purpose of this file is to act as the main.\n\nimport os\nimport cv2\nfrom utils import clean, process_image, get_containers, is_player_alive, screen_capture, get_round_map_status\nfrom profile import Player\nimport pytesseract\nimport numpy as np\nfrom pytesseract import image_to_string\n## TODO: USER: Change this pathing to be universal among users. It might be helpful to look at tesseract-OCR's installation manual and how they suggest setting the path.\npytesseract.pytesseract.tesseract_cmd = 'C:{}\\\\AppData\\\\Local\\\\Tesseract-OCR\\\\tesseract.exe'.format(os.environ['HOMEPATH'])\n\n## TODO: USER: Make some sort of GUI for the player to interact with where they can login and store their own profiles. For now we will just import a test player object to work with.\n#For now we will incorporate a preliminary UI that will run in the terminal. This will be built in the Player class in profile.py. This will allow us to easily remove the UI later.\nplayer = Player()\n\n#Output current data for player for testing\n#print(player.player_data[map_string].nodes(data=True))\n#print(player.player_data[map_string].edges(data=True))\n#player.visualize_data(map_string)\n\n#Define some initial variables\n#location, callout1, callout2, playerbox will contain coordinates needed to grab specific data from each screen\nlocation, callout1, callout2, playerbox = get_containers(player.resolution, player.aspect_ratio)\n#Path traveled will contain the path for any given round. This will be reinitialized every round.\npath_traveled = []\nmap_string, affiliation = None, None\n\nwhile True:\n    #First we must get the frame from the game\n    image = np.array(screen_capture(player.resolution))\n\n    #Then we must find the indicators that tell us whether the player is alive. This information comes from an area we call the playerbox. The playerbox is the icon for the player found on the HUD. In this case we want to see if there is a white box surrounding this player icon. This tells us whether the player is still alive.\n    crop_image_playerbox = image[playerbox[1]:playerbox[1]+playerbox[3], playerbox[0]:playerbox[0]+playerbox[2]]\n    playerbox_indicators = [\n        crop_image_playerbox[(crop_image_playerbox.shape[0]-1)][(crop_image_playerbox.shape[1]-1)],\n        crop_image_playerbox[(crop_image_playerbox.shape[0]-1)][0],\n        crop_image_playerbox[0][(crop_image_playerbox.shape[1]-1)],\n        crop_image_playerbox[0][0]\n    ]\n\n    #Then for every frame of playerbox we get we must check if the player is alive\n    player_alive = is_player_alive(playerbox_indicators)\n    if player_alive:\n        #If the player is alive then we can further process the frame. Before we can process the frame we will convert the image to grayscale. The processing of the frame will include defining three different boxes where text for the callouts will be located. We call these boxes location, callout1, and callout2. Once these boxes are grabbed, the image will be processed so that every pixel is either white (255) or black (0). This will make it easier for the OCR to classify text properly.\n        #Convert image to grayscale\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        #location is the top line of each callout. this information includes floor number or whether the player is in the exterior portion of the map\n        #callout1 is the location of the first line of a room or area\n        #callout2 includes the second line of the room or area\n        crop_image_location = image[location[1]:location[1]+location[3], location[0]:location[0]+location[2]]\n        crop_image_callout1 = image[callout1[1]:callout1[1]+callout1[3], callout1[0]:callout1[0]+callout1[2]]\n        crop_image_callout2 = image[callout2[1]:callout2[1]+callout2[3], callout2[0]:callout2[0]+callout2[2]]\n\n        #cv2.imshow('location', crop_image_location)\n        #cv2.waitKey(0)\n        #cv2.imshow('callout_1', crop_image_callout1)\n        #cv2.waitKey(0)\n        #cv2.imshow('callout_2', crop_image_callout2)\n        #cv2.waitKey(0)\n\n        #Define bound for processing\n        #Current method takes the max vlue of the pixel in the image and subtracts the mean of pixel values. this should theoretically make the text white and the background black but we end up swapping values in the function to make the text black and the background white\n        bound = np.max(image) - np.mean(image)\n        #Process subset frames\n        proc_image_location = process_image(crop_image_location.copy(), bound)\n        proc_image_callout1 = process_image(crop_image_callout1.copy(), bound)\n        proc_image_callout2 = process_image(crop_image_callout2.copy(), bound)\n\n        #Now that our images have been processed, we can throw those images into the OCR to acquire our text. We will than perform some sanitization and correct error that is within reason.\n        #store result of OCR processing for each subset frame\n        text_location = (image_to_string(proc_image_location, lang='eng')).upper()\n        #1F is frequently read as TF by the OCR so we are going to preemptively alter this so that callouts like 1FHALLWAY and 2FHALLWAY do not get confused\n        #Likewise, 3F is often read as BF which the program sometimes defaults to 1F due to the levenshtein error check\n        if text_location == 'TF':\n            text_location = '1F'\n        elif text_location == 'BF':\n            text_location = '3F'\n        text_callout1 = (image_to_string(proc_image_callout1, lang='eng')).upper()\n        text_callout2 = (image_to_string(proc_image_callout2, lang='eng')).upper()\n        text_callout = text_callout1 + text_callout2\n        #If map and affiliation not known, then we need to try to determine map and affiliation for round\n        if (map_string, affiliation) == (None, None):\n            map_string, affiliation, first_match = get_round_map_status(text_location + text_callout)\n            #This takes caore of the first instance for adding to path path_traveled\n            if first_match != None:\n                path_traveled.append(first_match)\n                print(path_traveled)\n        #If map and affiliation are known then we can build paths\n        if path_traveled:\n            text = clean(text_location + text_callout, map_string)\n            #Now that we have our text, we will check if a valid callout is constructed. If it is, we will check if a player has moved from their current position. If so, the callout will be added to a list that represents the path traveled.\n            #Start storing location changes into a list\n            current_pos = text\n            #This takes care of every other relevant instance of changes in location\n            if current_pos != None and path_traveled[-1] != current_pos:\n                path_traveled.append(current_pos)\n                print(path_traveled)\n            ## TODO: ACCURACY: Make a better error handling system for if the program accidently skips over a room.\n\n    #This next part will determine what to do when the player is deemed to not be alive.\n    else:\n        #If player is dead, we need to store path traveled data if data exists\n        if len(path_traveled) > 0:\n            #Perform updates to player_data\n            player.update_data(path_traveled, map_string, affiliation)\n            ## TODO: SCALABILITY: Currently this method is loading and updating the player's save file every round. This is inefficient, but will work for testing. Later on a method should be devised to update on a per game basis.\n            player.store_data()\n            print(\"Data saved to {} : {}\".format(map_string, affiliation))\n        #If the player is dead, we need to reset the path traveled variable\n        path_traveled = []\n        map_string, affiliation = None, None\n","repo_name":"Sean-V/R6LocationTracker","sub_path":"Project/code/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":7800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29410651719","text":"from math import pi\ndef area_triangulo(base,altura):\n    area = base*altura/2\n    return area\n\ndef area_rectangulo(base,altura):\n    area = base*altura\n    return area\n\ndef area_rombo(diagonal1,diagonal2):\n    area = diagonal1*diagonal2/2\n    return area\n\ndef area_circulo(radio):\n    area = pi*(radio**2)\n    return area\n\nif __name__ == \"__main__\":\n    print(\"0: Área triángulo\")\n    print(\"1: Área Rectángulo\")\n    print(\"2: Área Rombo\")\n    print(\"3: Área Círculo\")\n    op = int(input(\"Seleccione una operación:\"))\n    if op == 0:\n        base = float(input(\"Ingrse la base:\"))\n        altura = float(input(\"Ingrse la altura:\"))\n        area = area_triangulo(base,altura)\n    elif op == 1:\n        base = float(input(\"Ingrse la base:\"))\n        altura = float(input(\"Ingrse la altura:\"))\n        area = area_rectangulo(base,altura)\n    elif op == 2:\n        diagonal1 = float(input(\"Ingrse una diagonal:\"))\n        diagonal2 = float(input(\"Ingrse la otra diagonal:\"))\n        area = area_rombo(diagonal1,diagonal2)\n    elif op == 3:\n        radio = float(input(\"Ingrse el radio:\"))\n        area = area_circulo(radio)","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej1/tema2_ej1_118b3110d5b1b578d91bf348603d549d.py","file_name":"tema2_ej1_118b3110d5b1b578d91bf348603d549d.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24626048249","text":"###############################\n# Author: Ben Swinford\n# Class: CS 362\n# Assignment: #7\n# Program: leapyear.py\n###############################\n\n\ndef main():\n\tresult = 2\n\tyear = input(\"Please enter a year: \")\n\tyear = int(year)\n\tfourYears = year % 4\n\thunYears = year % 100\n\tfohunYears = year % 400\n\tif fourYears == 0 and hunYears != 0 or fohunYears == 0:\n\t\tresult = 1\n\telse:\n\t\tresult = 0\n\treturn result\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"swinforb/Assignment7","sub_path":"leapyear.py","file_name":"leapyear.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19491459590","text":"from enum import Enum\nimport time\n\nfrom action_msgs.msg import GoalStatus\nfrom geometry_msgs.msg import Pose, PoseStamped\nfrom geometry_msgs.msg import PoseWithCovarianceStamped\nfrom lifecycle_msgs.srv import GetState\nfrom nav2_msgs.action import NavigateToPose\n\nimport rclpy\nfrom rclpy.duration import Duration\nfrom rclpy.action import ActionClient\nfrom rclpy.node import Node\nfrom rclpy.qos import QoSDurabilityPolicy, QoSHistoryPolicy, QoSReliabilityPolicy\nfrom rclpy.qos import QoSProfile\n\nclass TaskResult(Enum):\n    UNKNOWN = 0\n    SUCCEEDED = 1\n    CANCELED = 2\n    FAILED = 3\n\n\nclass BasicNavigator(Node):\n    def __init__(self):\n        super().__init__(node_name='basic_navigator')\n        self.initial_pose = Pose()\n        self.goal_handle = None\n        self.result_future = None\n        self.feedback = None\n        self.status = None\n\n        amcl_pose_qos = QoSProfile(\n          durability=QoSDurabilityPolicy.TRANSIENT_LOCAL,\n          reliability=QoSReliabilityPolicy.RELIABLE,\n          history=QoSHistoryPolicy.KEEP_LAST,\n          depth=1)\n\n        self.initial_pose_received = False\n        \n        self.nav_to_pose_client = ActionClient(self, NavigateToPose, 'navigate_to_pose')\n        self.model_pose_sub = self.create_subscription(PoseWithCovarianceStamped,\n                                                       'amcl_pose',\n                                                       self._amclPoseCallback,\n                                                       amcl_pose_qos)\n        self.initial_pose_pub = self.create_publisher(PoseWithCovarianceStamped,\n                                                      'initialpose',\n                                                      10)\n\n    def setInitialPose(self, initial_pose):\n        self.initial_pose_received = False\n        self.initial_pose = initial_pose\n        self._setInitialPose()\n\n\n    def goToPose(self, pose):\n        # Sends a `NavToPose` action request and waits for completion\n        self.debug(\"Waiting for 'NavigateToPose' action server\")\n        while not self.nav_to_pose_client.wait_for_server(timeout_sec=1.0):\n            self.info(\"'NavigateToPose' action server not available, waiting...\")\n\n        goal_msg = NavigateToPose.Goal()\n        goal_msg.pose = pose\n\n        self.info('Navigating to goal: ' + str(pose.pose.position.x) + ' ' +\n                      str(pose.pose.position.y) + '...')\n        send_goal_future = self.nav_to_pose_client.send_goal_async(goal_msg,\n                                                                   self._feedbackCallback)\n        rclpy.spin_until_future_complete(self, send_goal_future)\n        self.goal_handle = send_goal_future.result()\n\n        if not self.goal_handle.accepted:\n            self.error('Goal to ' + str(pose.pose.position.x) + ' ' +\n                           str(pose.pose.position.y) + ' was rejected!')\n            return False\n\n        self.result_future = self.goal_handle.get_result_async()\n        return True\n\n    def cancelNav(self):\n        self.info('Canceling current goal.')\n        if self.result_future:\n            future = self.goal_handle.cancel_goal_async()\n            rclpy.spin_until_future_complete(self, future)\n        return\n\n    def isNavComplete(self):\n        if not self.result_future:\n            # task was cancelled or completed\n            return True\n        rclpy.spin_until_future_complete(self, self.result_future, timeout_sec=0.10)\n        if self.result_future.result():\n            self.status = self.result_future.result().status\n            if self.status != GoalStatus.STATUS_SUCCEEDED:\n                self.info('Goal with failed with status code: {0}'.format(self.status))\n                return True\n        else:\n            # Timed out, still processing, not complete yet\n            return False\n\n        self.info('Goal succeeded!')\n        return True\n\n    def getFeedback(self):\n        return self.feedback\n\n    def getResult(self):\n        return self.status\n\n    def waitUntilNav2Active(self, node_name='amcl'):\n        self._waitForNodeToActivate(node_name)\n        self._waitForInitialPose()\n        self._waitForNodeToActivate('bt_navigator')\n        self.info('Nav2 is ready for use!')\n        return\n\n    def _waitForNodeToActivate(self, node_name):\n        # Waits for the node within the tester namespace to become active\n        self.debug('Waiting for ' + node_name + ' to become active..')\n        node_service = node_name + '/get_state'\n        state_client = self.create_client(GetState, node_service)\n        while not state_client.wait_for_service(timeout_sec=1.0):\n            self.info(node_service + ' service not available, waiting...')\n\n        req = GetState.Request()\n        state = 'unknown'\n        while (state != 'active'):\n            self.debug('Getting ' + node_name + ' state...')\n            future = state_client.call_async(req)\n            rclpy.spin_until_future_complete(self, future)\n            if future.result() is not None:\n                state = future.result().current_state.label\n                self.debug('Result of get_state: %s' % state)\n            time.sleep(2)\n        return\n\n    def _waitForInitialPose(self):\n        while not self.initial_pose_received:\n            self.info('Setting initial pose')\n            self._setInitialPose()\n            self.info('Waiting for amcl_pose to be received')\n            rclpy.spin_once(self, timeout_sec=1)\n        return\n\n    def _amclPoseCallback(self, msg):\n        self.initial_pose_received = True\n        return\n\n    def _feedbackCallback(self, msg):\n        self.feedback = msg.feedback\n        return\n\n    def _setInitialPose(self):\n        msg = PoseWithCovarianceStamped()\n        msg.pose.pose = self.initial_pose.pose\n        msg.header.frame_id = 'map'\n        msg.header.stamp = self.get_clock().now().to_msg()\n        self.info('Publishing Initial Pose')\n        self.initial_pose_pub.publish(msg)\n        return\n\n    def info(self, msg):\n        self.get_logger().info(msg)\n        return\n\n    def warn(self, msg):\n        self.get_logger().warn(msg)\n        return\n\n    def error(self, msg):\n        self.get_logger().error(msg)\n        return\n\n    def debug(self, msg):\n        self.get_logger().debug(msg)\n        return\n\n\ndef main():\n    rclpy.init()\n\n    navigator = BasicNavigator()\n\n    # Set our demo's initial pose\n    initial_pose = PoseStamped()\n    initial_pose.header.frame_id = 'map'\n    initial_pose.header.stamp = navigator.get_clock().now().to_msg()\n    initial_pose.pose.position.x = 3.45\n    initial_pose.pose.position.y = 2.15\n    initial_pose.pose.orientation.z = 1.0\n    initial_pose.pose.orientation.w = 0.0\n    navigator.setInitialPose(initial_pose)\n\n    # Activate navigation, if not autostarted. This should be called after setInitialPose()\n    # or this will initialize at the origin of the map and update the costmap with bogus readings.\n    # If autostart, you should `waitUntilNav2Active()` instead.\n    # navigator.lifecycleStartup()\n\n    # Wait for navigation to fully activate, since autostarting nav2\n    #navigator.waitUntilNav2Active()\n\n    # If desired, you can change or load the map as well\n    # navigator.changeMap('/path/to/map.yaml')\n\n    # You may use the navigator to clear or obtain costmaps\n    # navigator.clearAllCostmaps()  # also have clearLocalCostmap() and clearGlobalCostmap()\n    # global_costmap = navigator.getGlobalCostmap()\n    # local_costmap = navigator.getLocalCostmap()\n\n    # Go to our demos first goal pose\n    goal_pose = PoseStamped()\n    goal_pose.header.frame_id = 'map'\n    goal_pose.header.stamp = navigator.get_clock().now().to_msg()\n    goal_pose.pose.position.x = -2.0\n    goal_pose.pose.position.y = -0.5\n    goal_pose.pose.orientation.w = 1.0\n\n    # sanity check a valid path exists\n    # path = navigator.getPath(initial_pose, goal_pose)\n\n    navigator.goToPose(goal_pose)\n\n    i = 0\n    while not navigator.isTaskComplete():\n        ################################################\n        #\n        # Implement some code here for your application!\n        #\n        ################################################\n\n        # Do something with the feedback\n        i = i + 1\n        feedback = navigator.getFeedback()\n        if feedback and i % 5 == 0:\n            print('Estimated time of arrival: ' + '{0:.0f}'.format(\n                  Duration.from_msg(feedback.estimated_time_remaining).nanoseconds / 1e9)\n                  + ' seconds.')\n\n            # Some navigation timeout to demo cancellation\n            if Duration.from_msg(feedback.navigation_time) > Duration(seconds=600.0):\n                navigator.cancelTask()\n\n            # Some navigation request change to demo preemption\n            if Duration.from_msg(feedback.navigation_time) > Duration(seconds=18.0):\n                goal_pose.pose.position.x = -3.0\n                navigator.goToPose(goal_pose)\n\n    # Do something depending on the return code\n    result = navigator.getResult()\n    if result == TaskResult.SUCCEEDED:\n        print('Goal succeeded!')\n    elif result == TaskResult.CANCELED:\n        print('Goal was canceled!')\n    elif result == TaskResult.FAILED:\n        print('Goal failed!')\n    else:\n        print('Goal has an invalid return status!')\n\n    navigator.lifecycleShutdown()\n\n    exit(0)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"orjano-max/uia_husky_0776","sub_path":"husky_master/husky_master/husky_master.py","file_name":"husky_master.py","file_ext":"py","file_size_in_byte":9337,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"27254226549","text":"# -*- coding:utf-8 -*-\n\"\"\"\n从原始数据的文件中找出 bad_man 文件中所有数据对应的药物\n\"\"\"\nimport pandas as pd\nimport numpy as np\n\n\ndef find_bad_man_drug(origin_file_name, bad_man_file_name, out_file_name):\n    data = pd.read_csv(origin_file_name, header=None)\n    bad_man = pd.read_csv(bad_man_file_name, header=None)\n\n    man = list(data.iloc[:, 0])\n    drug = list(data.iloc[:, 1])\n    data = np.array(data).tolist()\n\n    no_dup_man = man.duplicated()\n    for i in range(10):\n        if bad_man[i][0] in no_dup_man:\n            bad_man = list(bad_man.iloc[:, 0])\n        elif bad_man[i][1] in no_dup_man:\n            bad_man = list(bad_man.iloc[:, 1])\n        else:\n            print(\"请检查输入文件是否正确！\\n，未能够在 %s 中找到 %s 的前十条数据\" % (origin_file_name, bad_man_file_name))\n\n    no_dup_drug = [x[1] for x in data if x[0] in bad_man].duplicated()\n\n    dic = dict(zip(list(range(len(no_dup_drug))), no_dup_drug))\n    out = pd.DataFrame.from_dict(dic, orient='index')\n    out.to_csv(out_file_name, header=False, index=False)\n\n\norigin_file = r\"C:\\filename.csv\"\nbad_man_file= r\"C:\\filename.csv\"\nout_file = r\"C:\\filename.csv\"\nfind_bad_man_drug(origin_file, bad_man_file, out_file)\n\n\n\n","repo_name":"IPopy/CLife2","sub_path":"Tools/find_bad_man_drug.py","file_name":"find_bad_man_drug.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75191393207","text":"import imgui\n\nfrom deeper.setting import StringSetting, StringSettingVType\nfrom .setting_widget import SettingWidget, SettingWidgetBuilder\n\n\nclass StringWidget(SettingWidget):\n    def __init__(self, setting):\n        super().__init__(setting)\n\n    def draw(self):\n        changed, value = imgui.input_text(\n            self.name,\n            self.value,\n            256\n        )\n        if changed:\n            self.value = value\n        \n\nclass StringWidgetBuilder(SettingWidgetBuilder):\n    #key = StringSetting\n    key = StringSettingVType\n    cls = StringWidget\n","repo_name":"kfields/deeper","sub_path":"deeper/widgets/setting/string_widget.py","file_name":"string_widget.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"8388640610","text":"import os\n\nimport numpy as np\nimport pandas as pd\n\nHOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n\n# fetch_housing_data()\n\ndef load_housing_data(housing_path=HOUSING_PATH):\n    csv_path = os.path.join(housing_path, \"housing.csv\")\n    return pd.read_csv(csv_path)\n\n\nhousing = load_housing_data(HOUSING_PATH)\n\n'''\nCreate stratified train-test set on median_income\n'''\n\nhousing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n                               labels=[1, 2, 3, 4, 5])\n\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\nfor train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n    strat_train_set = housing.loc[train_index]\n    strat_test_set = housing.loc[test_index]\n\n# print(strat_train_set[\"income_cat\"].value_counts() / len(strat_train_set))\n\n# Remove the income_cat attribute\n\nfor set_ in (strat_train_set, strat_test_set):\n    set_.drop(\"income_cat\", axis=1, inplace=True)\n\n'''\nInsight from the data\n'''\n\n# correlations\n\ncorr_matrix = housing.corr()\n# print(corr_matrix[\"median_house_value\"].sort_values(ascending=False))\ncorr_matrix[\"median_house_value\"].sort_values(ascending=False)\n\n'''\nPrepare the data\n'''\n\n# separate the labels from the data\n\nhousing = strat_train_set.drop(\"median_house_value\", axis=1)\nhousing_labels = strat_train_set[\"median_house_value\"].copy()\n\n'''\nData Cleaning\n'''\n\nfrom sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(strategy=\"median\")\nhousing_num = housing.drop(\"ocean_proximity\",\n                           axis=1)  # dropping the Categorical data\n\nimputer.fit(housing_num)\nX = imputer.transform(housing_num)  # Filling the missing values with median\n\n# X is numpy array, put it back into a pandas DataFrame\nhousing_tr = pd.DataFrame(X, columns=housing_num.columns,\n                          index=housing_num.index)\n\n# Transformation pipeline\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nnum_pipeline = Pipeline([\n    ('imputer', SimpleImputer(strategy=\"median\")),\n    ('std_scaler', StandardScaler()),\n])\n\nhousing_num_tr = num_pipeline.fit_transform(housing_num)\n\n# Single Transformer for all: ColumnTransformer\n\nfrom sklearn.compose import ColumnTransformer\n\nnum_attribs = list(housing_num)\ncat_attribs = [\"ocean_proximity\"]\n\nfull_pipeline = ColumnTransformer([\n        ('num', num_pipeline, num_attribs),\n        ('cat', OneHotEncoder(), cat_attribs),\n    ])\n\nhousing_prepared = full_pipeline.fit_transform(housing)\n\n'''\nSelect and Train a Model\n'''\n\ndef display_scores(scores):\n    print(\"Scores:\", scores)\n    print(\"Mean:\", scores.mean())\n    print(\"Standard Deviation:\", scores.std())\n\n# Select and Train a model\n\nprint(\"Linear Regression Model\")\n# 1. Linear Regression Model\n\n# Train the Model\nfrom sklearn.linear_model import LinearRegression\nlin_reg = LinearRegression()\nlin_reg.fit(housing_prepared, housing_labels)\n\n# Test the Model\nfrom sklearn.metrics import mean_squared_error\nhousing_predictions = lin_reg.predict(housing_prepared)\nlin_mse = mean_squared_error(housing_labels, housing_predictions)\nlin_rmse = np.sqrt(lin_mse)\nprint(\"RMSE\", lin_rmse)\n\n# Evaluation using Cross-Validation\nfrom sklearn.model_selection import cross_val_score\nscores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n                         scoring=\"neg_mean_squared_error\", cv=10)\n\nprint (\"LinearRegression: After cross validation\")\nlin_rmse_scores = np.sqrt(-scores)\ndisplay_scores(lin_rmse_scores)\n\n\n# 2. DecisionTree Regression Model\n\nprint(\"Decision Tree Model\")\n# Train the Model\nfrom sklearn.tree import DecisionTreeRegressor\ntree_reg = DecisionTreeRegressor()\ntree_reg.fit(housing_prepared, housing_labels)\n\n# Test the Model\nhousing_predictions = tree_reg.predict(housing_prepared)\ntree_mse = mean_squared_error(housing_labels, housing_predictions)\ntree_rmse = np.sqrt(tree_mse)\nprint(\"RMSE:\", tree_rmse)\n\nprint (\"LinearRegression: After cross validation\")\n# Evaluation using Cross-Validation\nfrom sklearn.model_selection import cross_val_score\nscores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n                         scoring=\"neg_mean_squared_error\", cv=10)\n\ntree_rmse_scores = np.sqrt(-scores)\ndisplay_scores(tree_rmse_scores)\n\n\nprint(\"RandomForest Regressor\")\n# RandomForestRegressor\n\nfrom sklearn.ensemble import RandomForestRegressor\n\nforest_reg = RandomForestRegressor()\nforest_reg.fit(housing_prepared, housing_labels)\nhousing_predictions = forest_reg.predict(housing_prepared)\nforest_mse = mean_squared_error(housing_labels, housing_predictions)\nforest_rmse = np.sqrt(forest_mse)\n\nforest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n                         scoring=\"neg_mean_squared_error\", cv=10)\n\nprint (\"RandomForestRegressor: After cross validation\")\nforest_rmse_scores = np.sqrt(-forest_scores)\ndisplay_scores(forest_rmse_scores)\n\n\n'''\nEvaluate system on the Test set\n'''\n\nprint (\"Evaluate the best models\")\n\nfinal_model = forest_reg\n\nX_test = strat_test_set.drop(\"median_house_value\", axis=1)\ny_test = strat_test_set[\"median_house_value\"].copy()\n\nX_test_prepared = full_pipeline.transform(X_test)\nfinal_predictions = final_model.predict(X_test_prepared)\n\nfinal_mse = mean_squared_error(y_test, final_predictions)\nfinal_rmse = np.sqrt(final_mse)\n\nfinal_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n                             scoring=\"neg_mean_squared_error\", cv=10)\nfinal_rmse_scores = np.sqrt(-final_scores)\ndisplay_scores(final_rmse_scores)\n\n","repo_name":"akashp1712/ml-akash","sub_path":"Projects/Housing/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5645,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"31843164915","text":"import textwrap\n\nfrom conans.test.assets.genconanfile import GenConanfile\nfrom conans.test.utils.tools import TestClient\n\n\ndef test_timestamp_error():\n    \"\"\" this test is a reproduction for\n    # https://github.com/conan-io/conan/issues/11606\n\n    It was crashing because of multiple test_requires, some of them being BINARY_SKIP,\n    and the prev_timestamp was not being assigned by GraphBinariesAnalizer when caching\n    \"\"\"\n\n    c = TestClient(default_server_user=True)\n    engine = textwrap.dedent(\"\"\"\n        from conan import ConanFile\n\n        class Engine(ConanFile):\n            name = \"engine\"\n            version = \"0.1\"\n            def build_requirements(self):\n                self.test_requires(\"gtest/0.1\")\n        \"\"\")\n    app = textwrap.dedent(\"\"\"\n        from conan import ConanFile\n        class App(ConanFile):\n            def requirements(self):\n                self.requires(\"engine/0.1\")\n            def build_requirements(self):\n                self.test_requires(\"gtest/0.1\")\n        \"\"\")\n    c.save({\"gtest/conanfile.py\": GenConanfile(\"gtest\", \"0.1\"),\n            \"engine/conanfile.py\": engine,\n            \"app/conanfile.py\": app})\n    c.run(\"create gtest\")\n    c.run(\"create engine\")\n    c.run(\"upload * -r=default -c\")\n    c.run(\"remove * -c\")\n    c.run(\"install app\")\n    # This used to fail, now it is not crashing anymore\n    assert \"Finalizing install\" in c.out\n","repo_name":"conan-io/conan","sub_path":"conans/test/integration/test_timestamp_error.py","file_name":"test_timestamp_error.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":7343,"dataset":"github-code","pt":"77"}